A GENETIC ALGORITHM FOR MOTION DETECTION

Size: px
Start display at page:

Download "A GENETIC ALGORITHM FOR MOTION DETECTION"

Transcription

1 A GENETIC ALGORITHM FOR MOTION DETECTION Jarosław Mamica, Tomasz Walkowiak Institute of Engineering Cybernetics, Wrocław University of Technology ul. Janiszewskiego 11/17, Wrocław, POLAND, Phone: , Fax: , Abstract: Motion is a feature, which may be used for identification, description and differentiation of objects for purpose of automatic understanding of particular cages of an image. A critical part of the digital image s sequence analysis is a procedure of object s comparison. We propose to use a genetic algorithm for that task. The genetic algorithm is used for a purpose of optimal determination of transforming for every of compared objects. Keywords: genetic algorithm, traffic monitoring, machine vision, motion detection 1 Introduction In case of machine vision, one of the most essential features letting to interpret image s semantics is the object s motion. [6] In the aspect of time-spatial images (video), motion is interpreted as association of tested image on successive cages of an entrance image (displacement of double dimension of object image, separated in segmentation process, in time function of video image). Application area of video detectors is mainly a statistic data accumulation, where error margin is admitted and continuous work of a detector is not necessary. The possibility of integration with monitoring systems and traffic recording (with option of motion picture after record analysis), portable and software realization of module responsible for detection are the main advantages of video detectors. Detection is a presence of vehicle in given area. Localization is directly connected with detection definition. This is a possibility of co-ordinate determination, in established arrangement of reference of a detected object. System overview System presented here was done according to following principles: the aim of the system is traffic monitoring. images are received from a video camera mounted in a stable way over the analyzed fragment of route or cross-roads; system is localizing vehicles on the bases of motion picture analysis and generating number of cars moving in camera view statistics. system is designed to work in real time on standard PC class computer. Movement is one of the features which can be used to identify, describe and distinguish objects for automatic understanding of separated video frames [6][7]. Movement is the essential feature in these algorithms. In practice only analysis of video sequences allows detection and analysis of dynamic vision. Sequence of digital still images is a time-spatial image. We understand it as a sequence of D-dimension discrete images. We have based the vehicle move detection algorithm on a so called differential method [7][8]. Since move is associated with changes in observation, move analysis methods should analyze the difference between two following images in a sequence. Move detection in this situation can be done by observation of brightness level value in time function, which passes during registration of following images. It suits following math description [8]: x, y, t ) = f ( x, y, t ) f ( x, y, ) (1) f D ( t1 where: t 1, t time moments, f D difference of images, x,y image coordinates. Difference of two images obtained this way can be used as an approximation of derivation of brightness level after time df(x,y, t)/dt in the middle point time space t - t 1. Of course, in real situation not all value brightness levels changes results from move existence. One of possible implementation of move detection could consist of marking out binary area that represents vehicles moving in one sequence. These are so called displace masks. The method of marking the displace masks out we can divide to two categories: first of them insists in marking out the changes between following frames in a sequence, and the second insists in marking out the differences between current

2 analyzed frame and background image. Usually the background is static, not changeable. It is a part of observed scene, what requires earlier viewing of the scene. In our system we have used different method. In this method differential images are marked out related to dynamic background image. We are updating the background during time. The background is calculated based on an average values with history and area character consideration (belonging to moving object or background). This operation is carried out by the iteration during analysis of a sequence of frames. Then the frame displace masks is taken as an absolute difference of current frame and background image processed in one moment. In analysis process of image with displace masks we have used genetic algorithms. Genetic algorithms are used as a tool which lets to put in order a chaotic (random) objects collection. As a result of we got labeling on objects on each frame. Therefore, we can calculate the motion trajectory. Objects that possess that feature are acknowledged as objects in motion, and they generates detection signal. System logics is taking decision about classify object as disturbance or they leave to continue next analysis. 3 Detection Algorithm for Vision Based Monitoring The detection algorithm [3] could be divided into six steps described below. It is worth to mention that the algorithm is performed for each of following frames of recorded video. The main data processed by the algorithm is a list of objects. The list records all objects presented in the video. At the first phase objects are all coherent groups of points separated from differential image, [7] which matches some basic criterions (i.e. minimum area). During next interpretation of objects are marked as vehicles or deleted from the list. Step 1. Removing previous list of objects List contains objects from number of levels. Level is a group of objects registered on single frame. In this first step of algorithm the oldest level of objects is removed. Step. Differential image calculation New image frame is taken from signal source, and the differential image is calculated as difference between current frame and current background. For the first read frame the calculation is not performed, since at this moment the background bitmap is not available. Since we are analyzing color pictures we got three brightness difference values for each base color (RGB). This three dimensional vectors is converted into 1-D value, the vector length. Step. 3 Differential image pre-processing [5] At this stage the differential image is pre-processed. The main aim of it is to make image properties better for generating objects data. At first the differential image is filtered. We have tested different kind of used filters, i.e. linear, nonlinear, space and frequency filters. Selection of the best filter depends on a trade-off between the effectiveness of the filter (i.e. resulting in correct object detection) and computational complexity (resulting in time and memory usage). Filtered differential image is then thresholded. This means that the differential image with pixels ranges form 0 to 44 (55* 3 ) is turned into image with pixels value 0 or 1 (intuitive interpretation is that 1 represents an object and 0 the background). All character pixels with values below the threshold are set to 0, all those above are set to 1. This is a simple way to remove the effects of any non-linearity in the contrast in the frame. However, the threshold value must be defined. It could be achieved by calculating the threshold based on a histogram of a given differential image. And again filtration is performed, this time using logic or median filters. Step 4. Segmentation and dissimilarity coefficient extraction The key problem of this phase of the detection algorithm is a method which separates the objects contained in the image and labels them. Segmentation [5] is a technique which allows fragmentizing image areas that match a criterion of homogenous. In case of binary image the criterion is the coherence of pixels area, qualified as active, describing an object, that means with value 1. While the separation of objects is performed, at the same time, indexation process is realized, that means each pixel of the object receives unique index value. The index value is unique for a given frame. For every object separated in a segmentation process and matching minimum number of active point s criteria, operation of addition to the list is taken. As objects parameters remembered are: coordinates of object position in the image, number of the object active points, the color bitmap of object and index value. Moreover, for each object added to the list, dissimilarity coefficients are calculated. Coefficients represent the dissimilarity of a given object to each object on the previous frame. The value close to zero means that objects are almost identical. This is a very important part of the algorithm, allowing afterward the labeling process and is described in details in next chapter.

3 current list of objects previous list of objects Fig.1 An example of object matching Step. 5. Object matching The aim of this phase to find association between objects form following levels (objects from two following frames of motion picture) and label the object as a new one, not identified or as matched to some object form the previous level. This is done by comparison of dissimilarity coefficients of listed objects. At first, coefficients with values below given threshold are rejected. Next, for each object on current frame list the most likelihood object from the previous list is selected, i.e. a given dissimilarity coefficients has the highest value. The selected object from previous list is removed from coefficients list (this is done to prevent association of two current objects to one from previous list). The left object coefficients are subject to choose winning pair again. The procedure is repeated to moment when all coefficients are chosen or rejected. Labeling results are extended with the value of displacement of an objects following two frames. The displacement values are being written into a database for further statistic analysis (relative position of object, class of object largeness, etc). Step 6. Background update Last stage is the background bitmap update. [7] The bitmap is then used in step 1. For the first frame the recorded bitmap is directly copied to the background bitmap. In case of all other frames, the background is updated recurrently. The idea of background generating is based on integration of information inserted by every new frame with background using the Kalman filter [] (this is done for all three colors separately): B (t+1) = B t + [a 1 *(1- M t ) + a *M t ]*D t ; () where: B t - background model at time t; D t - differences between current frame of motion picture and background model; M t - hypothetic binary masks of objects; a 1 - coefficient of background updating in areas not occupied by objects; - coefficient of background updating in areas occupied by objects. a 4 Dissimilarity coefficient extraction by genetic algorithm Move detection which is vehicle detection on crossroads is based on association of matching objects in following image frames. Critical procedure of stage and whole analysis appears procedure of objects comparison. Principle which says that matching objects in following frames are objects moved relatively each other by shortest distance is not sufficient. Moreover, with image perspective, relative distances measured in pixels are far incorrect and they do not contain sufficient quantity of information to use them in the object association process. That is why direct comparison of objects to determine their similarity is so necessary. The dissimilarity coefficients are calculated as normalized Euclidean distance between objects treated as a multidimensional vector: i ( r ( i) r ( i) ) + ( g ( i) g ( i) ) + ( b ( i) b ( i ) ) ) dissimilarity =, (3) N where i span all object points (from 1 to N- number of points in analyzed object), r 1, g 1,b 1 represents all three colors of each bit of the first image and r, g,b of the second image. However, the objects usually have different size. Therefore, the smaller object is processed with scaling and displacement transform and compared with bigger object. Coordinates of every point in transformed object image are set basing to linear transform:

4 x` = (x * kx ) + dx` (4) y` = (y * ky ) + dy`, where x,y old coordinates, x,y new coordinates and dx`, dy` represents displacement and kx` and ky` the scaling factor. Therefore, we have to find these four values of transform coefficients for all pair of pictures. We have found out that the genetic algorithm allows an effective and trustful solution of this problem. The basic target of the algorithm is to determine optimal transform for each pair of compared objects. In consequence it makes determination of dissimilarity coefficients in objects list possible. There is no need to determine perspective in this algorithm. Objects are being suit each other and optimize function of target i.e. minimize the dissimilarity (3). We have followed the standard genetic algorithm, described in many textbooks (i.e. [1]). The result of the genetic algorithm working, for each of analysed objects is a few sequences of transform coefficients. Number of sequences depends on number of objects compared with. In perfect situation, sequence of winning objects pair should has much more lower values of dissimilarity coefficients (highest similarity), and should be getting low approximately (parameter values of algorithm getting to optimum values). Our genetic algorithm works on 0 elements chromosome. Alleles are implemented through binary values 0 or 1. In chromosome 4 values are encoded: encoded movement dx i dy ranging form 0 to15; encoded scale kx an ky coefficients ranging from 0 to 63. Encoded transform coefficients are related to no-encoded coefficients according to linear projection: kx = kx * c1+ c dx = dx + c3 (5) ky = ky * c1+ c dy = dy + c3, where c1, c, c3 are constants of calibration. dx dy kx ky Fig.. The chromosome The genetic algorithms parameters set in performed experiments are as follows are: probability of crossing operation: 0.6; probability of mutation operation: 0.03; one point simple crossing; roulette method selection. For every specimen of population target function is set. Correct definition of target function is one of the most important part of a genetic algorithm. In our case the target function is the inverse value of dissimilarity (3) plus some constant. Therefore, we are performing the minimization (3) of dissimilarity value. Opposite to typical usage of Genetic Algorithms, in our case, it is not necessary to determine exact value of target function (fitness). It is important that value of target function generated for winning pair should be significantly different to not correlated pairs. That is why number of generated populations can be significantly lower then in typical use of GA (dozens). Number of generated populations forms a stop criterion. 5 System implementation The system presented has been implemented in the VC++ language. For system testing purposes the sequence of the input picture is loaded from the disk as a sequence of JPEG algorithm compressed frames. After decompression the picture is stored in the RGB format. One of the aims of the work was to analyse the system abilities to work in real time system. The analysis time of a single picture frame has been finally reduced to approximately 8 seconds per frame on PC 366 MHz, (out of which, approx. 40% of time to read the data and perform the introductory processing of the picture and 60% to associate the objects using AG).

5 Fig. 3. The object matching One has to point out the fact that the achievement of video picture analysis in the matter of seconds per frame is compromised with the quality of the generated outcomes. It refers first of all to the number of generations generated by the genetic algorithm (directly implicating the matching quality of the corresponding objects, and therefore the resulting final outcomes) and the number of operations performed on the picture. Nevertheless, analyzing the results, one can ascertain that the elaborated picture analysis methodology makes it possible to implement the created system as a real time system. An additional code optimization would be necessary though, especially in the key points of the program. Decisions referring to the implemented algorithms were the outcome of the necessary compromise between the precision of the outcomes and the performance. The system has the characteristics of a real time system, i.e. the total analysis time of a single picture frame shouldn t exceed (with given analysis parameters) the time between two succeeding video picture frames. The precision of the results depends on the length of the analysis time. Optimizing the key, considering the time of performance, functions of the program, we are able to increase the real time analysis of the picture, and the same the precision of the results of system performance. To make it more simple the list of objects of designed system is based on two levels. The best results in the filtration module have been achieved by implementing two filters: averaging filter for one-dimensional differential picture and a quick logical filter for the binary picture. The segmentation process is based on a modified, iterative algorithm of direct segmentation. This algorithm guarantees a relatively fast and stable work (contrary to the recurrent algorithm, which problem is slow work and the possibility of overloading the stack). A substantial problem of the analysis occurred to be a proper method of the background picture generation. An element of the picture semantic interpretation has been introduced in order to achieve better results the points of the pictures have been distinguished into points belonging to one of the moving objects and background points. The distinction is based on assigning a given area a picture of responding background actualization coefficient. The background is generated dynamically using the Kalman filter. At the stage of pictures sequences association, for each of the objects, AG appeared to be the most effective analysis method. Direct comparing, as well as comparing using the approximation of perspectives in the picture didn t bring positive results. Using of neural networks has also been taken into consideration. Finally the choice was one of the directed methods of solution space searching AG. The implemented algorithm is based on simple selection operations, crossing and mutation. The difficulty occurred in the elaboration of an adequate method of transformation coefficients coding and a proper adjustment function definition. The last step of the detection algorithm is the dissimilarity coefficient analysis. At this point, the logic of the system ensures the preservation of the set rules concerning the correctness of the association process. For example if the picture of the given object has been associated with the picture of another object, all the remaining factors for the

6 objects of the pair are being eliminated from further analysis. The coordinates localizing an object are being set for each of the objects in a given coordinate system in this case in screen coordinates. In order to present the objects clearly on the screen, each of them has a unique identifier placed in the point defined by the co-ordinates of the localization. detected objects input picture current background 6 Summary Fig. 4. Process of vehicle detection During the research on vehicle localization in motion picture it turned out that object motion is the most essential feature that lets interpret semantic of out coming image in segmentation process. In those terms restricted on system and principle that image is being taken from video camera mounted over the crossroads, sufficient term of vehicle detection is a move. In consequence, system does not require additional modules and additional calculation expenditures in process of image analysis. It allows the system to work in real time. Necessity of using the most effective method of searching the space of solutions resulted in an experimental use of genetic algorithms. Obtained results of experiments allow us to mark this method very high. It should be stated here that the main aim of the research was to work out the easiest possible way of image analysis. There so some drawbacks of presented method. The system is highly sensitivity to the image quality. Trial of real use of elaborated methodology would need some modification of every of analysis stages in a way that the disturbances and deformations do not propagate to next stages. Summarizing results of experiment let us to confirm that genetic algorithms appeared to be most effective analysis method, which is a compromise between quality of results and elapsed time. References [1] GOLDBERG D., Genetic Algorithm and their usage. (in Polish), WNT Warszawa 1995 [] JUN ZHAO, EE/CSE 586: Advanced Topics in Computer Vision, Computer Project Report, 1999 Vision-Based Traffic Monitoring, [3] MAMICA J. Mobile detection based on video signal, Institute of Cybernetic Engineering, Wroclaw University of Technology, MSc dissertation, 00. (in Polish) [4] MATERKA A., Digital processing and analysis of images elements. (in Polish), PWN Warszawa-Lodz 1991 [5] TADEUSIEWICZ R., Visual systems of industrial robots. (in Polish), WNT Warszawa 199 [6] The CSIRO Image Analysis Group, Image motion and tracking, [7] The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer: Motion, Tracking and Time Sequence Analysis, Scene Understanding, Image Transformations and Filters, [8] WOŹNICKI J., KUKIELA G, Analysis of digital images sequence motion detection (in Polish) Own Researches Priority Program of Technical University of Warsaw.

Prof. Fanny Ficuciello Robotics for Bioengineering Visual Servoing

Prof. Fanny Ficuciello Robotics for Bioengineering Visual Servoing Visual servoing vision allows a robotic system to obtain geometrical and qualitative information on the surrounding environment high level control motion planning (look-and-move visual grasping) low level

More information

CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES

CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES 6.1 INTRODUCTION The exploration of applications of ANN for image classification has yielded satisfactory results. But, the scope for improving

More information

Analysis of Image and Video Using Color, Texture and Shape Features for Object Identification

Analysis of Image and Video Using Color, Texture and Shape Features for Object Identification IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 16, Issue 6, Ver. VI (Nov Dec. 2014), PP 29-33 Analysis of Image and Video Using Color, Texture and Shape Features

More information

The Genetic Algorithm for finding the maxima of single-variable functions

The Genetic Algorithm for finding the maxima of single-variable functions Research Inventy: International Journal Of Engineering And Science Vol.4, Issue 3(March 2014), PP 46-54 Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.com The Genetic Algorithm for finding

More information

MATCHING CRITERIA IN TEMPLATE LOCALIZING COMPARATIVE ANALYSIS OF EXPERIMENTAL RESULTS

MATCHING CRITERIA IN TEMPLATE LOCALIZING COMPARATIVE ANALYSIS OF EXPERIMENTAL RESULTS MATCHING CRITERIA IN TEMPLATE LOCALIZING COMPARATIVE ANALYSIS OF EXPERIMENTAL RESULTS Yulka PETKOVA*, Dimitar TYANEV* *Technical University of Varna, Department of Computer Sciences and Technologies, 1,

More information

COMPUTER VISION. Dr. Sukhendu Das Deptt. of Computer Science and Engg., IIT Madras, Chennai

COMPUTER VISION. Dr. Sukhendu Das Deptt. of Computer Science and Engg., IIT Madras, Chennai COMPUTER VISION Dr. Sukhendu Das Deptt. of Computer Science and Engg., IIT Madras, Chennai 600036. Email: sdas@iitm.ac.in URL: //www.cs.iitm.ernet.in/~sdas 1 INTRODUCTION 2 Human Vision System (HVS) Vs.

More information

ФУНДАМЕНТАЛЬНЫЕ НАУКИ. Информатика 9 ИНФОРМАТИКА MOTION DETECTION IN VIDEO STREAM BASED ON BACKGROUND SUBTRACTION AND TARGET TRACKING

ФУНДАМЕНТАЛЬНЫЕ НАУКИ. Информатика 9 ИНФОРМАТИКА MOTION DETECTION IN VIDEO STREAM BASED ON BACKGROUND SUBTRACTION AND TARGET TRACKING ФУНДАМЕНТАЛЬНЫЕ НАУКИ Информатика 9 ИНФОРМАТИКА UDC 6813 OTION DETECTION IN VIDEO STREA BASED ON BACKGROUND SUBTRACTION AND TARGET TRACKING R BOGUSH, S ALTSEV, N BROVKO, E IHAILOV (Polotsk State University

More information

Dynamic Obstacle Detection Based on Background Compensation in Robot s Movement Space

Dynamic Obstacle Detection Based on Background Compensation in Robot s Movement Space MATEC Web of Conferences 95 83 (7) DOI:.5/ matecconf/79583 ICMME 6 Dynamic Obstacle Detection Based on Background Compensation in Robot s Movement Space Tao Ni Qidong Li Le Sun and Lingtao Huang School

More information

Multimedia Systems Video II (Video Coding) Mahdi Amiri April 2012 Sharif University of Technology

Multimedia Systems Video II (Video Coding) Mahdi Amiri April 2012 Sharif University of Technology Course Presentation Multimedia Systems Video II (Video Coding) Mahdi Amiri April 2012 Sharif University of Technology Video Coding Correlation in Video Sequence Spatial correlation Similar pixels seem

More information

IMAGE COMPRESSION TECHNIQUES

IMAGE COMPRESSION TECHNIQUES IMAGE COMPRESSION TECHNIQUES A.VASANTHAKUMARI, M.Sc., M.Phil., ASSISTANT PROFESSOR OF COMPUTER SCIENCE, JOSEPH ARTS AND SCIENCE COLLEGE, TIRUNAVALUR, VILLUPURAM (DT), TAMIL NADU, INDIA ABSTRACT A picture

More information

Multi-pass approach to adaptive thresholding based image segmentation

Multi-pass approach to adaptive thresholding based image segmentation 1 Multi-pass approach to adaptive thresholding based image segmentation Abstract - Thresholding is still one of the most common approaches to monochrome image segmentation. It often provides sufficient

More information

Genetic Algorithm Based Template Optimization for a Vision System: Obstacle Detection

Genetic Algorithm Based Template Optimization for a Vision System: Obstacle Detection ISTET'09 Umair Ali Khan, Alireza Fasih, Kyandoghere Kyamakya, Jean Chamberlain Chedjou Transportation Informatics Group, Alpen Adria University, Klagenfurt, Austria. Genetic Algorithm Based Template Optimization

More information

Colour Segmentation-based Computation of Dense Optical Flow with Application to Video Object Segmentation

Colour Segmentation-based Computation of Dense Optical Flow with Application to Video Object Segmentation ÖGAI Journal 24/1 11 Colour Segmentation-based Computation of Dense Optical Flow with Application to Video Object Segmentation Michael Bleyer, Margrit Gelautz, Christoph Rhemann Vienna University of Technology

More information

Copyright Detection System for Videos Using TIRI-DCT Algorithm

Copyright Detection System for Videos Using TIRI-DCT Algorithm Research Journal of Applied Sciences, Engineering and Technology 4(24): 5391-5396, 2012 ISSN: 2040-7467 Maxwell Scientific Organization, 2012 Submitted: March 18, 2012 Accepted: June 15, 2012 Published:

More information

1. Introduction. 2. Motivation and Problem Definition. Volume 8 Issue 2, February Susmita Mohapatra

1. Introduction. 2. Motivation and Problem Definition. Volume 8 Issue 2, February Susmita Mohapatra Pattern Recall Analysis of the Hopfield Neural Network with a Genetic Algorithm Susmita Mohapatra Department of Computer Science, Utkal University, India Abstract: This paper is focused on the implementation

More information

Prediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai He 1,c

Prediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai He 1,c 2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 215) Prediction of traffic flow based on the EMD and wavelet neural network Teng Feng 1,a,Xiaohong Wang 1,b,Yunlai

More information

Research on time optimal trajectory planning of 7-DOF manipulator based on genetic algorithm

Research on time optimal trajectory planning of 7-DOF manipulator based on genetic algorithm Acta Technica 61, No. 4A/2016, 189 200 c 2017 Institute of Thermomechanics CAS, v.v.i. Research on time optimal trajectory planning of 7-DOF manipulator based on genetic algorithm Jianrong Bu 1, Junyan

More information

3. Genetic local search for Earth observation satellites operations scheduling

3. Genetic local search for Earth observation satellites operations scheduling Distance preserving recombination operator for Earth observation satellites operations scheduling Andrzej Jaszkiewicz Institute of Computing Science, Poznan University of Technology ul. Piotrowo 3a, 60-965

More information

Final Review CMSC 733 Fall 2014

Final Review CMSC 733 Fall 2014 Final Review CMSC 733 Fall 2014 We have covered a lot of material in this course. One way to organize this material is around a set of key equations and algorithms. You should be familiar with all of these,

More information

EXAM SOLUTIONS. Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006,

EXAM SOLUTIONS. Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006, School of Computer Science and Communication, KTH Danica Kragic EXAM SOLUTIONS Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006, 14.00 19.00 Grade table 0-25 U 26-35 3 36-45

More information

Introduction (7.1) Genetic Algorithms (GA) (7.2) Simulated Annealing (SA) (7.3) Random Search (7.4) Downhill Simplex Search (DSS) (7.

Introduction (7.1) Genetic Algorithms (GA) (7.2) Simulated Annealing (SA) (7.3) Random Search (7.4) Downhill Simplex Search (DSS) (7. Chapter 7: Derivative-Free Optimization Introduction (7.1) Genetic Algorithms (GA) (7.2) Simulated Annealing (SA) (7.3) Random Search (7.4) Downhill Simplex Search (DSS) (7.5) Jyh-Shing Roger Jang et al.,

More information

Use of Mean Square Error Measure in Biometric Analysis of Fingerprint Tests

Use of Mean Square Error Measure in Biometric Analysis of Fingerprint Tests Journal of Information Hiding and Multimedia Signal Processing c 2015 ISSN 2073-4212 Ubiquitous International Volume 6, Number 5, September 2015 Use of Mean Square Error Measure in Biometric Analysis of

More information

Chapter 3 Image Registration. Chapter 3 Image Registration

Chapter 3 Image Registration. Chapter 3 Image Registration Chapter 3 Image Registration Distributed Algorithms for Introduction (1) Definition: Image Registration Input: 2 images of the same scene but taken from different perspectives Goal: Identify transformation

More information

Idle Object Detection in Video for Banking ATM Applications

Idle Object Detection in Video for Banking ATM Applications Research Journal of Applied Sciences, Engineering and Technology 4(24): 5350-5356, 2012 ISSN: 2040-7467 Maxwell Scientific Organization, 2012 Submitted: March 18, 2012 Accepted: April 06, 2012 Published:

More information

A Genetic Approach for Solving Minimum Routing Cost Spanning Tree Problem

A Genetic Approach for Solving Minimum Routing Cost Spanning Tree Problem A Genetic Approach for Solving Minimum Routing Cost Spanning Tree Problem Quoc Phan Tan Abstract Minimum Routing Cost Spanning Tree (MRCT) is one of spanning tree optimization problems having several applications

More information

EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm

EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm EE368 Project Report CD Cover Recognition Using Modified SIFT Algorithm Group 1: Mina A. Makar Stanford University mamakar@stanford.edu Abstract In this report, we investigate the application of the Scale-Invariant

More information

SUITABLE CONFIGURATION OF EVOLUTIONARY ALGORITHM AS BASIS FOR EFFICIENT PROCESS PLANNING TOOL

SUITABLE CONFIGURATION OF EVOLUTIONARY ALGORITHM AS BASIS FOR EFFICIENT PROCESS PLANNING TOOL DAAAM INTERNATIONAL SCIENTIFIC BOOK 2015 pp. 135-142 Chapter 12 SUITABLE CONFIGURATION OF EVOLUTIONARY ALGORITHM AS BASIS FOR EFFICIENT PROCESS PLANNING TOOL JANKOWSKI, T. Abstract: The paper presents

More information

Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation

Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation. Range Imaging Through Triangulation Obviously, this is a very slow process and not suitable for dynamic scenes. To speed things up, we can use a laser that projects a vertical line of light onto the scene. This laser rotates around its vertical

More information

CHAPTER 6 MODIFIED FUZZY TECHNIQUES BASED IMAGE SEGMENTATION

CHAPTER 6 MODIFIED FUZZY TECHNIQUES BASED IMAGE SEGMENTATION CHAPTER 6 MODIFIED FUZZY TECHNIQUES BASED IMAGE SEGMENTATION 6.1 INTRODUCTION Fuzzy logic based computational techniques are becoming increasingly important in the medical image analysis arena. The significant

More information

VALIDATION METHODOLOGY FOR SIMULATION SOFTWARE OF SHIP BEHAVIOUR IN EXTREME SEAS

VALIDATION METHODOLOGY FOR SIMULATION SOFTWARE OF SHIP BEHAVIOUR IN EXTREME SEAS 10 th International Conference 409 VALIDATION METHODOLOGY FOR SIMULATION SOFTWARE OF SHIP BEHAVIOUR IN EXTREME SEAS Stefan Grochowalski, Polish Register of Shipping, S.Grochowalski@prs.pl Jan Jankowski,

More information

Part 3: Image Processing

Part 3: Image Processing Part 3: Image Processing Image Filtering and Segmentation Georgy Gimel farb COMPSCI 373 Computer Graphics and Image Processing 1 / 60 1 Image filtering 2 Median filtering 3 Mean filtering 4 Image segmentation

More information

An Interactive Technique for Robot Control by Using Image Processing Method

An Interactive Technique for Robot Control by Using Image Processing Method An Interactive Technique for Robot Control by Using Image Processing Method Mr. Raskar D. S 1., Prof. Mrs. Belagali P. P 2 1, E&TC Dept. Dr. JJMCOE., Jaysingpur. Maharashtra., India. 2 Associate Prof.

More information

Mapping of Hierarchical Activation in the Visual Cortex Suman Chakravartula, Denise Jones, Guillaume Leseur CS229 Final Project Report. Autumn 2008.

Mapping of Hierarchical Activation in the Visual Cortex Suman Chakravartula, Denise Jones, Guillaume Leseur CS229 Final Project Report. Autumn 2008. Mapping of Hierarchical Activation in the Visual Cortex Suman Chakravartula, Denise Jones, Guillaume Leseur CS229 Final Project Report. Autumn 2008. Introduction There is much that is unknown regarding

More information

In addition, the image registration and geocoding functionality is also available as a separate GEO package.

In addition, the image registration and geocoding functionality is also available as a separate GEO package. GAMMA Software information: GAMMA Software supports the entire processing from SAR raw data to products such as digital elevation models, displacement maps and landuse maps. The software is grouped into

More information

Motion Tracking and Event Understanding in Video Sequences

Motion Tracking and Event Understanding in Video Sequences Motion Tracking and Event Understanding in Video Sequences Isaac Cohen Elaine Kang, Jinman Kang Institute for Robotics and Intelligent Systems University of Southern California Los Angeles, CA Objectives!

More information

6. Advanced Topics in Computability

6. Advanced Topics in Computability 227 6. Advanced Topics in Computability The Church-Turing thesis gives a universally acceptable definition of algorithm Another fundamental concept in computer science is information No equally comprehensive

More information

Genetic algorithm for optimal imperceptibility in image communication through noisy channel

Genetic algorithm for optimal imperceptibility in image communication through noisy channel Genetic algorithm for optimal imperceptibility in image communication through noisy channel SantiP.Maity 1, Malay K. Kundu 2 andprasantak.nandi 3 1 Bengal Engineering College (DU), P.O.-Botanic Garden,

More information

We show that the composite function h, h(x) = g(f(x)) is a reduction h: A m C.

We show that the composite function h, h(x) = g(f(x)) is a reduction h: A m C. 219 Lemma J For all languages A, B, C the following hold i. A m A, (reflexive) ii. if A m B and B m C, then A m C, (transitive) iii. if A m B and B is Turing-recognizable, then so is A, and iv. if A m

More information

EVALUATION OF SEQUENTIAL IMAGES FOR PHOTOGRAMMETRICALLY POINT DETERMINATION

EVALUATION OF SEQUENTIAL IMAGES FOR PHOTOGRAMMETRICALLY POINT DETERMINATION Archives of Photogrammetry, Cartography and Remote Sensing, Vol. 22, 2011, pp. 285-296 ISSN 2083-2214 EVALUATION OF SEQUENTIAL IMAGES FOR PHOTOGRAMMETRICALLY POINT DETERMINATION Michał Kowalczyk 1 1 Department

More information

CORRELATION BASED CAR NUMBER PLATE EXTRACTION SYSTEM

CORRELATION BASED CAR NUMBER PLATE EXTRACTION SYSTEM CORRELATION BASED CAR NUMBER PLATE EXTRACTION SYSTEM 1 PHYO THET KHIN, 2 LAI LAI WIN KYI 1,2 Department of Information Technology, Mandalay Technological University The Republic of the Union of Myanmar

More information

An Efficient Single Chord-based Accumulation Technique (SCA) to Detect More Reliable Corners

An Efficient Single Chord-based Accumulation Technique (SCA) to Detect More Reliable Corners An Efficient Single Chord-based Accumulation Technique (SCA) to Detect More Reliable Corners Mohammad Asiful Hossain, Abdul Kawsar Tushar, and Shofiullah Babor Computer Science and Engineering Department,

More information

Image processing and features

Image processing and features Image processing and features Gabriele Bleser gabriele.bleser@dfki.de Thanks to Harald Wuest, Folker Wientapper and Marc Pollefeys Introduction Previous lectures: geometry Pose estimation Epipolar geometry

More information

Optical Microscopy and Metallography: Recognition of Metal Structures using Customizable Automatic Qualifiers

Optical Microscopy and Metallography: Recognition of Metal Structures using Customizable Automatic Qualifiers ECNDT 2006 - Poster 68 Optical Microscopy and Metallography: Recognition of Metal Structures using Customizable Automatic Qualifiers Mikhail V. FILINOV, Andrey S. FURSOV, Association SPEKTR_GROUP, Moscow,

More information

MRR (Multi Resolution Raster) Revolutionizing Raster

MRR (Multi Resolution Raster) Revolutionizing Raster MRR (Multi Resolution Raster) Revolutionizing Raster Praveen Gupta Praveen.Gupta@pb.com Pitney Bowes, Noida, India T +91 120 4026000 M +91 9810 659 350 Pitney Bowes, pitneybowes.com/in 5 th Floor, Tower

More information

Mouse Pointer Tracking with Eyes

Mouse Pointer Tracking with Eyes Mouse Pointer Tracking with Eyes H. Mhamdi, N. Hamrouni, A. Temimi, and M. Bouhlel Abstract In this article, we expose our research work in Human-machine Interaction. The research consists in manipulating

More information

SIMULATION TESTS ON SHAPING THE WORKING WIDTH OF THE CONCRETE PROTECTIVE SYSTEMS

SIMULATION TESTS ON SHAPING THE WORKING WIDTH OF THE CONCRETE PROTECTIVE SYSTEMS Journal of KONES Powertrain and Transport, Vol. 7, No. 00 SIMULATION TESTS ON SHAPING THE WORKING WIDTH OF THE CONCRETE PROTECTIVE SYSTEMS Wac aw Borkowski, Zdzis aw Hryciów, Piotr Rybak, Józef Wysocki

More information

Thresholding in Edge Detection

Thresholding in Edge Detection Thresholding in Edge Detection Yulka Petkova, Dimitar Tyanev Technical University - Varna, Varna, Bulgaria Abstract: Many edge detectors are described in the literature about image processing, where the

More information

Evolving SQL Queries for Data Mining

Evolving SQL Queries for Data Mining Evolving SQL Queries for Data Mining Majid Salim and Xin Yao School of Computer Science, The University of Birmingham Edgbaston, Birmingham B15 2TT, UK {msc30mms,x.yao}@cs.bham.ac.uk Abstract. This paper

More information

One category of visual tracking. Computer Science SURJ. Michael Fischer

One category of visual tracking. Computer Science SURJ. Michael Fischer Computer Science Visual tracking is used in a wide range of applications such as robotics, industrial auto-control systems, traffic monitoring, and manufacturing. This paper describes a new algorithm for

More information

DATA and signal modeling for images and video sequences. Region-Based Representations of Image and Video: Segmentation Tools for Multimedia Services

DATA and signal modeling for images and video sequences. Region-Based Representations of Image and Video: Segmentation Tools for Multimedia Services IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 9, NO. 8, DECEMBER 1999 1147 Region-Based Representations of Image and Video: Segmentation Tools for Multimedia Services P. Salembier,

More information

Image Processing, Analysis and Machine Vision

Image Processing, Analysis and Machine Vision Image Processing, Analysis and Machine Vision Milan Sonka PhD University of Iowa Iowa City, USA Vaclav Hlavac PhD Czech Technical University Prague, Czech Republic and Roger Boyle DPhil, MBCS, CEng University

More information

Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras

Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Advanced Operations Research Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 35 Quadratic Programming In this lecture, we continue our discussion on

More information

Visual Tracking. Image Processing Laboratory Dipartimento di Matematica e Informatica Università degli studi di Catania.

Visual Tracking. Image Processing Laboratory Dipartimento di Matematica e Informatica Università degli studi di Catania. Image Processing Laboratory Dipartimento di Matematica e Informatica Università degli studi di Catania 1 What is visual tracking? estimation of the target location over time 2 applications Six main areas:

More information

Reference Tracking System for a Mobile Skid Steer Robot (Version 1.00)

Reference Tracking System for a Mobile Skid Steer Robot (Version 1.00) CENTER FOR MACHINE PERCEPTION CZECH TECHNICAL UNIVERSITY IN PRAGUE Reference Tracking System for a Mobile Skid Steer Robot (Version 1.00) Vladimír Kubelka, Vladimír Burian, Přemysl Kafka kubelka.vladimir@fel.cvut.cz

More information

A GENETIC ALGORITHM FOR CLUSTERING ON VERY LARGE DATA SETS

A GENETIC ALGORITHM FOR CLUSTERING ON VERY LARGE DATA SETS A GENETIC ALGORITHM FOR CLUSTERING ON VERY LARGE DATA SETS Jim Gasvoda and Qin Ding Department of Computer Science, Pennsylvania State University at Harrisburg, Middletown, PA 17057, USA {jmg289, qding}@psu.edu

More information

Motion in 2D image sequences

Motion in 2D image sequences Motion in 2D image sequences Definitely used in human vision Object detection and tracking Navigation and obstacle avoidance Analysis of actions or activities Segmentation and understanding of video sequences

More information

ALOS-PALSAR performances on a multiple sensor DInSAR scenario for deformation monitoring

ALOS-PALSAR performances on a multiple sensor DInSAR scenario for deformation monitoring ALOS-PALSAR performances on a multiple sensor DInSAR scenario for deformation monitoring Pablo Blanco, Roman Arbiol and Vicenç Palà Remote Sensing Department Institut Cartogràfic de Catalunya (ICC) Parc

More information

GroundFX Tracker Manual

GroundFX Tracker Manual Manual Documentation Version: 1.4.m02 The latest version of this manual is available at http://www.gesturetek.com/support.php 2007 GestureTek Inc. 317 Adelaide Street West, Toronto, Ontario, M5V 1P9 Canada

More information

A Robust and Efficient Motion Segmentation Based on Orthogonal Projection Matrix of Shape Space

A Robust and Efficient Motion Segmentation Based on Orthogonal Projection Matrix of Shape Space A Robust and Efficient Motion Segmentation Based on Orthogonal Projection Matrix of Shape Space Naoyuki ICHIMURA Electrotechnical Laboratory 1-1-4, Umezono, Tsukuba Ibaraki, 35-8568 Japan ichimura@etl.go.jp

More information

Visible and Long-Wave Infrared Image Fusion Schemes for Situational. Awareness

Visible and Long-Wave Infrared Image Fusion Schemes for Situational. Awareness Visible and Long-Wave Infrared Image Fusion Schemes for Situational Awareness Multi-Dimensional Digital Signal Processing Literature Survey Nathaniel Walker The University of Texas at Austin nathaniel.walker@baesystems.com

More information

Topic 5 Image Compression

Topic 5 Image Compression Topic 5 Image Compression Introduction Data Compression: The process of reducing the amount of data required to represent a given quantity of information. Purpose of Image Compression: the reduction of

More information

Detection and Classification of Vehicles

Detection and Classification of Vehicles Detection and Classification of Vehicles Gupte et al. 2002 Zeeshan Mohammad ECG 782 Dr. Brendan Morris. Introduction Previously, magnetic loop detectors were used to count vehicles passing over them. Advantages

More information

Sımultaneous estımatıon of Aquifer Parameters and Parameter Zonations using Genetic Algorithm

Sımultaneous estımatıon of Aquifer Parameters and Parameter Zonations using Genetic Algorithm Sımultaneous estımatıon of Aquifer Parameters and Parameter Zonations using Genetic Algorithm M.Tamer AYVAZ Visiting Graduate Student Nov 20/2006 MULTIMEDIA ENVIRONMENTAL SIMULATIONS LABORATORY (MESL)

More information

Adaptive Skin Color Classifier for Face Outline Models

Adaptive Skin Color Classifier for Face Outline Models Adaptive Skin Color Classifier for Face Outline Models M. Wimmer, B. Radig, M. Beetz Informatik IX, Technische Universität München, Germany Boltzmannstr. 3, 87548 Garching, Germany [wimmerm, radig, beetz]@informatik.tu-muenchen.de

More information

Moving Object Segmentation Method Based on Motion Information Classification by X-means and Spatial Region Segmentation

Moving Object Segmentation Method Based on Motion Information Classification by X-means and Spatial Region Segmentation IJCSNS International Journal of Computer Science and Network Security, VOL.13 No.11, November 2013 1 Moving Object Segmentation Method Based on Motion Information Classification by X-means and Spatial

More information

CS4733 Class Notes, Computer Vision

CS4733 Class Notes, Computer Vision CS4733 Class Notes, Computer Vision Sources for online computer vision tutorials and demos - http://www.dai.ed.ac.uk/hipr and Computer Vision resources online - http://www.dai.ed.ac.uk/cvonline Vision

More information

Detecting motion by means of 2D and 3D information

Detecting motion by means of 2D and 3D information Detecting motion by means of 2D and 3D information Federico Tombari Stefano Mattoccia Luigi Di Stefano Fabio Tonelli Department of Electronics Computer Science and Systems (DEIS) Viale Risorgimento 2,

More information

Detecting and Identifying Moving Objects in Real-Time

Detecting and Identifying Moving Objects in Real-Time Chapter 9 Detecting and Identifying Moving Objects in Real-Time For surveillance applications or for human-computer interaction, the automated real-time tracking of moving objects in images from a stationary

More information

16720 Computer Vision: Homework 3 Template Tracking and Layered Motion.

16720 Computer Vision: Homework 3 Template Tracking and Layered Motion. 16720 Computer Vision: Homework 3 Template Tracking and Layered Motion. Instructor: Martial Hebert TAs: Varun Ramakrishna and Tomas Simon Due Date: October 24 th, 2011. 1 Instructions You should submit

More information

ME/CS 132: Introduction to Vision-based Robot Navigation! Low-level Image Processing" Larry Matthies"

ME/CS 132: Introduction to Vision-based Robot Navigation! Low-level Image Processing Larry Matthies ME/CS 132: Introduction to Vision-based Robot Navigation! Low-level Image Processing" Larry Matthies" lhm@jpl.nasa.gov, 818-354-3722" Announcements" First homework grading is done! Second homework is due

More information

CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM

CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 20 CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 2.1 CLASSIFICATION OF CONVENTIONAL TECHNIQUES Classical optimization methods can be classified into two distinct groups:

More information

Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition

Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition M. Morita,2, R. Sabourin 3, F. Bortolozzi 3 and C. Y. Suen 2 École de Technologie Supérieure, Montreal,

More information

Task analysis based on observing hands and objects by vision

Task analysis based on observing hands and objects by vision Task analysis based on observing hands and objects by vision Yoshihiro SATO Keni Bernardin Hiroshi KIMURA Katsushi IKEUCHI Univ. of Electro-Communications Univ. of Karlsruhe Univ. of Tokyo Abstract In

More information

HIERARCHIC APPROACH IN THE ANALYSIS OF TOMOGRAPHIC EYE IMAGE

HIERARCHIC APPROACH IN THE ANALYSIS OF TOMOGRAPHIC EYE IMAGE HIERARCHIC APPROACH IN THE ANALYSIS OF TOMOGRAPHIC EYE IMAGE Robert Koprowski, Zygmunt Wróbel University of Silesia, Faculty of Computer Science and Materials Science Institute of Computer Science, Department

More information

Reducing Graphic Conflict In Scale Reduced Maps Using A Genetic Algorithm

Reducing Graphic Conflict In Scale Reduced Maps Using A Genetic Algorithm Reducing Graphic Conflict In Scale Reduced Maps Using A Genetic Algorithm Dr. Ian D. Wilson School of Technology, University of Glamorgan, Pontypridd CF37 1DL, UK Dr. J. Mark Ware School of Computing,

More information

Experiments with Edge Detection using One-dimensional Surface Fitting

Experiments with Edge Detection using One-dimensional Surface Fitting Experiments with Edge Detection using One-dimensional Surface Fitting Gabor Terei, Jorge Luis Nunes e Silva Brito The Ohio State University, Department of Geodetic Science and Surveying 1958 Neil Avenue,

More information

CHAPTER 4 DETECTION OF DISEASES IN PLANT LEAF USING IMAGE SEGMENTATION

CHAPTER 4 DETECTION OF DISEASES IN PLANT LEAF USING IMAGE SEGMENTATION CHAPTER 4 DETECTION OF DISEASES IN PLANT LEAF USING IMAGE SEGMENTATION 4.1. Introduction Indian economy is highly dependent of agricultural productivity. Therefore, in field of agriculture, detection of

More information

VMDC Version 7.0 Performance Guide

VMDC Version 7.0 Performance Guide VMDC Version 7.0 Performance Guide General With the release of the VMDC version 7.0 Vicon has introduced an improved display performance algorithm. As before, using multiple monitors and maximizing the

More information

Motion Estimation for Video Coding Standards

Motion Estimation for Video Coding Standards Motion Estimation for Video Coding Standards Prof. Ja-Ling Wu Department of Computer Science and Information Engineering National Taiwan University Introduction of Motion Estimation The goal of video compression

More information

Available online at ScienceDirect. Energy Procedia 69 (2015 )

Available online at   ScienceDirect. Energy Procedia 69 (2015 ) Available online at www.sciencedirect.com ScienceDirect Energy Procedia 69 (2015 ) 1885 1894 International Conference on Concentrating Solar Power and Chemical Energy Systems, SolarPACES 2014 Heliostat

More information

Understanding Tracking and StroMotion of Soccer Ball

Understanding Tracking and StroMotion of Soccer Ball Understanding Tracking and StroMotion of Soccer Ball Nhat H. Nguyen Master Student 205 Witherspoon Hall Charlotte, NC 28223 704 656 2021 rich.uncc@gmail.com ABSTRACT Soccer requires rapid ball movements.

More information

Introduction to Medical Imaging (5XSA0) Module 5

Introduction to Medical Imaging (5XSA0) Module 5 Introduction to Medical Imaging (5XSA0) Module 5 Segmentation Jungong Han, Dirk Farin, Sveta Zinger ( s.zinger@tue.nl ) 1 Outline Introduction Color Segmentation region-growing region-merging watershed

More information

MOTION STEREO DOUBLE MATCHING RESTRICTION IN 3D MOVEMENT ANALYSIS

MOTION STEREO DOUBLE MATCHING RESTRICTION IN 3D MOVEMENT ANALYSIS MOTION STEREO DOUBLE MATCHING RESTRICTION IN 3D MOVEMENT ANALYSIS ZHANG Chun-sen Dept of Survey, Xi an University of Science and Technology, No.58 Yantazhonglu, Xi an 710054,China -zhchunsen@yahoo.com.cn

More information

Cs : Computer Vision Final Project Report

Cs : Computer Vision Final Project Report Cs 600.461: Computer Vision Final Project Report Giancarlo Troni gtroni@jhu.edu Raphael Sznitman sznitman@jhu.edu Abstract Given a Youtube video of a busy street intersection, our task is to detect, track,

More information

CONTRIBUTION TO THE INVESTIGATION OF STOPPING SIGHT DISTANCE IN THREE-DIMENSIONAL SPACE

CONTRIBUTION TO THE INVESTIGATION OF STOPPING SIGHT DISTANCE IN THREE-DIMENSIONAL SPACE National Technical University of Athens School of Civil Engineering Department of Transportation Planning and Engineering Doctoral Dissertation CONTRIBUTION TO THE INVESTIGATION OF STOPPING SIGHT DISTANCE

More information

LECTURE 3 ALGORITHM DESIGN PARADIGMS

LECTURE 3 ALGORITHM DESIGN PARADIGMS LECTURE 3 ALGORITHM DESIGN PARADIGMS Introduction Algorithm Design Paradigms: General approaches to the construction of efficient solutions to problems. Such methods are of interest because: They provide

More information

Automatic visual recognition for metro surveillance

Automatic visual recognition for metro surveillance Automatic visual recognition for metro surveillance F. Cupillard, M. Thonnat, F. Brémond Orion Research Group, INRIA, Sophia Antipolis, France Abstract We propose in this paper an approach for recognizing

More information

Robot Vision without Calibration

Robot Vision without Calibration XIV Imeko World Congress. Tampere, 6/97 Robot Vision without Calibration Volker Graefe Institute of Measurement Science Universität der Bw München 85577 Neubiberg, Germany Phone: +49 89 6004-3590, -3587;

More information

Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization

Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization 2017 2 nd International Electrical Engineering Conference (IEEC 2017) May. 19 th -20 th, 2017 at IEP Centre, Karachi, Pakistan Meta- Heuristic based Optimization Algorithms: A Comparative Study of Genetic

More information

Week 7 Picturing Network. Vahe and Bethany

Week 7 Picturing Network. Vahe and Bethany Week 7 Picturing Network Vahe and Bethany Freeman (2005) - Graphic Techniques for Exploring Social Network Data The two main goals of analyzing social network data are identification of cohesive groups

More information

Digital copying involves a process. Developing a raster detector system with the J array processing language SOFTWARE.

Digital copying involves a process. Developing a raster detector system with the J array processing language SOFTWARE. Developing a raster detector system with the J array processing language by Jan Jacobs All digital copying aims to reproduce an original image as faithfully as possible under certain constraints. In the

More information

An actor-critic reinforcement learning controller for a 2-DOF ball-balancer

An actor-critic reinforcement learning controller for a 2-DOF ball-balancer An actor-critic reinforcement learning controller for a 2-DOF ball-balancer Andreas Stückl Michael Meyer Sebastian Schelle Projektpraktikum: Computational Neuro Engineering 2 Empty side for praktikums

More information

Algorithm Design Paradigms

Algorithm Design Paradigms CmSc250 Intro to Algorithms Algorithm Design Paradigms Algorithm Design Paradigms: General approaches to the construction of efficient solutions to problems. Such methods are of interest because: They

More information

CHAPTER 6 QUANTITATIVE PERFORMANCE ANALYSIS OF THE PROPOSED COLOR TEXTURE SEGMENTATION ALGORITHMS

CHAPTER 6 QUANTITATIVE PERFORMANCE ANALYSIS OF THE PROPOSED COLOR TEXTURE SEGMENTATION ALGORITHMS 145 CHAPTER 6 QUANTITATIVE PERFORMANCE ANALYSIS OF THE PROPOSED COLOR TEXTURE SEGMENTATION ALGORITHMS 6.1 INTRODUCTION This chapter analyzes the performance of the three proposed colortexture segmentation

More information

A Generalized Method to Solve Text-Based CAPTCHAs

A Generalized Method to Solve Text-Based CAPTCHAs A Generalized Method to Solve Text-Based CAPTCHAs Jason Ma, Bilal Badaoui, Emile Chamoun December 11, 2009 1 Abstract We present work in progress on the automated solving of text-based CAPTCHAs. Our method

More information

Comparison of Some Motion Detection Methods in cases of Single and Multiple Moving Objects

Comparison of Some Motion Detection Methods in cases of Single and Multiple Moving Objects Comparison of Some Motion Detection Methods in cases of Single and Multiple Moving Objects Shamir Alavi Electrical Engineering National Institute of Technology Silchar Silchar 788010 (Assam), India alavi1223@hotmail.com

More information

Small-scale objects extraction in digital images

Small-scale objects extraction in digital images 102 Int'l Conf. IP, Comp. Vision, and Pattern Recognition IPCV'15 Small-scale objects extraction in digital images V. Volkov 1,2 S. Bobylev 1 1 Radioengineering Dept., The Bonch-Bruevich State Telecommunications

More information

Object Detection in Video Streams

Object Detection in Video Streams Object Detection in Video Streams Sandhya S Deore* *Assistant Professor Dept. of Computer Engg., SRES COE Kopargaon *sandhya.deore@gmail.com ABSTRACT Object Detection is the most challenging area in video

More information

Computer Science Faculty, Bandar Lampung University, Bandar Lampung, Indonesia

Computer Science Faculty, Bandar Lampung University, Bandar Lampung, Indonesia Application Object Detection Using Histogram of Oriented Gradient For Artificial Intelegence System Module of Nao Robot (Control System Laboratory (LSKK) Bandung Institute of Technology) A K Saputra 1.,

More information

Research Fellow, Korea Institute of Civil Engineering and Building Technology, Korea (*corresponding author) 2

Research Fellow, Korea Institute of Civil Engineering and Building Technology, Korea (*corresponding author) 2 Algorithm and Experiment for Vision-Based Recognition of Road Surface Conditions Using Polarization and Wavelet Transform 1 Seung-Ki Ryu *, 2 Taehyeong Kim, 3 Eunjoo Bae, 4 Seung-Rae Lee 1 Research Fellow,

More information