Multicriteria Image Thresholding Based on Multiobjective Particle Swarm Optimization

Size: px
Start display at page:

Download "Multicriteria Image Thresholding Based on Multiobjective Particle Swarm Optimization"

Transcription

1 Applied Mathematical Sciences, Vol. 8, 2014, no. 3, HIKARI Ltd, Multicriteria Image Thresholding Based on Multiobjective Particle Swarm Optimization Maxvell Arulraj, Amir Nakib, Yann Cooren and Patrick Siarry Laboratoire LISSI, Université Paris Est Créteil 122 rue Paul Armangot, Vitry-sur-Seine, France Copyright c 2014 Maxvell Arulraj et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract This paper deals with using the MultiObjective Particle Swarm Optimization (MOPSO) [5] metaheuristic for optimally thresholding medical images. Two criteria are optimized: the interclass variance) and the Shannon entropy. The process generates a Pareto front with various segmentations, leaving the final choice to the user. The paper will, first, describe MOPSO algorithm and then, focus on obtained results. Keywords: image segmentation, image thresholding, multiobjective optimization, Pareto approach, Particle swarm optimization, metaheuristic 1 Introduction Image segmentation is an essential step in many computer vision applications. If man can naturally separate objects in an image, the development of segmentation algorithms for the automation of this task is still an important axe of research in image processing. Many segmentation criteria exist according to the domain of application or the type of image. Criteria may be based, for example, on edge detection or even on light intensity. From an algorithmic view, the segmentation consists in assigning each pixel in the image a label belonging to a given region. This classification can be achieved in two ways: the supervised mode, where the number of regions and their characteristics are provided by the user, and the unsupervised mode, where information on the classification process is determined completely automatically. There are

2 132 Maxvell Arulraj et al. various methods of segmentation that can be divided in two groups: methods based on regions [5]: the image is decomposed into a set of related regions according to homogeneity. These methods are used especially when the image histogram is multimodal. And methods based on contours [4, 6]: the shape is a high variation region of light intensity. The idea is to detect the shape of objects in order to estimate light intensity variation for each pixel. In this paper, we only focus on the region based methods. For both classes of methods, two approaches are possible: parametric and non-parametric segmentation. Parametric segmentation [7]: the histogram of the image is approximated by a probability distribution such as a Gaussian model. Thresholds are then fixed to separate histogram into classes. Non parametric segmentation [4, 6, 11]: the optimal level of segmentation is found without any parameter estimation. Two reference methods are used in this survey: the interclass variance [2] and the Shannon entropy [3]. The objective here is rather to optimize statistical criteria. Segmentation by thresholding is the most common technique to extract objects. Segmentation is based on the assumption that objects can be separated by the variations of gray levels. Thresholds are positioned on the histogram to classify pixels per grayscale class. Then, the pixels are classified according to their brightness, obtained from the histogram. For supervised classification, the number of classes is fixed before the process and for unsupervised classification, the number of classes is calculated automatically. In this work, we propose to perform segmentation with a non parametric method. Two statistical criteria will be optimized simultaneously. Segmentation becomes an optimization problem, resolved by using a metaheuristic. Metaheuristic algorithms try to solve difficult optimization problems from engineering for which conventional methods are not effective. They are known to be generic and optimize a broad range of problems without necessarily changing the algorithm in depth. To overcome the obstacle of local minima, they try to mimic natural systems. Inspired by natural phenomena such as simulated annealing or ethology, these algorithms tolerate a temporary degradation of the solution. It then becomes possible to escape from a "sink" and explore other more promising "valleys". This paper proposes to use an algorithm inspired from social behavior of animal swarms called PSO [8], modified in view of a multi-objective optimization. 2 MOPSO algorithm Two types of methods are available for segmentation by thresholding: parametric and non parametric techniques. In this study, a second type method will be used. No parameter is estimated, the histogram is not approximated. The

3 Multicriteria image thresholding 133 objective is to optimize two statistical criteria. Multi-objective optimization will be first treated, followed by the presentation of the pseudo-code and of our criteria. Metaheuristics are originally designed for mono-objective problems. Some problems require modeling several criteria. Consequently, the resolution does not lead to a single solution but to a set of compromises between different criteria. The particle swarm optimization (PSO) algorithm [8] has been adapted to handle multiple objectives optimization problems. The well known algorithm is MOPSO (Multi-Objective Particle Swarm Optimization) [5]. Solving a multiobjective problem consists in selecting non-dominated solutions found by the algorithm during the execution. Solutions obtained at the end of the execution must be non-dominated compared to all positions reached by particles during successive iterations. Consequently, it seems to be necessary to establish a list of non-dominated solutions found during all the process. Then, a potential solution is accepted only if it is not dominated compared to the contents of the archive. The diversity of non-dominated solutions stored in the archive is maintained using a criterion based on the crowding distance: for an element i of the Pareto front, the crowding distance is the volume of the largest hypercube separating it from its neighbors. The aim is, thus, to obtain a uniform front, as wide as possible. More details about MOPSO and multiobjective optimization can be found in [5, 9, 10, 11]. 3 Segmentation criteria The purpose of this paper is to achieve an automatic thresholding using a non parametric method. Two statistical criteria are optimized: the interclass variance based method [2] and the Shannon entropy based method [3]. One can use other criteria, our goal here, is to show the efficiency of the MO approach for image segmentation. 4 Results and discussions The algorithm was tested using several kinds of images, in this paper, we present two main results: the first one is that obtained on the well known Lena and the second one results from a real world application in the case of brain MRI image segmentation. For our experimentations, we used the following fitting of MOPSO: we set the number of particles to 20. The maximum number of iterations was set to At the end of the algorithm, a Pareto front is available, where each particle of this front proposes its segmentation result (a set of thresholds). In first we check if the proposed set of solutions at the end of the process corresponds to the exact Pareto optimal front. To do so, the obtained result is

4 134 Maxvell Arulraj et al. compared to an exhaustive search. Both statistical criteria are evaluated by an exhaustive search and compared to optimal results obtained by non exhaustive search. Figure 1 shows the test realized on image "Lena". The exhaustive search optimizes criteria for a 4 classes segmentation. It shows the values of all particles during all the process, they reach at the end the Pareto front, highlighted in bold. We also show the non-exhaustive process, the position of the Pareto front is still the same. This result demonstrates that particles can reach an optimal solution by a non-exhaustive search using MOPSO. We consider the MRI image of the brain with a ventricle atrophy (Figure 2). In this application, our goal is to segment the ventricle in order to estimate its volume. The histogram presented in Figure 2 (b) presents three peaks. The first is high and fine, followed by a second, smaller. Only a very small distance is available to possibly place a threshold between these two modes. There is also a last peak, wide and flat. Figure 9 shows different Pareto fronts for 7, 5 or 3 classes segmentation. For a 7 classes segmentation, the optimal Pareto front is excellent (Figure 2(c)). It is wide and uniform. The crowding distance between particles is high, even for 3 classes segmentation. A large front, well distributed, is interesting for the user. The range of proposed solutions is large; the user can either select solutions that optimize better the first criterion (inter-class variance) or choose to focus on the second criterion (entropy of Shannon). He can also choose solutions which are a compromise between these two criteria. Figure 2(d) shows three segmented images obtained by three particles A, B and C for a 5 classes segmentation. The particle A gives a set of thresholds almost similar to that of a single objective optimization (with inter-class variance). The set of thresholds given by particle B is a compromise between both criteria. Finally, the particle C provides a thresholding similar to a single objective optimization with the entropy of Shannon. The best segmentation is done by particle C, the gray matter is more homogeneous in this case compared to the others. 5 Conclusion In this paper, a new application of MO approach was presented. Indeed, this approach allows to mix several segmentation approaches and takes profit from the advantage of each one depending on the input images. As it was shown, the obtained Pareto fronts are large and well distributed with an interesting compromise zone. A future objective will be to evaluate this algorithm through other methods currently in use for automatic thresholding. In this work, the algorithm provides a set of solutions with a good quality and leaves the final choice to the user. In work under progress, we will assist the user in his choice

5 Multicriteria image thresholding 135 (a) (b) (c) Figure 1: Illustrations of the performances of the proposed approach on standard images. (a) Lena image, (b)result of the Exhaustive search for a 4 classes segmentation on the image in (a) and the zoom on the Pareto front, (b) MOPSO result for a 4 classes segmentation on the image in (a) and the zoom on the Pareto front.

6 136 Maxvell Arulraj et al. and during the first uses, the program will learn the behavior of the user. Then, the system will anticipate preferences and propose only few solutions to the user (one or two for instance). References [1] C.R. Raquel and P.C. Naval, "An effective use of crowding distance in multiobjective particle swarm optimization", in Proceedings of the 2005 Genetic and Evolutionary Computation Conference, 2005, pp [2] N.A. Otsu., "A Threshold Selection Method from Gray Level Histograms", IEEE Trans. on Syst., Man and Cyb., vol. 9, no. 1, pp , [3] J. N. Kapur, P. K. Sahoo, and A. C. K. Wong, "A new method for graylevel picture thresholding using the entropy of the histogram", Computer Vision, Graphics and Image Processing, vol. 29, pp , [4] J. Kitler and J. Illingworth, "Minimum error thresholding", vol. 19 (1), 1986, p [5] R. C. Gonzalez and R.E. Woods, "Digital image processing using Matlab", in Pearson Prentice Hall, [6] W. Synder, G. Bilbro, A. Logenthiran, and S. Rajala, "Optimal thresholding- A new approach", Pattern Recognition Letters, vol. 11, pp , [7] J. Kennedy and R.C. Eberhart, "Particle Swarm Optimisation", in Proceedings of the IEEE International Conference On Neural Networks, 1995, pp [8] A.C. Briza and P.C. Naval Jr, "Design of Stock Trading System for Historical Market Data using Multiobjective Particle Swarm Optimization of Technical Indicators", in GECCO 2008, Atlanta,Georgia (USA), July [9] V. Pareto, Cours d économie politique, [10] A. Nakib, H. Oulhadj, and P. Siarry, "Non-supervised image segmentation based on multiobjective optimization", Pattern Recognition Letters, vol. 29, pp , [11] M. D. Levine and A.M. Nazif, "Dynamic measurement of computer generated image segmentations", pp , Received: January 19, 2013

7 Multicriteria image thresholding 137 (a) (b) (c) (d) Figure 2: Illustrations of the performances of the proposed approach on real world images. (a) brain MRI image, (b) histogram of the image in (a), (c) Obtained Pareto fronts using MOPSO for a 7, 5 and 3 classes, segmentation of the brain MRI image, from left to right respectively, (d) Histograms and segmented images obtained by particles A, B and C from Pareto front for a 5 classes segmentation on the pathologic brain, from left to right respectively.

Multi-level Fractal Decomposition Based. Feature Extraction Using Two Dimensional. Discrete Wavelet Transforms

Multi-level Fractal Decomposition Based. Feature Extraction Using Two Dimensional. Discrete Wavelet Transforms Advanced Studies in Theoretical Physics Vol. 8, 2014, no. 20, 849-856 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/astp.2014.49124 Multi-level Fractal Decomposition Based Feature Extraction

More information

Robust Descriptive Statistics Based PSO Algorithm for Image Segmentation

Robust Descriptive Statistics Based PSO Algorithm for Image Segmentation Robust Descriptive Statistics Based PSO Algorithm for Image Segmentation Ripandeep Kaur 1, Manpreet Kaur 2 1, 2 Punjab Technical University, Chandigarh Engineering College, Landran, Punjab, India Abstract:

More information

Image Segmentation Based on. Modified Tsallis Entropy

Image Segmentation Based on. Modified Tsallis Entropy Contemporary Engineering Sciences, Vol. 7, 2014, no. 11, 523-529 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ces.2014.4439 Image Segmentation Based on Modified Tsallis Entropy V. Vaithiyanathan

More information

A Clustering-Based Method for. Brain Tumor Segmentation

A Clustering-Based Method for. Brain Tumor Segmentation Contemporary Engineering Sciences, Vol. 9, 2016, no. 15, 743-754 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ces.2016.6564 A Clustering-Based Method for Brain Tumor Segmentation Idanis Diaz

More information

An ICA based Approach for Complex Color Scene Text Binarization

An ICA based Approach for Complex Color Scene Text Binarization An ICA based Approach for Complex Color Scene Text Binarization Siddharth Kherada IIIT-Hyderabad, India siddharth.kherada@research.iiit.ac.in Anoop M. Namboodiri IIIT-Hyderabad, India anoop@iiit.ac.in

More information

arxiv:cs/ v1 [cs.cv] 12 Feb 2006

arxiv:cs/ v1 [cs.cv] 12 Feb 2006 Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm arxiv:cs/0602044v1 [cs.cv] 12 Feb 2006 S. Arora a, J. Acharya b, A. Verma c, Prasanta K. Panigrahi c 1 a Dhirubhai

More information

A Texture Extraction Technique for. Cloth Pattern Identification

A Texture Extraction Technique for. Cloth Pattern Identification Contemporary Engineering Sciences, Vol. 8, 2015, no. 3, 103-108 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ces.2015.412261 A Texture Extraction Technique for Cloth Pattern Identification Reshmi

More information

Research Article Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation

Research Article Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation Discrete Dynamics in Nature and Society Volume 2008, Article ID 384346, 8 pages doi:10.1155/2008/384346 Research Article Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation

More information

Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding

Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding e Scientific World Journal, Article ID 746260, 8 pages http://dx.doi.org/10.1155/2014/746260 Research Article Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding Ming-Yi

More information

SEPARATING TEXT AND BACKGROUND IN DEGRADED DOCUMENT IMAGES A COMPARISON OF GLOBAL THRESHOLDING TECHNIQUES FOR MULTI-STAGE THRESHOLDING

SEPARATING TEXT AND BACKGROUND IN DEGRADED DOCUMENT IMAGES A COMPARISON OF GLOBAL THRESHOLDING TECHNIQUES FOR MULTI-STAGE THRESHOLDING SEPARATING TEXT AND BACKGROUND IN DEGRADED DOCUMENT IMAGES A COMPARISON OF GLOBAL THRESHOLDING TECHNIQUES FOR MULTI-STAGE THRESHOLDING GRAHAM LEEDHAM 1, SAKET VARMA 2, ANISH PATANKAR 2 and VENU GOVINDARAJU

More information

A Massively Parallel Virtual Machine for. SIMD Architectures

A Massively Parallel Virtual Machine for. SIMD Architectures Advanced Studies in Theoretical Physics Vol. 9, 15, no. 5, 37-3 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.19/astp.15.519 A Massively Parallel Virtual Machine for SIMD Architectures M. Youssfi and

More information

Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization

Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization Richa Agnihotri #1, Dr. Shikha Agrawal #1, Dr. Rajeev Pandey #1 # Department of Computer Science Engineering, UIT,

More information

A derivative based algorithm for image thresholding

A derivative based algorithm for image thresholding A derivative based algorithm for image thresholding André Ricardo Backes arbackes@yahoo.com.br Bruno Augusto Nassif Travençolo travencolo@gmail.com Mauricio Cunha Escarpinati escarpinati@gmail.com Faculdade

More information

What is the Optimal Bin Size of a Histogram: An Informal Description

What is the Optimal Bin Size of a Histogram: An Informal Description International Mathematical Forum, Vol 12, 2017, no 15, 731-736 HIKARI Ltd, wwwm-hikaricom https://doiorg/1012988/imf20177757 What is the Optimal Bin Size of a Histogram: An Informal Description Afshin

More information

SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) volume1 issue7 September 2014

SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) volume1 issue7 September 2014 SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) volume issue7 September 24 A Thresholding Method for Color Image Binarization Kalavathi P Department of Computer Science and

More information

Argha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India.

Argha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India. Volume 3, Issue 3, March 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Training Artificial

More information

Improved Glowworm Swarm Optimization Algorithm applied to Multi-level Thresholding

Improved Glowworm Swarm Optimization Algorithm applied to Multi-level Thresholding Improved Glowworm Swarm Optimization Algorithm applied to Multi-level Thresholding Simone A. Ludwig North Dakota State University Fargo, ND, USA simone.ludwig@ndsu.edu Abstract Image segmentation is considered

More information

Gray-Level Reduction Using Local Spatial Features

Gray-Level Reduction Using Local Spatial Features Computer Vision and Image Understanding 78, 336 350 (2000) doi:10.1006/cviu.2000.0838, available online at http://www.idealibrary.com on Gray-Level Reduction Using Local Spatial Features Nikos Papamarkos

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

QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION

QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION International Journal of Computer Engineering and Applications, Volume VIII, Issue I, Part I, October 14 QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION Shradha Chawla 1, Vivek Panwar 2 1 Department

More information

Applying Catastrophe Theory to Image Segmentation

Applying Catastrophe Theory to Image Segmentation Applying Catastrophe Theory to Image Segmentation Mohamad Raad, Majd Ghareeb, Ali Bazzi Department of computer and communications engineering Lebanese International University Beirut, Lebanon Abstract

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

Unidimensional Search for solving continuous high-dimensional optimization problems

Unidimensional Search for solving continuous high-dimensional optimization problems 2009 Ninth International Conference on Intelligent Systems Design and Applications Unidimensional Search for solving continuous high-dimensional optimization problems Vincent Gardeux, Rachid Chelouah,

More information

Region-based Segmentation

Region-based Segmentation Region-based Segmentation Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) to obtain a compact representation. Applications: Finding tumors, veins, etc.

More information

Adaptive Wavelet Image Denoising Based on the Entropy of Homogenus Regions

Adaptive Wavelet Image Denoising Based on the Entropy of Homogenus Regions International Journal of Electrical and Electronic Science 206; 3(4): 9-25 http://www.aascit.org/journal/ijees ISSN: 2375-2998 Adaptive Wavelet Image Denoising Based on the Entropy of Homogenus Regions

More information

OCR For Handwritten Marathi Script

OCR For Handwritten Marathi Script International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012 1 OCR For Handwritten Marathi Script Mrs.Vinaya. S. Tapkir 1, Mrs.Sushma.D.Shelke 2 1 Maharashtra Academy Of Engineering,

More information

WEINER FILTER AND SUB-BLOCK DECOMPOSITION BASED IMAGE RESTORATION FOR MEDICAL APPLICATIONS

WEINER FILTER AND SUB-BLOCK DECOMPOSITION BASED IMAGE RESTORATION FOR MEDICAL APPLICATIONS WEINER FILTER AND SUB-BLOCK DECOMPOSITION BASED IMAGE RESTORATION FOR MEDICAL APPLICATIONS ARIFA SULTANA 1 & KANDARPA KUMAR SARMA 2 1,2 Department of Electronics and Communication Engineering, Gauhati

More information

Histogram and watershed based segmentation of color images

Histogram and watershed based segmentation of color images Histogram and watershed based segmentation of color images O. Lezoray H. Cardot LUSAC EA 2607 IUT Saint-Lô, 120 rue de l'exode, 50000 Saint-Lô, FRANCE Abstract A novel method for color image segmentation

More information

A Multi-objective Binary Cuckoo Search for Bicriteria

A Multi-objective Binary Cuckoo Search for Bicriteria I.J. Information Engineering and Electronic Business, 203, 4, 8-5 Published Online October 203 in MECS (http://www.mecs-press.org/) DOI: 0.585/ijieeb.203.04.02 A Multi-objective Binary Cuckoo Search for

More information

A Comparison of Evolutionary Algorithms: PSO, DE and GA for Fuzzy C-Partition

A Comparison of Evolutionary Algorithms: PSO, DE and GA for Fuzzy C-Partition A Comparison Evolutionary Algorithms: PSO, DE and GA for Fuzzy C-Partition Assas Ouarda Department Computer Science, University M sila, Algeria. Laboratory Analysis Signals and Systems (LASS). University

More information

AN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE

AN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE AN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE sbsridevi89@gmail.com 287 ABSTRACT Fingerprint identification is the most prominent method of biometric

More information

Available online Journal of Scientific and Engineering Research, 2019, 6(1): Research Article

Available online   Journal of Scientific and Engineering Research, 2019, 6(1): Research Article Available online www.jsaer.com, 2019, 6(1):193-197 Research Article ISSN: 2394-2630 CODEN(USA): JSERBR An Enhanced Application of Fuzzy C-Mean Algorithm in Image Segmentation Process BAAH Barida 1, ITUMA

More information

Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques

Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques Nasser Sadati Abstract Particle Swarm Optimization (PSO) algorithms recently invented as intelligent optimizers with several highly

More information

A Function-Based Image Binarization based on Histogram

A Function-Based Image Binarization based on Histogram International Research Journal of Applied and Basic Sciences 2015 Available online at www.irjabs.com ISSN 2251-838X / Vol, 9 (3): 418-426 Science Explorer Publications A Function-Based Image Binarization

More information

MRI Brain Image Segmentation based on Thresholding

MRI Brain Image Segmentation based on Thresholding International Journal of Advanced Computer Research (ISSN (print): 49-777 ISSN (online): 77-7970) Volume-3 Number- Issue-8 March-03 MRI Brain Image Segmentation based on Thresholding G. Evelin Sujji, Y.V.S.

More information

Binary Histogram in Image Classification for Retrieval Purposes

Binary Histogram in Image Classification for Retrieval Purposes Binary Histogram in Image Classification for Retrieval Purposes Iivari Kunttu 1, Leena Lepistö 1, Juhani Rauhamaa 2, and Ari Visa 1 1 Tampere University of Technology Institute of Signal Processing P.

More information

Particle Swarm Optimization applied to Pattern Recognition

Particle Swarm Optimization applied to Pattern Recognition Particle Swarm Optimization applied to Pattern Recognition by Abel Mengistu Advisor: Dr. Raheel Ahmad CS Senior Research 2011 Manchester College May, 2011-1 - Table of Contents Introduction... - 3 - Objectives...

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

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

PROBLEM FORMULATION AND RESEARCH METHODOLOGY

PROBLEM FORMULATION AND RESEARCH METHODOLOGY PROBLEM FORMULATION AND RESEARCH METHODOLOGY ON THE SOFT COMPUTING BASED APPROACHES FOR OBJECT DETECTION AND TRACKING IN VIDEOS CHAPTER 3 PROBLEM FORMULATION AND RESEARCH METHODOLOGY The foregoing chapter

More information

A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization

A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization Dr. Liu Dasheng James Cook University, Singapore / 48 Outline of Talk. Particle Swam Optimization 2. Multiobjective Particle Swarm

More information

Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization

Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization adfa, p. 1, 2011. Springer-Verlag Berlin Heidelberg 2011 Devang Agarwal and Deepak Sharma Department of Mechanical

More information

EDGE BASED REGION GROWING

EDGE BASED REGION GROWING EDGE BASED REGION GROWING Rupinder Singh, Jarnail Singh Preetkamal Sharma, Sudhir Sharma Abstract Image segmentation is a decomposition of scene into its components. It is a key step in image analysis.

More information

Adaptive Local Thresholding for Fluorescence Cell Micrographs

Adaptive Local Thresholding for Fluorescence Cell Micrographs TR-IIS-09-008 Adaptive Local Thresholding for Fluorescence Cell Micrographs Jyh-Ying Peng and Chun-Nan Hsu November 11, 2009 Technical Report No. TR-IIS-09-008 http://www.iis.sinica.edu.tw/page/library/lib/techreport/tr2009/tr09.html

More information

Image Segmentation Based on Watershed and Edge Detection Techniques

Image Segmentation Based on Watershed and Edge Detection Techniques 0 The International Arab Journal of Information Technology, Vol., No., April 00 Image Segmentation Based on Watershed and Edge Detection Techniques Nassir Salman Computer Science Department, Zarqa Private

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

CITS 4402 Computer Vision

CITS 4402 Computer Vision CITS 4402 Computer Vision A/Prof Ajmal Mian Adj/A/Prof Mehdi Ravanbakhsh, CEO at Mapizy (www.mapizy.com) and InFarm (www.infarm.io) Lecture 02 Binary Image Analysis Objectives Revision of image formation

More information

Novel Approaches of Image Segmentation for Water Bodies Extraction

Novel Approaches of Image Segmentation for Water Bodies Extraction Novel Approaches of Image Segmentation for Water Bodies Extraction Naheed Sayyed 1, Prarthana Joshi 2, Chaitali Wagh 3 Student, Electronics & Telecommunication, PGMCOE, Pune, India 1 Student, Electronics

More information

A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation

A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation A Novel q-parameter Automation in Tsallis Entropy for Image Segmentation M Seetharama Prasad KL University Vijayawada- 522202 P Radha Krishna KL University Vijayawada- 522202 ABSTRACT Image Thresholding

More information

Global Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images

Global Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images Global Thresholding Techniques to Classify Dead Cells in Diffusion Weighted Magnetic Resonant Images Ravi S 1, A. M. Khan 2 1 Research Student, Department of Electronics, Mangalore University, Karnataka

More information

Overcompressing JPEG images with Evolution Algorithms

Overcompressing JPEG images with Evolution Algorithms Author manuscript, published in "EvoIASP2007, Valencia : Spain (2007)" Overcompressing JPEG images with Evolution Algorithms Jacques Lévy Véhel 1, Franklin Mendivil 2 and Evelyne Lutton 1 1 Inria, Complex

More information

Application of global thresholding in bread porosity evaluation

Application of global thresholding in bread porosity evaluation International Journal of Intelligent Systems and Applications in Engineering ISSN:2147-67992147-6799www.atscience.org/IJISAE Advanced Technology and Science Original Research Paper Application of global

More information

Content Based Image Retrieval using Combined Features of Color and Texture Features with SVM Classification

Content Based Image Retrieval using Combined Features of Color and Texture Features with SVM Classification Content Based Image Retrieval using Combined Features of Color and Texture Features with SVM Classification R. Usha [1] K. Perumal [2] Research Scholar [1] Associate Professor [2] Madurai Kamaraj University,

More information

CHAPTER VIII SEGMENTATION USING REGION GROWING AND THRESHOLDING ALGORITHM

CHAPTER VIII SEGMENTATION USING REGION GROWING AND THRESHOLDING ALGORITHM CHAPTER VIII SEGMENTATION USING REGION GROWING AND THRESHOLDING ALGORITHM 8.1 Algorithm Requirement The analysis of medical images often requires segmentation prior to visualization or quantification.

More information

Multithresholding of color and gray-level images through a neural network technique

Multithresholding of color and gray-level images through a neural network technique Image and Vision Computing 8 (2000) 23 222 www.elsevier.com/locate/imavis Multithresholding of color and gray-level images through a neural network technique N. Papamarkos*, C. Strouthopoulos, I. Andreadis

More information

Segmenting an Image Assigning labels to pixels (cat, ball, floor) Point processing: Lecture 3: Region Based Vision. Overview

Segmenting an Image Assigning labels to pixels (cat, ball, floor) Point processing: Lecture 3: Region Based Vision. Overview Slide 2 Lecture 3: Region Based Vision Dr Carole Twining Thursday 18th March 1:00pm 1:50pm Segmenting an Image Assigning labels to pixels (cat, ball, floor) Point processing: colour or grayscale values,

More information

Image Edge Detection Using Ant Colony Optimization

Image Edge Detection Using Ant Colony Optimization Image Edge Detection Using Ant Colony Optimization Anna Veronica Baterina and Carlos Oppus Abstract Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging behavior of

More information

Face Detection for Skintone Images Using Wavelet and Texture Features

Face Detection for Skintone Images Using Wavelet and Texture Features Face Detection for Skintone Images Using Wavelet and Texture Features 1 H.C. Vijay Lakshmi, 2 S. Patil Kulkarni S.J. College of Engineering Mysore, India 1 vijisjce@yahoo.co.in, 2 pk.sudarshan@gmail.com

More information

A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model

A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model Deepak Ranjan Nayak Dept. of CSE, College of Engineering and Technology Bhubaneswar, Odisha India-751003

More information

Generalized Fuzzy Clustering Model with Fuzzy C-Means

Generalized Fuzzy Clustering Model with Fuzzy C-Means Generalized Fuzzy Clustering Model with Fuzzy C-Means Hong Jiang 1 1 Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, US jiangh@cse.sc.edu http://www.cse.sc.edu/~jiangh/

More information

Evolutionary multi-objective algorithm design issues

Evolutionary multi-objective algorithm design issues Evolutionary multi-objective algorithm design issues Karthik Sindhya, PhD Postdoctoral Researcher Industrial Optimization Group Department of Mathematical Information Technology Karthik.sindhya@jyu.fi

More information

An Approach to Polygonal Approximation of Digital CurvesBasedonDiscreteParticleSwarmAlgorithm

An Approach to Polygonal Approximation of Digital CurvesBasedonDiscreteParticleSwarmAlgorithm Journal of Universal Computer Science, vol. 13, no. 10 (2007), 1449-1461 submitted: 12/6/06, accepted: 24/10/06, appeared: 28/10/07 J.UCS An Approach to Polygonal Approximation of Digital CurvesBasedonDiscreteParticleSwarmAlgorithm

More information

Segmentation of Noisy Binary Images Containing Circular and Elliptical Objects using Genetic Algorithms

Segmentation of Noisy Binary Images Containing Circular and Elliptical Objects using Genetic Algorithms Segmentation of Noisy Binary Images Containing Circular and Elliptical Objects using Genetic Algorithms B. D. Phulpagar Computer Engg. Dept. P. E. S. M. C. O. E., Pune, India. R. S. Bichkar Prof. ( Dept.

More information

Segmentation Using a Region Growing Thresholding

Segmentation Using a Region Growing Thresholding Segmentation Using a Region Growing Thresholding Matei MANCAS 1, Bernard GOSSELIN 1, Benoît MACQ 2 1 Faculté Polytechnique de Mons, Circuit Theory and Signal Processing Laboratory Bâtiment MULTITEL/TCTS

More information

Image Segmentation Techniques

Image Segmentation Techniques A Study On Image Segmentation Techniques Palwinder Singh 1, Amarbir Singh 2 1,2 Department of Computer Science, GNDU Amritsar Abstract Image segmentation is very important step of image analysis which

More information

Exponential Entropy Approach for Image Edge Detection

Exponential Entropy Approach for Image Edge Detection International Journal of Theoretical and Applied Mathematics 2016; 2(2): 150-155 http://www.sciencepublishinggroup.com/j/ijtam doi: 10.11648/j.ijtam.20160202.29 Exponential Entropy Approach for Image Edge

More information

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering Digital Image Processing Prof. P.K. Biswas Department of Electronics & Electrical Communication Engineering Indian Institute of Technology, Kharagpur Image Segmentation - III Lecture - 31 Hello, welcome

More information

Lecture: Segmentation I FMAN30: Medical Image Analysis. Anders Heyden

Lecture: Segmentation I FMAN30: Medical Image Analysis. Anders Heyden Lecture: Segmentation I FMAN30: Medical Image Analysis Anders Heyden 2017-11-13 Content What is segmentation? Motivation Segmentation methods Contour-based Voxel/pixel-based Discussion What is segmentation?

More information

Image Analysis Image Segmentation (Basic Methods)

Image Analysis Image Segmentation (Basic Methods) Image Analysis Image Segmentation (Basic Methods) Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Computer Vision course

More information

Improved Integral Histogram Algorithm. for Big Sized Images in CUDA Environment

Improved Integral Histogram Algorithm. for Big Sized Images in CUDA Environment Contemporary Engineering Sciences, Vol. 7, 2014, no. 24, 1415-1423 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ces.2014.49174 Improved Integral Histogram Algorithm for Big Sized Images in CUDA

More information

Problems of Sensor Placement for Intelligent Environments of Robotic Testbeds

Problems of Sensor Placement for Intelligent Environments of Robotic Testbeds Int. Journal of Math. Analysis, Vol. 7, 2013, no. 47, 2333-2339 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijma.2013.36150 Problems of Sensor Placement for Intelligent Environments of Robotic

More information

The Generalized Stability Indicator of. Fragment of the Network. II Critical Performance Event

The Generalized Stability Indicator of. Fragment of the Network. II Critical Performance Event Applied Mathematical Sciences, Vol. 7, 2013, no. 113, 5627-5632 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2013.38472 The Generalized Stability Indicator of Fragment of the Network. II

More information

Toward Part-based Document Image Decoding

Toward Part-based Document Image Decoding 2012 10th IAPR International Workshop on Document Analysis Systems Toward Part-based Document Image Decoding Wang Song, Seiichi Uchida Kyushu University, Fukuoka, Japan wangsong@human.ait.kyushu-u.ac.jp,

More information

Optik 124 (2013) Contents lists available at SciVerse ScienceDirect. Optik. jou rnal homepage:

Optik 124 (2013) Contents lists available at SciVerse ScienceDirect. Optik. jou rnal homepage: Optik 14 013 45 431 Contents lists available at SciVerse ScienceDirect Optik jou rnal homepage: www.elsevier.de/ijleo Range Limited Bi-Histogram Equalization for image contrast enhancement Chao Zuo, Qian

More information

GA is the most popular population based heuristic algorithm since it was developed by Holland in 1975 [1]. This algorithm runs faster and requires les

GA is the most popular population based heuristic algorithm since it was developed by Holland in 1975 [1]. This algorithm runs faster and requires les Chaotic Crossover Operator on Genetic Algorithm Hüseyin Demirci Computer Engineering, Sakarya University, Sakarya, 54187, Turkey Ahmet Turan Özcerit Computer Engineering, Sakarya University, Sakarya, 54187,

More information

RESTORATION OF DEGRADED DOCUMENTS USING IMAGE BINARIZATION TECHNIQUE

RESTORATION OF DEGRADED DOCUMENTS USING IMAGE BINARIZATION TECHNIQUE RESTORATION OF DEGRADED DOCUMENTS USING IMAGE BINARIZATION TECHNIQUE K. Kaviya Selvi 1 and R. S. Sabeenian 2 1 Department of Electronics and Communication Engineering, Communication Systems, Sona College

More information

Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method

Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method T. Balaji 1, M. Sumathi 2 1 Assistant Professor, Dept. of Computer Science, Govt. Arts College,

More information

MEDICAL IMAGE SEGMENTATION BY MARKER- CONTROLLED WATERSHED AND MATHEMATICAL MORPHOLOGY

MEDICAL IMAGE SEGMENTATION BY MARKER- CONTROLLED WATERSHED AND MATHEMATICAL MORPHOLOGY MEDICAL IMAGE SEGMENTATION BY MARKER- CONTROLLED WATERSHED AND MATHEMATICAL MORPHOLOGY Ahmad EL ALLAOUI 1 and M barek NASRI 1 1 LABO MATSI, ESTO, B.P 473, University Mohammed I OUJDA, MOROCCO. ahmadallaoui@yahoo.fr

More information

Texture Image Segmentation using FCM

Texture Image Segmentation using FCM Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) (2012) IACSIT Press, Singapore Texture Image Segmentation using FCM Kanchan S. Deshmukh + M.G.M

More information

Slant Correction using Histograms

Slant Correction using Histograms Slant Correction using Histograms Frank de Zeeuw Bachelor s Thesis in Artificial Intelligence Supervised by Axel Brink & Tijn van der Zant July 12, 2006 Abstract Slant is one of the characteristics that

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

Detecting Salient Contours Using Orientation Energy Distribution. Part I: Thresholding Based on. Response Distribution

Detecting Salient Contours Using Orientation Energy Distribution. Part I: Thresholding Based on. Response Distribution Detecting Salient Contours Using Orientation Energy Distribution The Problem: How Does the Visual System Detect Salient Contours? CPSC 636 Slide12, Spring 212 Yoonsuck Choe Co-work with S. Sarma and H.-C.

More information

Automatic thresholding for defect detection

Automatic thresholding for defect detection Pattern Recognition Letters xxx (2006) xxx xxx www.elsevier.com/locate/patrec Automatic thresholding for defect detection Hui-Fuang Ng * Department of Computer Science and Information Engineering, Asia

More information

A Comparative Study of the Application of Swarm Intelligence in Kruppa-Based Camera Auto- Calibration

A Comparative Study of the Application of Swarm Intelligence in Kruppa-Based Camera Auto- Calibration ISSN 2229-5518 56 A Comparative Study of the Application of Swarm Intelligence in Kruppa-Based Camera Auto- Calibration Ahmad Fariz Hasan, Ali Abuassal, Mutaz Khairalla, Amar Faiz Zainal Abidin, Mohd Fairus

More information

FITTING PIECEWISE LINEAR FUNCTIONS USING PARTICLE SWARM OPTIMIZATION

FITTING PIECEWISE LINEAR FUNCTIONS USING PARTICLE SWARM OPTIMIZATION Suranaree J. Sci. Technol. Vol. 19 No. 4; October - December 2012 259 FITTING PIECEWISE LINEAR FUNCTIONS USING PARTICLE SWARM OPTIMIZATION Pavee Siriruk * Received: February 28, 2013; Revised: March 12,

More information

Classification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University

Classification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Classification Vladimir Curic Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Outline An overview on classification Basics of classification How to choose appropriate

More information

Webpage: Volume 3, Issue V, May 2015 eissn:

Webpage:   Volume 3, Issue V, May 2015 eissn: Morphological Image Processing of MRI Brain Tumor Images Using MATLAB Sarla Yadav 1, Parul Yadav 2 and Dinesh K. Atal 3 Department of Biomedical Engineering Deenbandhu Chhotu Ram University of Science

More information

Enhanced Bleed Through Removal Using Normalized Picture Information Based Measures Sahil Mahaldar, Serene Banerjee

Enhanced Bleed Through Removal Using Normalized Picture Information Based Measures Sahil Mahaldar, Serene Banerjee Enhanced Bleed Through Removal Using Normalized Picture Information Based Measures Sahil Mahaldar, Serene Banerjee HP Laboratories HPL-2009-159 Keyword(s): bleed through, document cleanup Abstract: Back-to-front

More information

A Quantitative Approach for Textural Image Segmentation with Median Filter

A Quantitative Approach for Textural Image Segmentation with Median Filter International Journal of Advancements in Research & Technology, Volume 2, Issue 4, April-2013 1 179 A Quantitative Approach for Textural Image Segmentation with Median Filter Dr. D. Pugazhenthi 1, Priya

More information

A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data

A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data Available online at www.sciencedirect.com Procedia Technology 4 (2012 ) 802 806 C3IT-2012 A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data Sashikala Mishra a, Kailash Shaw

More information

ARTICLE IN PRESS. Engineering Applications of Artificial Intelligence

ARTICLE IN PRESS. Engineering Applications of Artificial Intelligence Engineering Applications of Artificial Intelligence 23 (2010) 676 688 Contents lists available at ScienceDirect Engineering Applications of Artificial Intelligence journal homepage: www.elsevier.com/locate/engappai

More information

Improving Generalization of Radial Basis Function Network with Adaptive Multi-Objective Particle Swarm Optimization

Improving Generalization of Radial Basis Function Network with Adaptive Multi-Objective Particle Swarm Optimization Proceedings of the 009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 009 Improving Generalization of Radial Basis Function Network with Adaptive Multi-Obective

More information

Research Article A LITERATURE REVIEW ON HEURISTIC ALGORITHMS IN IMAGE SEGMENTATION APPLICATIONS T.Abimala 1, S.

Research Article  A LITERATURE REVIEW ON HEURISTIC ALGORITHMS IN IMAGE SEGMENTATION APPLICATIONS T.Abimala 1, S. ISSN: 0975-766X CODEN: IJPTFI Available Online through Research Article www.ijptonline.com A LITERATURE REVIEW ON HEURISTIC ALGORITHMS IN IMAGE SEGMENTATION APPLICATIONS T.Abimala 1, S.Gayathri 2 1 PG

More information

Structural Analysis of Aerial Photographs (HB47 Computer Vision: Assignment)

Structural Analysis of Aerial Photographs (HB47 Computer Vision: Assignment) Structural Analysis of Aerial Photographs (HB47 Computer Vision: Assignment) Xiaodong Lu, Jin Yu, Yajie Li Master in Artificial Intelligence May 2004 Table of Contents 1 Introduction... 1 2 Edge-Preserving

More information

ARTICLE; BIOINFORMATICS Clustering performance comparison using K-means and expectation maximization algorithms

ARTICLE; BIOINFORMATICS Clustering performance comparison using K-means and expectation maximization algorithms Biotechnology & Biotechnological Equipment, 2014 Vol. 28, No. S1, S44 S48, http://dx.doi.org/10.1080/13102818.2014.949045 ARTICLE; BIOINFORMATICS Clustering performance comparison using K-means and expectation

More information

A *69>H>N6 #DJGC6A DG C<>C::G>C<,8>:C8:H /DA 'D 2:6G, ()-"&"3 -"(' ( +-" " " % '.+ % ' -0(+$,

A *69>H>N6 #DJGC6A DG C<>C::G>C<,8>:C8:H /DA 'D 2:6G, ()-&3 -(' ( +-   % '.+ % ' -0(+$, The structure is a very important aspect in neural network design, it is not only impossible to determine an optimal structure for a given problem, it is even impossible to prove that a given structure

More information

Operators-Based on Second Derivative double derivative Laplacian operator Laplacian Operator Laplacian Of Gaussian (LOG) Operator LOG

Operators-Based on Second Derivative double derivative Laplacian operator Laplacian Operator Laplacian Of Gaussian (LOG) Operator LOG Operators-Based on Second Derivative The principle of edge detection based on double derivative is to detect only those points as edge points which possess local maxima in the gradient values. Laplacian

More information

A New iterative triclass thresholding technique for Image Segmentation

A New iterative triclass thresholding technique for Image Segmentation A New iterative triclass thresholding technique for Image Segmentation M.M.Raghavendra Asst Prof, Department of ECE Brindavan Institute of Technology & Science Kurnool, India E-mail: mmraghavendraece@gmail.com

More information

Automatic differentiation based for particle swarm optimization steepest descent direction

Automatic differentiation based for particle swarm optimization steepest descent direction International Journal of Advances in Intelligent Informatics ISSN: 2442-6571 Vol 1, No 2, July 2015, pp. 90-97 90 Automatic differentiation based for particle swarm optimization steepest descent direction

More information

Object Shape Recognition in Image for Machine Vision Application

Object Shape Recognition in Image for Machine Vision Application Object Shape Recognition in Image for Machine Vision Application Mohd Firdaus Zakaria, Hoo Seng Choon, and Shahrel Azmin Suandi Abstract Vision is the most advanced of our senses, so it is not surprising

More information