AUTOMATED VIDEO INDEXING AND VIDEO SEARCH IN LARGE LECTURE VIDEO ARCHIVES USING HADOOP FRAMEWORK

Size: px
Start display at page:

Download "AUTOMATED VIDEO INDEXING AND VIDEO SEARCH IN LARGE LECTURE VIDEO ARCHIVES USING HADOOP FRAMEWORK"

Transcription

1 AUTOMATED VIDEO INDEXING AND VIDEO SEARCH IN LARGE LECTURE VIDEO ARCHIVES USING HADOOP FRAMEWORK P. Satya Shekar Varma 1,Prof.K.VenkateshwarRao 2,A.SaiPhanindra 3,G. Ritin Surya Sainadh 4 1,3,4 Department of Computer Science & Engineering MGIT, Hyderabad, Telangana, India. 2 Department of Computer Science & Engineering JNTU, Hyderabad, Telangana, India. ABSTRACT: Still it is a difficult problem to index video archives for future use. Generally, lecture videos contain slides and explanation of them is done. The text displayed in those slides are the main part of video content. Therefore, video text can be used for indexing the videos in a large video data archives. In this paper, we propose a method for automated lecture video indexing and video content browsing and video search. Keywords: Lecture Video Archives, Video Segmentation, Stroke Width Transform, Connected Components [1] INTRODUCTION The development of recording technology, compression of video and network speed, audio-video recordings are used very frequently in e-lecturing systems. Which led to tremendous increase in multimedia data on the web. It is very tedious task to find desired or required videos within a video archive without any search function. The video user would require information being a small part of the entire video without viewing the whole video archive. So an internal tag based technique is needed. And the process of tagging and assigning for huge number of videos requires resources and high computational power. So a unique system cannot lead to a feasible solution. Therefore, a distributed approach is taken, wherein number of system are used as nodes in Hadoop cluster-setup, thereby required computational power is provided for the process to run. Why not General CBVR? In general, Content Based Video Retrieval (CBVR) works by parsing a video, its Abstraction, Content analysis etc. Which are not very effective in case of huge lecture video archives, and it is assumed that lecture videos are consisting of slides text. Hence a specialized video segmentation techniq0ue to get individual slides is applied. Big Data: It is a broad term for data sets which are large that traditional data processing applications fail to handle. Data sets grow in size in part because they are being gathered by many application resources. There are mainly three key differences in big data. They are: Volume: The exponential growth in data storage is the volume. Now-a- days Terabytes and Petabytes of the storage system for enterprises is very common. 33

2 Velocity: The data generation is so fast in many applications. The data movement or the flow of data is massive and continuous. Variety: We can find data in the format of videos, music and large images on our social media channels. In the real world we have data in many different formats and that is the challenge we need to overcome with the Big Data. There is much advancement in big data analysis which offers cost-effective opportunities to improve critical development and also decision-making in areas like productivity, security, health care, crime and natural disaster. Hadoop: It is designed to efficiently distribute large amounts of data across a group of machines and well also to efficiently process them to work in parallel. It saves a lot of time and money. It uses a large-scale distributed batch processing infrastructure and an open source programming framework. It also facilitates to continue operating without interruption in case of a node failure also. Hadoop common: Hadoop Common refers to the collection of common utilities and libraries that are supported to Hadoop modules. It assumes that hardware failures are common and that these should be automatically handled in software by the Hadoop Framework. HDFS: Hadoop Distributed File System (HDFS) is a file system uses all the nodes in a Hadoop cluster together for data storage. It also stores as replication of data which may be recovered at the time of node failure. YARN: Yet Another Resource Negotiator (YARN) assigns and also manages CPU, memory and storage to applications running on a Hadoop cluster. The initial stage of Hadoop could only run MapReduce applications but YARN enables other application frameworks to run on Hadoop. MapReduce: MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. A MapReduce program is consists of a Map () phase that performs filtering and sorting and a Reduce () phase that performs a summarization of operations. It provides redundancy and fault tolerance. Big Video Processing: Basically, the lecture videos archives contain several hours of lectures recorded. So to process these huge datasets Hadoop framework is adopted. Hadoop breaks the bit stream chunks in parallel manner and produces a sequence file. It stores huge files in a distributed file system called Hadoop Distributed File System (HDFS) as small chunks of certain block size across a cluster of commodity hardware. In order to process such huge video datasets, we are using the Hadoop Map Reduce framework. [2] METHODOLOGY If there is a huge dataset there is a problem of storing and processing the data and Hadoop is the best solution for these ever. The metadata extracted is the crucial way to find a particular video matching in a large scale database of lecture videos. Initially, from the visual screen we detect the slide transitions and extract each unique slide frame with its temporal scope considered as the video segment. A. Segmentation of video into slides and Recognition of keywords 34

3 Here in this phase we perform segmentation of video into representative key frames. Some frames are selected as key frames which can provide a visual guideline for retrieving of the required lecture video. After this stage the key frames are subjected to OCR. As the basic element for differencing analysis, instead of pixel we used Connected Components (CCs). We can also have called it as component-level-differencing metric. The salt and pepper noise can be rejected from the differencing process and robustness is also increased, in this way we can also control the valid size of the CC. Finally, the unique slides with total content are recognized and captured as video segments. In the first step of segmentation, the entire slide video is analyzed. Change between adjacent frames is observed, by taking both accuracy and efficiency into account we had established an interval of three seconds. For the adjacent observed frames, we create canny edge maps and make pixel differential image from those canny edge maps. Eventually, the Connected Components (CCs) analysis is performed on this differential image and Connected Components (CCs) number is used as a threshold value for segmentation. In the second step of segmentation, the real slide transitions will be captured. First, the title region and content region of slide frame is defined. The content distribution of commonly used slide styles is established by analysis of large-scale lecture videos. This is the process of segmentation of video into slides. Figure: 1. Flow of Video Segmentation In retrieval task the important thing we took is text given in the slides of lecture video, because they are more closely related to the content explained in the video. A new verification scheme is used for detecting the text. To quickly localize candidate text regions an edge-based multi-scale text detector is used with a low rejection rate. To reject false positives that exposes low edge density and to split text and non-text regions into separate blocks an image entropybased adaptive refinement algorithm is used [3]. Finally, to remove the non-text area blocks Stroke Width Transform (SWT) is applied. The SWT verifier fails to identify special non-text patterns such as garden fence, sphere etc. To ameliorate the detection accuracy, we had adopted an additional SVM classifier to remove these non-text areas which SWT cannot remove. For recognition of text regions, we had developed a binarization approach, so that for identification of text pixels we use canny edge maps which are generated previously. This approach mainly contains three steps: seed pixel selection, text gradient direction analysis and seed-region growing. After the end of this process, the video text images are converted into a compatible format for standard OCR. To find out the incorrect words from the results we are also having spell-checking process. B. Structural Analysis of slides Initially, identification of title line procedure is done. If a text line is in the upper third part of the frame and has more than three characters, then it is considered as title line. Based 35

4 on the height and average stroke width of text line all non-title lines are divided into three types they are: key point, content text and foot line. [7] key-point if st > smean ^ ht > hmean footline if st < smean ^ ht < hmean ^ y = ymax content text otherwise; Where smean and hmean denote the average stroke width and the average text line height of a slide frame, and ymax denotes the maximum vertical position of a text line object. Then spell check is applied. C. Browsing Video Content with segmented key frames Within the video player we can find out the segments of video, this is introduced to have a fast navigation. A slide gallery is also provided below the video player, so that if user need to study the slide content clearly he/she can go through it. On clicking the time line units the video navigation is done better. D. Keyword Extraction and Video Search Hence the metadata can be generated by output of OCR. Many times, the recognized words may be irrelevant to the content or errors. So, from the raw results of recognized words we extract some keywords. Finally, those extracted key words are summed up and are useful to retrieve the video from a large lecture video archives. Majority of extracted key words are nouns and spelling errors can also be removed by applying OCR. Because a dictionary based filtering process is done. [3] RESULTS AND DISCUSSIONS Input Data: A presentation video of a power point presentation using generate video feature of Ms power point. Output: Metadata is generated for the videos and stored in a database. Video Segmentation: Following are the slides detected by video segmentation. Figure: 2.: Slides detected by video segmentation. Separating non-text areas: Input: The input for this step is all the key frames extracted from the previous step. These key frames are all the distinct slides present in the video. Output: We apply Stroke Width Transform to all these key frames. The result of this is that all the non-text areas like images, logos, etc. are eliminated from the frames and the text areas are. 36

5 Figure: 3. An extracted Key Frame. Figure: 4. Text areas of the Key Frame. Recognizing words and sentences: Input: After separating all the non-text areas from the text areas in the key frames, the resultant frames are inputted in this step. Output: Using Connected Components and the distance between individual letters, all the words are distinguished from each other and a box is drawn around each word. Following slide contains recognised words and sentences. Figure: 5. Slide contains recognized words and sentences. Generating attributes of words detected: Input: All the words recognized in the previous step are given as input in this step along with the attributes of the slides they are present to process them Output: OCR is applied on each of the word recognized to eliminate errors in words. Then attributes of each word such as position, slide number, etc. are found. Valid words filtered from Dictionary after OCR Detection. 37

6 Figure: 6. Valid words filtered from Dictionary after OCR Detection. Assigning weights to words: Input: The attributes of words detected. Output: The weights assigned to each word based on their attributes. Average heights of text (font height) for each slide Figure: 7. Average heights of text (font height) for each slide. [4] CONCLUSION Using this method videos can be tagged automatically based on content they have in their slides. This paper provides an easy way to navigate the exacted video where our query is present and also go to the exact location in terms on time elapsed where the search query is first encountered. ACKNOWLEDGEMENT The authors would like to thank D. Vijay Kumar for his invaluable guidance which led to improvise the quality of this paper. REFERENCES [1] M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12,pp , Dec [2] Ikeda, Osamu. "Segmentation of faces in video footage using HSV color for face detection and image retrieval." Image Processing, ICIP Proceedings. 2003International Conference on. Vol. 3. IEEE, [3] Doulamis, A., and N. Doulamis. "Optimal content-based video decomposition for interactive video navigation." Circuits and Systems for Video Technology, IEEETransactions on 14.6 (2004): [4] Padmakala, S., G. S. AnandhaMala, and M. Shalini. "An effective content based video retrieval utilizing texture, color and optimal key frame features."image Information Processing (ICIIP), 2011 International Conference on. IEEE,

7 [5] H. Yang, B. Quehl, and H. Sack. (2012), A framework for improved video text detection and recognition, Multimedia Tools Appl., pp. 1 29, [Online]. Available: [6] Chaisorn, Lekha, Corey Manders, and SusantoRahardja. "Video retrieval-evolution of video segmentation, indexing and search." Computer Science and Information Technology, ICCSIT nd IEEE International Conference on. IEEE, [7] Haojin Yang and Christoph Meinel, Content Based Lecture Video Retrieval Using Speech and Video Text Information, IEEE Transactions on Learning Technologies, Vol. 7, No.2, April-June, [8] H. J. Jeong, T.-E. Kim, and M. H. Kim.(2012), An accurate lecture video segmentation method by using sift and adaptive threshold, in Proc. 10th Int. Conf. Advances Mobile Comput., pp [Online].Available: [9] H. Sack and J. Waitelonis, Integrating social tagging and document annotation for contentbased search in multimedia data, in Proc. 1st Semantic Authoring Annotation Workshop, [10] G.Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, Inf. Process. Manage., vol. 24, no. 5, pp , [11] B. Epshtein, E. Ofek, and Y. Wexler, Detecting text in natural scenes with stroke width transform, in Proc. Int. Conf. Computation. Vis. Pattern Recognition, San Francisco, CA, June [12] F. Chang, C.-J. Chen, and C.-J. Lu, A linear-time componentlabeling algorithm using contour tracing technique, Comput. Vis.Image Understanding, vol. 93, no. 2, pp ,Jan [13] B. T. N. Dala, Histograms of oriented gradients for human detection, in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2005,vol. 1, pp [14] Ground truthdata.(2013).[online]. Available: [15] C. Meinel, F. Moritz, and M. Siebert, Community tagging in tele-teaching environments, in Proc. 2nd Int. Conf. e-educ., e-bus.,e-manage. and E-Learn., [16] S. Repp, A. Gross, and C. Meinel, Browsing within lecture videos based on the chain index of speech transcription, IEEE Trans.Learn. Technol., vol. 1, no. 3, pp , Jul

AUTOMATED EXTRACTION OF LECTURE OUTLINES FROM LECTURE VIDEOS A Hybrid Solution for Lecture Video Indexing

AUTOMATED EXTRACTION OF LECTURE OUTLINES FROM LECTURE VIDEOS A Hybrid Solution for Lecture Video Indexing AUTOMATED EXTRACTION OF LECTURE OUTLINES FROM LECTURE VIDEOS A Hybrid Solution for Lecture Video Indexing Haojin Yang, Franka Gruenewald and Christoph Meinel Hasso Plattner Institute (HPI), University

More information

Lecture Video Indexing and Analysis Using Video OCR Technology

Lecture Video Indexing and Analysis Using Video OCR Technology Lecture Video Indexing and Analysis Using Video OCR Technology Haojin Yang, Harald Sack, Christoph Meinel Hasso Plattner Institute (HPI), University of Potsdam P.O. Box 900460 D-14440 Potsdam {haojin.yang,

More information

Scene Text Detection Using Machine Learning Classifiers

Scene Text Detection Using Machine Learning Classifiers 601 Scene Text Detection Using Machine Learning Classifiers Nafla C.N. 1, Sneha K. 2, Divya K.P. 3 1 (Department of CSE, RCET, Akkikkvu, Thrissur) 2 (Department of CSE, RCET, Akkikkvu, Thrissur) 3 (Department

More information

Lecture Video Indexing and Retrieval Using Topic Keywords

Lecture Video Indexing and Retrieval Using Topic Keywords Lecture Video Indexing and Retrieval Using Topic Keywords B. J. Sandesh, Saurabha Jirgi, S. Vidya, Prakash Eljer, Gowri Srinivasa International Science Index, Computer and Information Engineering waset.org/publication/10007915

More information

142 IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, VOL. 7, NO. 2, APRIL-JUNE 2014

142 IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, VOL. 7, NO. 2, APRIL-JUNE 2014 142 IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, VOL. 7, NO. 2, APRIL-JUNE 2014 Content Based Lecture Video Retrieval Using Speech and Video Text Information Haojin Yang and Christoph Meinel, Member, IEEE

More information

Elimination of Duplicate Videos in Video Sharing Sites

Elimination of Duplicate Videos in Video Sharing Sites Elimination of Duplicate Videos in Video Sharing Sites Narendra Kumar S, Murugan S, Krishnaveni R Abstract - In some social video networking sites such as YouTube, there exists large numbers of duplicate

More information

Text Extraction from Natural Scene Images and Conversion to Audio in Smart Phone Applications

Text Extraction from Natural Scene Images and Conversion to Audio in Smart Phone Applications Text Extraction from Natural Scene Images and Conversion to Audio in Smart Phone Applications M. Prabaharan 1, K. Radha 2 M.E Student, Department of Computer Science and Engineering, Muthayammal Engineering

More information

Searching Video Collections:Part I

Searching Video Collections:Part I Searching Video Collections:Part I Introduction to Multimedia Information Retrieval Multimedia Representation Visual Features (Still Images and Image Sequences) Color Texture Shape Edges Objects, Motion

More information

ABSTRACT 1. INTRODUCTION 2. RELATED WORK

ABSTRACT 1. INTRODUCTION 2. RELATED WORK Improving text recognition by distinguishing scene and overlay text Bernhard Quehl, Haojin Yang, Harald Sack Hasso Plattner Institute, Potsdam, Germany Email: {bernhard.quehl, haojin.yang, harald.sack}@hpi.de

More information

Holistic Approach for Multimodal Lecture Video Retrieval

Holistic Approach for Multimodal Lecture Video Retrieval Holistic Approach for Multimodal Lecture Video Retrieval Dipali Patil Department of Computer Engineering D.Y. Patil College of Engineering, Akurdi Savitribai Phule Pune University, Pune, India Mrs. M.

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

MATRIX BASED INDEXING TECHNIQUE FOR VIDEO DATA

MATRIX BASED INDEXING TECHNIQUE FOR VIDEO DATA Journal of Computer Science, 9 (5): 534-542, 2013 ISSN 1549-3636 2013 doi:10.3844/jcssp.2013.534.542 Published Online 9 (5) 2013 (http://www.thescipub.com/jcs.toc) MATRIX BASED INDEXING TECHNIQUE FOR VIDEO

More information

Available online at ScienceDirect. Procedia Computer Science 96 (2016 )

Available online at   ScienceDirect. Procedia Computer Science 96 (2016 ) Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 96 (2016 ) 1409 1417 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems,

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

Content Based Image Retrieval: Survey and Comparison between RGB and HSV model

Content Based Image Retrieval: Survey and Comparison between RGB and HSV model Content Based Image Retrieval: Survey and Comparison between RGB and HSV model Simardeep Kaur 1 and Dr. Vijay Kumar Banga 2 AMRITSAR COLLEGE OF ENGG & TECHNOLOGY, Amritsar, India Abstract Content based

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

Enhanced Image. Improved Dam point Labelling

Enhanced Image. Improved Dam point Labelling 3rd International Conference on Multimedia Technology(ICMT 2013) Video Text Extraction Based on Stroke Width and Color Xiaodong Huang, 1 Qin Wang, Kehua Liu, Lishang Zhu Abstract. Video text can be used

More information

A Novel Image Retrieval Method Using Segmentation and Color Moments

A Novel Image Retrieval Method Using Segmentation and Color Moments A Novel Image Retrieval Method Using Segmentation and Color Moments T.V. Saikrishna 1, Dr.A.Yesubabu 2, Dr.A.Anandarao 3, T.Sudha Rani 4 1 Assoc. Professor, Computer Science Department, QIS College of

More information

A Robust Wipe Detection Algorithm

A Robust Wipe Detection Algorithm A Robust Wipe Detection Algorithm C. W. Ngo, T. C. Pong & R. T. Chin Department of Computer Science The Hong Kong University of Science & Technology Clear Water Bay, Kowloon, Hong Kong Email: fcwngo, tcpong,

More information

An Overview of Projection, Partitioning and Segmentation of Big Data Using Hp Vertica

An Overview of Projection, Partitioning and Segmentation of Big Data Using Hp Vertica IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 19, Issue 5, Ver. I (Sep.- Oct. 2017), PP 48-53 www.iosrjournals.org An Overview of Projection, Partitioning

More information

Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features

Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features Content Based Image Retrieval Using Color Quantizes, EDBTC and LBP Features 1 Kum Sharanamma, 2 Krishnapriya Sharma 1,2 SIR MVIT Abstract- To describe the image features the Local binary pattern (LBP)

More information

International Journal of Electrical, Electronics ISSN No. (Online): and Computer Engineering 3(2): 85-90(2014)

International Journal of Electrical, Electronics ISSN No. (Online): and Computer Engineering 3(2): 85-90(2014) I J E E E C International Journal of Electrical, Electronics ISSN No. (Online): 2277-2626 Computer Engineering 3(2): 85-90(2014) Robust Approach to Recognize Localize Text from Natural Scene Images Khushbu

More information

Revealing the Modern History of Japanese Philosophy Using Digitization, Natural Language Processing, and Visualization

Revealing the Modern History of Japanese Philosophy Using Digitization, Natural Language Processing, and Visualization Revealing the Modern History of Japanese Philosophy Using Digitization, Natural Language Katsuya Masuda *, Makoto Tanji **, and Hideki Mima *** Abstract This study proposes a framework to access to the

More information

Finger Print Enhancement Using Minutiae Based Algorithm

Finger Print Enhancement Using Minutiae Based Algorithm Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 8, August 2014,

More information

Image retrieval based on bag of images

Image retrieval based on bag of images University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2009 Image retrieval based on bag of images Jun Zhang University of Wollongong

More information

An Enhanced Image Retrieval Using K-Mean Clustering Algorithm in Integrating Text and Visual Features

An Enhanced Image Retrieval Using K-Mean Clustering Algorithm in Integrating Text and Visual Features An Enhanced Image Retrieval Using K-Mean Clustering Algorithm in Integrating Text and Visual Features S.Najimun Nisha 1, Mrs.K.A.Mehar Ban 2, 1 PG Student, SVCET, Puliangudi. najimunnisha@yahoo.com 2 AP/CSE,

More information

I. INTRODUCTION. Figure-1 Basic block of text analysis

I. INTRODUCTION. Figure-1 Basic block of text analysis ISSN: 2349-7637 (Online) (RHIMRJ) Research Paper Available online at: www.rhimrj.com Detection and Localization of Texts from Natural Scene Images: A Hybrid Approach Priyanka Muchhadiya Post Graduate Fellow,

More information

An Efficient Character Segmentation Based on VNP Algorithm

An Efficient Character Segmentation Based on VNP Algorithm Research Journal of Applied Sciences, Engineering and Technology 4(24): 5438-5442, 2012 ISSN: 2040-7467 Maxwell Scientific organization, 2012 Submitted: March 18, 2012 Accepted: April 14, 2012 Published:

More information

Segmentation Framework for Multi-Oriented Text Detection and Recognition

Segmentation Framework for Multi-Oriented Text Detection and Recognition Segmentation Framework for Multi-Oriented Text Detection and Recognition Shashi Kant, Sini Shibu Department of Computer Science and Engineering, NRI-IIST, Bhopal Abstract - Here in this paper a new and

More information

Content Based Video Retrieval Using Integrated Feature Extraction and Personalization of Results

Content Based Video Retrieval Using Integrated Feature Extraction and Personalization of Results International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume, Issue 08 (August 205), PP.72-80 Content Based Retrieval Using Integrated Feature

More information

Image Retrieval System Based on Sketch

Image Retrieval System Based on Sketch Image Retrieval System Based on Sketch Author 1 Mrs. Asmita A. Desai Assistant Professor,Department of Electronics Engineering, Author 2 Prof. Dr. A. N. Jadhav HOD,Department of Electronics Engineering,

More information

Mobile Camera Based Text Detection and Translation

Mobile Camera Based Text Detection and Translation Mobile Camera Based Text Detection and Translation Derek Ma Qiuhau Lin Tong Zhang Department of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Mechanical Engineering Email: derekxm@stanford.edu

More information

AUTOMATIC VIDEO INDEXING

AUTOMATIC VIDEO INDEXING AUTOMATIC VIDEO INDEXING Itxaso Bustos Maite Frutos TABLE OF CONTENTS Introduction Methods Key-frame extraction Automatic visual indexing Shot boundary detection Video OCR Index in motion Image processing

More information

AUTOMATIC VISUAL CONCEPT DETECTION IN VIDEOS

AUTOMATIC VISUAL CONCEPT DETECTION IN VIDEOS AUTOMATIC VISUAL CONCEPT DETECTION IN VIDEOS Nilam B. Lonkar 1, Dinesh B. Hanchate 2 Student of Computer Engineering, Pune University VPKBIET, Baramati, India Computer Engineering, Pune University VPKBIET,

More information

Mining User - Aware Rare Sequential Topic Pattern in Document Streams

Mining User - Aware Rare Sequential Topic Pattern in Document Streams Mining User - Aware Rare Sequential Topic Pattern in Document Streams A.Mary Assistant Professor, Department of Computer Science And Engineering Alpha College Of Engineering, Thirumazhisai, Tamil Nadu,

More information

Efficient Indexing and Searching Framework for Unstructured Data

Efficient Indexing and Searching Framework for Unstructured Data Efficient Indexing and Searching Framework for Unstructured Data Kyar Nyo Aye, Ni Lar Thein University of Computer Studies, Yangon kyarnyoaye@gmail.com, nilarthein@gmail.com ABSTRACT The proliferation

More information

12/12 A Chinese Words Detection Method in Camera Based Images Qingmin Chen, Yi Zhou, Kai Chen, Li Song, Xiaokang Yang Institute of Image Communication

12/12 A Chinese Words Detection Method in Camera Based Images Qingmin Chen, Yi Zhou, Kai Chen, Li Song, Xiaokang Yang Institute of Image Communication A Chinese Words Detection Method in Camera Based Images Qingmin Chen, Yi Zhou, Kai Chen, Li Song, Xiaokang Yang Institute of Image Communication and Information Processing, Shanghai Key Laboratory Shanghai

More information

Image-Based Competitive Printed Circuit Board Analysis

Image-Based Competitive Printed Circuit Board Analysis Image-Based Competitive Printed Circuit Board Analysis Simon Basilico Department of Electrical Engineering Stanford University Stanford, CA basilico@stanford.edu Ford Rylander Department of Electrical

More information

Distributed Face Recognition Using Hadoop

Distributed Face Recognition Using Hadoop Distributed Face Recognition Using Hadoop A. Thorat, V. Malhotra, S. Narvekar and A. Joshi Dept. of Computer Engineering and IT College of Engineering, Pune {abhishekthorat02@gmail.com, vinayak.malhotra20@gmail.com,

More information

University of Cambridge Engineering Part IIB Module 4F12 - Computer Vision and Robotics Mobile Computer Vision

University of Cambridge Engineering Part IIB Module 4F12 - Computer Vision and Robotics Mobile Computer Vision report University of Cambridge Engineering Part IIB Module 4F12 - Computer Vision and Robotics Mobile Computer Vision Web Server master database User Interface Images + labels image feature algorithm Extract

More information

TEVI: Text Extraction for Video Indexing

TEVI: Text Extraction for Video Indexing TEVI: Text Extraction for Video Indexing Hichem KARRAY, Mohamed SALAH, Adel M. ALIMI REGIM: Research Group on Intelligent Machines, EIS, University of Sfax, Tunisia hichem.karray@ieee.org mohamed_salah@laposte.net

More information

Text Information Extraction And Analysis From Images Using Digital Image Processing Techniques

Text Information Extraction And Analysis From Images Using Digital Image Processing Techniques Text Information Extraction And Analysis From Images Using Digital Image Processing Techniques Partha Sarathi Giri Department of Electronics and Communication, M.E.M.S, Balasore, Odisha Abstract Text data

More information

High Performance Computing on MapReduce Programming Framework

High Performance Computing on MapReduce Programming Framework International Journal of Private Cloud Computing Environment and Management Vol. 2, No. 1, (2015), pp. 27-32 http://dx.doi.org/10.21742/ijpccem.2015.2.1.04 High Performance Computing on MapReduce Programming

More information

Data Analysis Using MapReduce in Hadoop Environment

Data Analysis Using MapReduce in Hadoop Environment Data Analysis Using MapReduce in Hadoop Environment Muhammad Khairul Rijal Muhammad*, Saiful Adli Ismail, Mohd Nazri Kama, Othman Mohd Yusop, Azri Azmi Advanced Informatics School (UTM AIS), Universiti

More information

Time Stamp Detection and Recognition in Video Frames

Time Stamp Detection and Recognition in Video Frames Time Stamp Detection and Recognition in Video Frames Nongluk Covavisaruch and Chetsada Saengpanit Department of Computer Engineering, Chulalongkorn University, Bangkok 10330, Thailand E-mail: nongluk.c@chula.ac.th

More information

HCR Using K-Means Clustering Algorithm

HCR Using K-Means Clustering Algorithm HCR Using K-Means Clustering Algorithm Meha Mathur 1, Anil Saroliya 2 Amity School of Engineering & Technology Amity University Rajasthan, India Abstract: Hindi is a national language of India, there are

More information

Image Text Extraction and Recognition using Hybrid Approach of Region Based and Connected Component Methods

Image Text Extraction and Recognition using Hybrid Approach of Region Based and Connected Component Methods Image Text Extraction and Recognition using Hybrid Approach of Region Based and Connected Component Methods Ms. N. Geetha 1 Assistant Professor Department of Computer Applications Vellalar College for

More information

Information Retrieval

Information Retrieval Multimedia Computing: Algorithms, Systems, and Applications: Information Retrieval and Search Engine By Dr. Yu Cao Department of Computer Science The University of Massachusetts Lowell Lowell, MA 01854,

More information

Layout Segmentation of Scanned Newspaper Documents

Layout Segmentation of Scanned Newspaper Documents , pp-05-10 Layout Segmentation of Scanned Newspaper Documents A.Bandyopadhyay, A. Ganguly and U.Pal CVPR Unit, Indian Statistical Institute 203 B T Road, Kolkata, India. Abstract: Layout segmentation algorithms

More information

Available online at ScienceDirect. Procedia Computer Science 87 (2016 ) 12 17

Available online at  ScienceDirect. Procedia Computer Science 87 (2016 ) 12 17 Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 87 (2016 ) 12 17 4th International Conference on Recent Trends in Computer Science & Engineering Segment Based Indexing

More information

INTELLIGENT transportation systems have a significant

INTELLIGENT transportation systems have a significant INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 205, VOL. 6, NO. 4, PP. 35 356 Manuscript received October 4, 205; revised November, 205. DOI: 0.55/eletel-205-0046 Efficient Two-Step Approach for Automatic

More information

Introduction to Hadoop and MapReduce

Introduction to Hadoop and MapReduce Introduction to Hadoop and MapReduce Antonino Virgillito THE CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION Large-scale Computation Traditional solutions for computing large

More information

Efficient Algorithm for Frequent Itemset Generation in Big Data

Efficient Algorithm for Frequent Itemset Generation in Big Data Efficient Algorithm for Frequent Itemset Generation in Big Data Anbumalar Smilin V, Siddique Ibrahim S.P, Dr.M.Sivabalakrishnan P.G. Student, Department of Computer Science and Engineering, Kumaraguru

More information

OTCYMIST: Otsu-Canny Minimal Spanning Tree for Born-Digital Images

OTCYMIST: Otsu-Canny Minimal Spanning Tree for Born-Digital Images OTCYMIST: Otsu-Canny Minimal Spanning Tree for Born-Digital Images Deepak Kumar and A G Ramakrishnan Medical Intelligence and Language Engineering Laboratory Department of Electrical Engineering, Indian

More information

Comparative Analysis of Range Aggregate Queries In Big Data Environment

Comparative Analysis of Range Aggregate Queries In Big Data Environment Comparative Analysis of Range Aggregate Queries In Big Data Environment Ranjanee S PG Scholar, Dept. of Computer Science and Engineering, Institute of Road and Transport Technology, Erode, TamilNadu, India.

More information

Cross Reference Strategies for Cooperative Modalities

Cross Reference Strategies for Cooperative Modalities Cross Reference Strategies for Cooperative Modalities D.SRIKAR*1 CH.S.V.V.S.N.MURTHY*2 Department of Computer Science and Engineering, Sri Sai Aditya institute of Science and Technology Department of Information

More information

Recognition of Gurmukhi Text from Sign Board Images Captured from Mobile Camera

Recognition of Gurmukhi Text from Sign Board Images Captured from Mobile Camera International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 17 (2014), pp. 1839-1845 International Research Publications House http://www. irphouse.com Recognition of

More information

A Review on Identifying the Main Content From Web Pages

A Review on Identifying the Main Content From Web Pages A Review on Identifying the Main Content From Web Pages Madhura R. Kaddu 1, Dr. R. B. Kulkarni 2 1, 2 Department of Computer Scienece and Engineering, Walchand Institute of Technology, Solapur University,

More information

Content-Based Real Time Video Copy Detection Using Hadoop

Content-Based Real Time Video Copy Detection Using Hadoop IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Volume 6, PP 70-74 www.iosrjen.org Content-Based Real Time Video Copy Detection Using Hadoop Pramodini Kamble 1, Priyanka

More information

Frequent Item Set using Apriori and Map Reduce algorithm: An Application in Inventory Management

Frequent Item Set using Apriori and Map Reduce algorithm: An Application in Inventory Management Frequent Item Set using Apriori and Map Reduce algorithm: An Application in Inventory Management Kranti Patil 1, Jayashree Fegade 2, Diksha Chiramade 3, Srujan Patil 4, Pradnya A. Vikhar 5 1,2,3,4,5 KCES

More information

Bus Detection and recognition for visually impaired people

Bus Detection and recognition for visually impaired people Bus Detection and recognition for visually impaired people Hangrong Pan, Chucai Yi, and Yingli Tian The City College of New York The Graduate Center The City University of New York MAP4VIP Outline Motivation

More information

Performance Analysis of Hadoop Application For Heterogeneous Systems

Performance Analysis of Hadoop Application For Heterogeneous Systems IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 3, Ver. I (May-Jun. 2016), PP 30-34 www.iosrjournals.org Performance Analysis of Hadoop Application

More information

Scene Text Recognition in Mobile Application using K-Mean Clustering and Support Vector Machine

Scene Text Recognition in Mobile Application using K-Mean Clustering and Support Vector Machine ISSN: 2278 1323 All Rights Reserved 2015 IJARCET 2492 Scene Text Recognition in Mobile Application using K-Mean Clustering and Support Vector Machine Priyanka N Guttedar, Pushpalata S Abstract In natural

More information

ADAPTIVE HANDLING OF 3V S OF BIG DATA TO IMPROVE EFFICIENCY USING HETEROGENEOUS CLUSTERS

ADAPTIVE HANDLING OF 3V S OF BIG DATA TO IMPROVE EFFICIENCY USING HETEROGENEOUS CLUSTERS INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 ADAPTIVE HANDLING OF 3V S OF BIG DATA TO IMPROVE EFFICIENCY USING HETEROGENEOUS CLUSTERS Radhakrishnan R 1, Karthik

More information

Text Localization and Extraction in Natural Scene Images

Text Localization and Extraction in Natural Scene Images Text Localization and Extraction in Natural Scene Images Miss Nikita her M.E. Student, MET BKC IOE, University of Pune, Nasik, India. e-mail: nikitaaher@gmail.com bstract Content based image analysis methods

More information

Top 25 Big Data Interview Questions And Answers

Top 25 Big Data Interview Questions And Answers Top 25 Big Data Interview Questions And Answers By: Neeru Jain - Big Data The era of big data has just begun. With more companies inclined towards big data to run their operations, the demand for talent

More information

Databases 2 (VU) ( / )

Databases 2 (VU) ( / ) Databases 2 (VU) (706.711 / 707.030) MapReduce (Part 3) Mark Kröll ISDS, TU Graz Nov. 27, 2017 Mark Kröll (ISDS, TU Graz) MapReduce Nov. 27, 2017 1 / 42 Outline 1 Problems Suited for Map-Reduce 2 MapReduce:

More information

Fast and Effective System for Name Entity Recognition on Big Data

Fast and Effective System for Name Entity Recognition on Big Data International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-3, Issue-2 E-ISSN: 2347-2693 Fast and Effective System for Name Entity Recognition on Big Data Jigyasa Nigam

More information

HANDWRITTEN GURMUKHI CHARACTER RECOGNITION USING WAVELET TRANSFORMS

HANDWRITTEN GURMUKHI CHARACTER RECOGNITION USING WAVELET TRANSFORMS International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN 2249-684X Vol.2, Issue 3 Sep 2012 27-37 TJPRC Pvt. Ltd., HANDWRITTEN GURMUKHI

More information

Content based Image Retrievals for Brain Related Diseases

Content based Image Retrievals for Brain Related Diseases Content based Image Retrievals for Brain Related Diseases T.V. Madhusudhana Rao Department of CSE, T.P.I.S.T., Bobbili, Andhra Pradesh, INDIA S. Pallam Setty Department of CS&SE, Andhra University, Visakhapatnam,

More information

International Journal of Advance Research in Engineering, Science & Technology

International Journal of Advance Research in Engineering, Science & Technology Impact Factor (SJIF): 4.542 International Journal of Advance Research in Engineering, Science & Technology e-issn: 2393-9877, p-issn: 2394-2444 Volume 4, Issue 4, April-2017 A Simple Effective Algorithm

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

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

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) CONTEXT SENSITIVE TEXT SUMMARIZATION USING HIERARCHICAL CLUSTERING ALGORITHM

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) CONTEXT SENSITIVE TEXT SUMMARIZATION USING HIERARCHICAL CLUSTERING ALGORITHM INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & 6367(Print), ISSN 0976 6375(Online) Volume 3, Issue 1, January- June (2012), TECHNOLOGY (IJCET) IAEME ISSN 0976 6367(Print) ISSN 0976 6375(Online) Volume

More information

Video shot segmentation using late fusion technique

Video shot segmentation using late fusion technique Video shot segmentation using late fusion technique by C. Krishna Mohan, N. Dhananjaya, B.Yegnanarayana in Proc. Seventh International Conference on Machine Learning and Applications, 2008, San Diego,

More information

Text Extraction in Video

Text Extraction in Video International Journal of Computational Engineering Research Vol, 03 Issue, 5 Text Extraction in Video 1, Ankur Srivastava, 2, Dhananjay Kumar, 3, Om Prakash Gupta, 4, Amit Maurya, 5, Mr.sanjay kumar Srivastava

More information

Meta-Content framework for back index generation

Meta-Content framework for back index generation Meta-Content framework for back index generation Tripti Sharma, Assistant Professor Department of computer science Chhatrapati Shivaji Institute of Technology. Durg, India triptisharma@csitdurg.in Sarang

More information

LOG FILE ANALYSIS USING HADOOP AND ITS ECOSYSTEMS

LOG FILE ANALYSIS USING HADOOP AND ITS ECOSYSTEMS LOG FILE ANALYSIS USING HADOOP AND ITS ECOSYSTEMS Vandita Jain 1, Prof. Tripti Saxena 2, Dr. Vineet Richhariya 3 1 M.Tech(CSE)*,LNCT, Bhopal(M.P.)(India) 2 Prof. Dept. of CSE, LNCT, Bhopal(M.P.)(India)

More information

MATRIX BASED SEQUENTIAL INDEXING TECHNIQUE FOR VIDEO DATA MINING

MATRIX BASED SEQUENTIAL INDEXING TECHNIQUE FOR VIDEO DATA MINING MATRIX BASED SEQUENTIAL INDEXING TECHNIQUE FOR VIDEO DATA MINING 1 D.SARAVANAN 2 V.SOMASUNDARAM Assistant Professor, Faculty of Computing, Sathyabama University Chennai 600 119, Tamil Nadu, India Email

More information

Auto Secured Text Monitor in Natural Scene Images

Auto Secured Text Monitor in Natural Scene Images IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 4, Ver. V (Jul.-Aug. 2016), PP 148-152 www.iosrjournals.org Auto Secured Text Monitor in Natural Scene

More information

Hello, I am from the State University of Library Studies and Information Technologies, Bulgaria

Hello, I am from the State University of Library Studies and Information Technologies, Bulgaria Hello, My name is Svetla Boytcheva, I am from the State University of Library Studies and Information Technologies, Bulgaria I am goingto present you work in progress for a research project aiming development

More information

IDIAP IDIAP. Martigny ffl Valais ffl Suisse

IDIAP IDIAP. Martigny ffl Valais ffl Suisse R E S E A R C H R E P O R T IDIAP IDIAP Martigny - Valais - Suisse ASYMMETRIC FILTER FOR TEXT RECOGNITION IN VIDEO Datong Chen, Kim Shearer IDIAP Case Postale 592 Martigny Switzerland IDIAP RR 00-37 Nov.

More information

Consistent Line Clusters for Building Recognition in CBIR

Consistent Line Clusters for Building Recognition in CBIR Consistent Line Clusters for Building Recognition in CBIR Yi Li and Linda G. Shapiro Department of Computer Science and Engineering University of Washington Seattle, WA 98195-250 shapiro,yi @cs.washington.edu

More information

Topics. Big Data Analytics What is and Why Hadoop? Comparison to other technologies Hadoop architecture Hadoop ecosystem Hadoop usage examples

Topics. Big Data Analytics What is and Why Hadoop? Comparison to other technologies Hadoop architecture Hadoop ecosystem Hadoop usage examples Hadoop Introduction 1 Topics Big Data Analytics What is and Why Hadoop? Comparison to other technologies Hadoop architecture Hadoop ecosystem Hadoop usage examples 2 Big Data Analytics What is Big Data?

More information

Shrey Patel B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India

Shrey Patel B.E. Computer Engineering, Gujarat Technological University, Ahmedabad, Gujarat, India International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018 IJSRCSEIT Volume 3 Issue 3 ISSN : 2456-3307 Some Issues in Application of NLP to Intelligent

More information

A Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images

A Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images A Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images Karthik Ram K.V & Mahantesh K Department of Electronics and Communication Engineering, SJB Institute of Technology, Bangalore,

More information

Text Enhancement with Asymmetric Filter for Video OCR. Datong Chen, Kim Shearer and Hervé Bourlard

Text Enhancement with Asymmetric Filter for Video OCR. Datong Chen, Kim Shearer and Hervé Bourlard Text Enhancement with Asymmetric Filter for Video OCR Datong Chen, Kim Shearer and Hervé Bourlard Dalle Molle Institute for Perceptual Artificial Intelligence Rue du Simplon 4 1920 Martigny, Switzerland

More information

A brief history on Hadoop

A brief history on Hadoop Hadoop Basics A brief history on Hadoop 2003 - Google launches project Nutch to handle billions of searches and indexing millions of web pages. Oct 2003 - Google releases papers with GFS (Google File System)

More information

Improved MapReduce k-means Clustering Algorithm with Combiner

Improved MapReduce k-means Clustering Algorithm with Combiner 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation Improved MapReduce k-means Clustering Algorithm with Combiner Prajesh P Anchalia Department Of Computer Science and Engineering

More information

NeuroMem. A Neuromorphic Memory patented architecture. NeuroMem 1

NeuroMem. A Neuromorphic Memory patented architecture. NeuroMem 1 NeuroMem A Neuromorphic Memory patented architecture NeuroMem 1 Unique simple architecture NM bus A chain of identical neurons, no supervisor 1 neuron = memory + logic gates Context Category ted during

More information

AN ENHANCED ATTRIBUTE RERANKING DESIGN FOR WEB IMAGE SEARCH

AN ENHANCED ATTRIBUTE RERANKING DESIGN FOR WEB IMAGE SEARCH AN ENHANCED ATTRIBUTE RERANKING DESIGN FOR WEB IMAGE SEARCH Sai Tejaswi Dasari #1 and G K Kishore Babu *2 # Student,Cse, CIET, Lam,Guntur, India * Assistant Professort,Cse, CIET, Lam,Guntur, India Abstract-

More information

Obtaining Rough Set Approximation using MapReduce Technique in Data Mining

Obtaining Rough Set Approximation using MapReduce Technique in Data Mining Obtaining Rough Set Approximation using MapReduce Technique in Data Mining Varda Dhande 1, Dr. B. K. Sarkar 2 1 M.E II yr student, Dept of Computer Engg, P.V.P.I.T Collage of Engineering Pune, Maharashtra,

More information

Ontology Extraction from Heterogeneous Documents

Ontology Extraction from Heterogeneous Documents Vol.3, Issue.2, March-April. 2013 pp-985-989 ISSN: 2249-6645 Ontology Extraction from Heterogeneous Documents Kirankumar Kataraki, 1 Sumana M 2 1 IV sem M.Tech/ Department of Information Science & Engg

More information

XETA: extensible metadata System

XETA: extensible metadata System XETA: extensible metadata System Abstract: This paper presents an extensible metadata system (XETA System) which makes it possible for the user to organize and extend the structure of metadata. We discuss

More information

Recall precision graph

Recall precision graph VIDEO SHOT BOUNDARY DETECTION USING SINGULAR VALUE DECOMPOSITION Λ Z.»CERNEKOVÁ, C. KOTROPOULOS AND I. PITAS Aristotle University of Thessaloniki Box 451, Thessaloniki 541 24, GREECE E-mail: (zuzana, costas,

More information

6. Applications - Text recognition in videos - Semantic video analysis

6. Applications - Text recognition in videos - Semantic video analysis 6. Applications - Text recognition in videos - Semantic video analysis Stephan Kopf 1 Motivation Goal: Segmentation and classification of characters Only few significant features are visible in these simple

More information

DESIGNING AN INTEREST SEARCH MODEL USING THE KEYWORD FROM THE CLUSTERED DATASETS

DESIGNING AN INTEREST SEARCH MODEL USING THE KEYWORD FROM THE CLUSTERED DATASETS ISSN: 0976-3104 SPECIAL ISSUE: Emerging Technologies in Networking and Security (ETNS) Ajitha et al. ARTICLE OPEN ACCESS DESIGNING AN INTEREST SEARCH MODEL USING THE KEYWORD FROM THE CLUSTERED DATASETS

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

An Edge-Based Approach to Motion Detection*

An Edge-Based Approach to Motion Detection* An Edge-Based Approach to Motion Detection* Angel D. Sappa and Fadi Dornaika Computer Vison Center Edifici O Campus UAB 08193 Barcelona, Spain {sappa, dornaika}@cvc.uab.es Abstract. This paper presents

More information

A REVIEW PAPER ON BIG DATA ANALYTICS

A REVIEW PAPER ON BIG DATA ANALYTICS A REVIEW PAPER ON BIG DATA ANALYTICS Kirti Bhatia 1, Lalit 2 1 HOD, Department of Computer Science, SKITM Bahadurgarh Haryana, India bhatia.kirti.it@gmail.com 2 M Tech 4th sem SKITM Bahadurgarh, Haryana,

More information