Object Recognition Using Force Data Clustering And Hmm Based Shape Recognition

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Object Recognition Using Force Data Clustering and HMM Based Shape Recognition

Author : Masoumeh Kalantari Khandani
Publisher :
ISBN 13 :
Total Pages : 190 pages
Book Rating : 4.:/5 (897 download)

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Book Synopsis Object Recognition Using Force Data Clustering and HMM Based Shape Recognition by : Masoumeh Kalantari Khandani

Download or read book Object Recognition Using Force Data Clustering and HMM Based Shape Recognition written by Masoumeh Kalantari Khandani and published by . This book was released on 2010 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis the problem of detecting a known model object in a scene or database of images is addressed. We present two major components of a complete solution for this problem: a data clustering technique for image segmentation and feature extraction, and a shape recognition method. The presented novel data clustering method (Force) relies on the laws of electrostatic fields to find clusters of datapoints in a multiple-dimension space. Application of Force to image segmentation in gray level and color images is described in the thesis. We also show that Force can be successfully used for feature extraction from object images. We present a statistical shape matching method based on Hidden Markov Models (HMM) and then combine its recognition results with the recognition outcome of the Force based algorithm. We show improvement made when Force based features are added to the HMM based approach.

Toward Category-Level Object Recognition

Author : Jean Ponce
Publisher : Springer
ISBN 13 : 3540687955
Total Pages : 622 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Toward Category-Level Object Recognition by : Jean Ponce

Download or read book Toward Category-Level Object Recognition written by Jean Ponce and published by Springer. This book was released on 2007-01-25 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.

An Introduction to Object Recognition

Author : Marco Alexander Treiber
Publisher : Springer Science & Business Media
ISBN 13 : 1849962359
Total Pages : 210 pages
Book Rating : 4.8/5 (499 download)

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Book Synopsis An Introduction to Object Recognition by : Marco Alexander Treiber

Download or read book An Introduction to Object Recognition written by Marco Alexander Treiber and published by Springer Science & Business Media. This book was released on 2010-07-23 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.

Object Recognition

Author : M. Bennamoun
Publisher : Springer Science & Business Media
ISBN 13 : 1447137221
Total Pages : 352 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Object Recognition by : M. Bennamoun

Download or read book Object Recognition written by M. Bennamoun and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatie object recognition is a multidisciplinary research area using con cepts and tools from mathematics, computing, optics, psychology, pattern recognition, artificial intelligence and various other disciplines. The purpose of this research is to provide a set of coherent paradigms and algorithms for the purpose of designing systems that will ultimately emulate the functions performed by the Human Visual System (HVS). Hence, such systems should have the ability to recognise objects in two or three dimensions independently of their positions, orientations or scales in the image. The HVS is employed for tens of thousands of recognition events each day, ranging from navigation (through the recognition of landmarks or signs), right through to communication (through the recognition of characters or people themselves). Hence, the motivations behind the construction of recognition systems, which have the ability to function in the real world, is unquestionable and would serve industrial (e.g. quality control), military (e.g. automatie target recognition) and community needs (e.g. aiding the visually impaired). Scope, Content and Organisation of this Book This book provides a comprehensive, yet readable foundation to the field of object recognition from which research may be initiated or guided. It repre sents the culmination of research topics that I have either covered personally or in conjunction with my PhD students. These areas include image acqui sition, 3-D object reconstruction, object modelling, and the matching of ob jects, all of which are essential in the construction of an object recognition system.

Object Detection with Deep Learning Models

Author : S Poonkuntran
Publisher : CRC Press
ISBN 13 : 1000686795
Total Pages : 345 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Object Detection with Deep Learning Models by : S Poonkuntran

Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

Shape Based Object Detection and Recognition in Silhouettes and Real Images

Author : Xingwei Yang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (858 download)

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Book Synopsis Shape Based Object Detection and Recognition in Silhouettes and Real Images by : Xingwei Yang

Download or read book Shape Based Object Detection and Recognition in Silhouettes and Real Images written by Xingwei Yang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer and Information Science

Generic Object Recognition Using Form & Function

Author : Louise Stark
Publisher : World Scientific
ISBN 13 : 9789810215088
Total Pages : 162 pages
Book Rating : 4.2/5 (15 download)

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Book Synopsis Generic Object Recognition Using Form & Function by : Louise Stark

Download or read book Generic Object Recognition Using Form & Function written by Louise Stark and published by World Scientific. This book was released on 1996 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a detailed record of the ?GRUFF? research project. The goal of the GRUFF project is to develop techniques for robotic vision systems to recognize objects by reasoning about their intended function rather than matching to a pre-defined database of 2-D object appearances or 3-D object shapes. The contributions of this work are: a demonstration of the feasibility of the ?form and function? approach to reasoning about 3-D shapes; a demonstration of the concept of using a small number of knowledge primitives as component building blocks in creating a function-based definition of an object category; and an indexing mechanism to make processing for recognition more efficient without any substantial decrease in correctness of classification. Results are given for the analysis of over 500 3-D shape descriptions created with a solid modeling tool and over 200 shape descriptions extracted from real laser range finder images.

Advancement of Deep Learning and its Applications in Object Detection and Recognition

Author : Roohie Naaz Mir
Publisher : CRC Press
ISBN 13 : 1000880419
Total Pages : 319 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Advancement of Deep Learning and its Applications in Object Detection and Recognition by : Roohie Naaz Mir

Download or read book Advancement of Deep Learning and its Applications in Object Detection and Recognition written by Roohie Naaz Mir and published by CRC Press. This book was released on 2023-05-10 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.

An Introduction to Object Recognition

Author : Marco Alexander Treiber
Publisher : Springer
ISBN 13 : 9781849962360
Total Pages : 202 pages
Book Rating : 4.9/5 (623 download)

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Book Synopsis An Introduction to Object Recognition by : Marco Alexander Treiber

Download or read book An Introduction to Object Recognition written by Marco Alexander Treiber and published by Springer. This book was released on 2013-01-02 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm. This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum. Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters). Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses. Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.

Guiding Object Recognition

Author : Timothy M. Lebo
Publisher :
ISBN 13 :
Total Pages : 162 pages
Book Rating : 4.:/5 (618 download)

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Book Synopsis Guiding Object Recognition by : Timothy M. Lebo

Download or read book Guiding Object Recognition written by Timothy M. Lebo and published by . This book was released on 2005 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a components-based object detection and localization algorithm for static images as well as a detailed analysis of the model dynamics during the localization process.

Fast Learning and Invariant Object Recognition

Author : Branko Soucek
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 306 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Fast Learning and Invariant Object Recognition by : Branko Soucek

Download or read book Fast Learning and Invariant Object Recognition written by Branko Soucek and published by Wiley-Interscience. This book was released on 1992-05-07 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This applications-oriented book presents, for the first time, Learning-Generalization-Seeing-Recognition Hybrids. Numerous new learning algorithms are described, including holographic networks, adaptive decoupled momentum, feature construction, second-order gradient, and adaptive-symbolic methods. Object recognition systems in real-time applications are presented and include massively parallel and systolic array implementations. These systems exhibit up to 2 billion operations and over 300 billion connections per second. Position, scale and rotation invariant systems for industrial machine vision are presented, including testing of IC chips; flying object recognition; space shuttle and aircraft experiments; detection of moving objects; shape recognition in manufacturing; recognition of occluded objects; biomedical image classification; three-dimensional ultrasonic imaging in clinical ophthalmology, and others. New invariant object recognition paradigms include orthogonal sets of feature layers; higher-order neural networks; detection of movement-attention-tracking; landmark matching; segmentation of three-dimensional images; dynamic links on the reduced mesh of trees. Fast Learning and Invariant Object Recognition presents a unified treatment of material that has previously been scattered worldwide in a number of research reports, as well as previously unpublished methods and results from the IRIS (Integration of Reasoning, Informing and Serving) Group.

Computer Vision - ECCV 2000

Author : David Vernon
Publisher : Springer Science & Business Media
ISBN 13 : 3540676864
Total Pages : 881 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Computer Vision - ECCV 2000 by : David Vernon

Download or read book Computer Vision - ECCV 2000 written by David Vernon and published by Springer Science & Business Media. This book was released on 2000-06-19 with total page 881 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 1842/1843 constitutes the refereed proceedings of the 6th European Conference on Computer Vision, ECCV 2000, held in Dublin, Ireland in June/July 2000. The 116 revised full papers presented were carefully selected from a total of 266 submissions. The two volumes offer topical sections on recognitions and modelling; stereoscopic vision; texture and shading; shape; structure from motion; image features; active, real-time, and robot vision; segmentation and grouping; vision systems engineering and evaluation; calibration; medical image understanding; and visual motion.

Shape-based Object Recognition

Author : Thomas B. Sebastian
Publisher :
ISBN 13 :
Total Pages : 416 pages
Book Rating : 4.:/5 (532 download)

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Book Synopsis Shape-based Object Recognition by : Thomas B. Sebastian

Download or read book Shape-based Object Recognition written by Thomas B. Sebastian and published by . This book was released on 2002 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt:

A Two Dimensional Object Recognition System Using a Gradient-based Shape Metric Technique

Author : Guat Eng Gan
Publisher :
ISBN 13 :
Total Pages : 136 pages
Book Rating : 4.:/5 (392 download)

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Book Synopsis A Two Dimensional Object Recognition System Using a Gradient-based Shape Metric Technique by : Guat Eng Gan

Download or read book A Two Dimensional Object Recognition System Using a Gradient-based Shape Metric Technique written by Guat Eng Gan and published by . This book was released on 1997 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Matching Algorithms and Feature Match Quality Measures for Model-based Object Recognition with Applications to Automatic Target Recognition

Download Matching Algorithms and Feature Match Quality Measures for Model-based Object Recognition with Applications to Automatic Target Recognition PDF Online Free

Author : Martin Garcia Keller
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.:/5 (549 download)

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Book Synopsis Matching Algorithms and Feature Match Quality Measures for Model-based Object Recognition with Applications to Automatic Target Recognition by : Martin Garcia Keller

Download or read book Matching Algorithms and Feature Match Quality Measures for Model-based Object Recognition with Applications to Automatic Target Recognition written by Martin Garcia Keller and published by . This book was released on 1999 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:

From Shape-based Object Recognition and Discovery to 3D Scene Interpretation

Author : Nadia Payet
Publisher :
ISBN 13 :
Total Pages : 130 pages
Book Rating : 4.:/5 (723 download)

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Book Synopsis From Shape-based Object Recognition and Discovery to 3D Scene Interpretation by : Nadia Payet

Download or read book From Shape-based Object Recognition and Discovery to 3D Scene Interpretation written by Nadia Payet and published by . This book was released on 2011 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in images, as well as 3D scene reconstruction and scene interpretation. The key ideas behind our approaches include using shape as a basic object feature, and using structured prediction modeling paradigms for representing objects and scenes. In this work, we make a number of new contributions both in computer vision and machine learning. We address the vision problems of shape matching, shape-based mining of objects in arbitrary image collections, context-aware object recognition, monocular estimation of 3D object poses, and monocular 3D scene reconstruction using shape from texture. Our work on shape-based object discovery is the first to show that meaningful objects can be extracted from a collection of arbitrary images, without any human supervision, by shape matching. We also show that a spatial repetition of objects in images (e.g., windows on a building facade, or cars lined up along a street) can be used for 3D scene reconstruction from a single image. The aforementioned topics have never been addressed in the literature. The dissertation also presents new algorithms and object representations for the aforementioned vision problems. We fuse two traditionally different modeling paradigms Conditional Random Fields (CRF) and Random Forests (RF) into a unified framework, referred to as (RF)^2. We also derive theoretical error bounds of estimating distribution ratios by a two-class RF, which is then used to derive the theoretical performance bounds of a two-class (RF)^2. Thorough experimental evaluation of individual aspects of all our approaches is presented. In general, the experiments demonstrate that we outperform the state of the art on the benchmark datasets, without increasing complexity and supervision in training.

Object Class Recognition Using Global Shape Descriptors in 3D

Author :
Publisher :
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.:/5 (889 download)

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Book Synopsis Object Class Recognition Using Global Shape Descriptors in 3D by :

Download or read book Object Class Recognition Using Global Shape Descriptors in 3D written by and published by . This book was released on 2014 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: We formulate Global Shape Descriptors for object classifi cation in range data. The goal of object class recognition is to identify and localize objects of interest in a 3D point cloud.This work focuses on the automatic classifi cation of objects that lie within the vicinity of streets in 3D point clouds of urban environments. The system first successfully segments objects of interest from the scene through a combination of ground segmentation and road extraction using a Kalman filtering approach, and cluster extraction using a region growing technique. Those clusters that fall close to the road are then passed to a classifi cation phase, where they are compared against a labelled database of such clusters. The comparison of clusters is based upon Variable- Dimensional Global Shape Descriptors, which encode the geometry of the objects into multi-dimensional histograms, the similarities of which are measured against the database clusters using a variety of metrics including Earth Movers Distance and Bhattacharya similarity. The technique was tested on a dense data set acquired from central New York City, containing 110 objects partitioned into 5 classes. The method had an average successful recognition rate of 94.5% for a rich set of vehicles, pedestrians, and street furniture such as re hydrants, street signs, and poles.