A Joint Framework For Object Recognition
Download A Joint Framework For Object Recognition full books in PDF, epub, and Kindle. Read online free A Joint Framework For Object Recognition ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads.
Author |
: Tarek El-Gaaly |
Publisher |
: |
Total Pages |
: 152 |
Release |
: 2016 |
ISBN-10 |
: OCLC:945683880 |
ISBN-13 |
: |
Rating |
: 4/5 (80 Downloads) |
Synopsis A Joint Framework for Object Recognition by : Tarek El-Gaaly
Visual object recognition is a challenging problem with a wide range of real-life applications. The difficulty of this problem is due to variation in shape and appearance among objects within the same category, as well as varying viewing conditions, such as viewpoint, scale, illumination, occlusion and articulation of multi-part deformable objects. In addition, beyond the visual spectrum, depth and range sensors suffer from noise that inhibits object recognition. Under visual object recognition lie three subproblems that are each challenging: category recognition, instance recognition and pose estimation. Impressive work has been done in the last decade on developing systems for generic object recognition. Previous research has covered many recognition-related issues, however, the problem of multi-view recognition remains among the most fundamental challenges in computer vision. In this dissertation we focus on discovering low-dimensional latent representations that enable efficient joint multi-view object recognition over multiple modalities. These discovered latent representations allow us to work in lower dimensional latent spaces that capture the factors needed for object recognition from multi-view images and over multiple modalities; from images to depthmaps and 3D point clouds. Each of the models we present in this dissertation explore a different representation space of latent factors. The first model builds multiple kernel induced spaces to fuse information between different modalities and performs object pose estimation in a regression framework. The second model performs manifold analysis to solve categorization and pose estimation simultaneously. It does this by factorizing the space of topological mappings between a unified conceptual manifold and feature spaces. We present two variations of this; an unsupervised learning model and a supervised learning model. The third approach analyzes the representational spaces of the layers of Convolutional Neural Networks and builds on the findings by proposing a network that jointly solves category and pose. The fourth approach explores solving pose-invariant categorization of multi-part objects by shape information, in the form of 3D point clouds. We build a representation that inherently encodes pose and allows objects to be represented by multiple levels of object-part decompositions for more robust object recognition. In each approach we support our hypotheses by extensive experimentation.
Author |
: Jean Ponce |
Publisher |
: Springer |
Total Pages |
: 622 |
Release |
: 2007-01-25 |
ISBN-10 |
: 9783540687955 |
ISBN-13 |
: 3540687955 |
Rating |
: 4/5 (55 Downloads) |
Synopsis Toward Category-Level Object Recognition by : Jean Ponce
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.
Author |
: S Poonkuntran |
Publisher |
: CRC Press |
Total Pages |
: 345 |
Release |
: 2022-11-01 |
ISBN-10 |
: 9781000686791 |
ISBN-13 |
: 1000686795 |
Rating |
: 4/5 (91 Downloads) |
Synopsis Object Detection with Deep Learning Models by : S Poonkuntran
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
Author |
: Rong Zhang |
Publisher |
: |
Total Pages |
: 178 |
Release |
: 2000 |
ISBN-10 |
: OCLC:45191183 |
ISBN-13 |
: |
Rating |
: 4/5 (83 Downloads) |
Synopsis A Stochastic Framework for Object Recognition by : Rong Zhang
Author |
: Marco Alexander Treiber |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 210 |
Release |
: 2010-07-23 |
ISBN-10 |
: 9781849962353 |
ISBN-13 |
: 1849962359 |
Rating |
: 4/5 (53 Downloads) |
Synopsis An Introduction to Object Recognition by : Marco Alexander Treiber
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.
Author |
: Thomas M. Strat |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 186 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461229322 |
ISBN-13 |
: 1461229324 |
Rating |
: 4/5 (22 Downloads) |
Synopsis Natural Object Recognition by : Thomas M. Strat
Natural Object Recognition presents a totally new approach to the automation of scene understanding. Rather than attempting to construct highly specialized algorithms for recognizing physical objects, as is customary in modern computer vision research, the application and subsequent evaluation of large numbers of relatively straightforward image processing routines is used to recognize natural features such as trees, bushes, and rocks. The use of contextual information is the key to simplifying the problem to the extent that well understood algorithms give reliable results in ground-level, outdoor scenes.
Author |
: Roohie Naaz Mir |
Publisher |
: CRC Press |
Total Pages |
: 319 |
Release |
: 2023-05-10 |
ISBN-10 |
: 9781000880410 |
ISBN-13 |
: 1000880419 |
Rating |
: 4/5 (10 Downloads) |
Synopsis Advancement of Deep Learning and its Applications in Object Detection and Recognition by : Roohie Naaz Mir
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.
Author |
: Louise Stark |
Publisher |
: World Scientific |
Total Pages |
: 162 |
Release |
: 1996 |
ISBN-10 |
: 9810215088 |
ISBN-13 |
: 9789810215088 |
Rating |
: 4/5 (88 Downloads) |
Synopsis Generic Object Recognition Using Form & Function by : Louise Stark
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.
Author |
: David Fleet |
Publisher |
: Springer |
Total Pages |
: 855 |
Release |
: 2014-08-14 |
ISBN-10 |
: 9783319105994 |
ISBN-13 |
: 331910599X |
Rating |
: 4/5 (94 Downloads) |
Synopsis Computer Vision -- ECCV 2014 by : David Fleet
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Author |
: Derek Hoiem |
Publisher |
: Morgan & Claypool Publishers |
Total Pages |
: 172 |
Release |
: 2011 |
ISBN-10 |
: 9781608457281 |
ISBN-13 |
: 1608457281 |
Rating |
: 4/5 (81 Downloads) |
Synopsis Representations and Techniques for 3D Object Recognition and Scene Interpretation by : Derek Hoiem
One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions