A Stochastic Framework for Object Recognition
Author: Rong Zhang
Publisher:
Published: 2000
Total Pages: 178
ISBN-13:
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Object Recognition by Stochastic Model Adaptation and Selection
Author: Antony Louis Reno
Publisher:
Published: 1999
Total Pages:
ISBN-13:
DOWNLOAD EBOOKA Stochastic Hierarchical Model and Nonparametric Statistics Approach of Object Recognition
Author: Antonio Zelić
Publisher:
Published: 1996
Total Pages: 174
ISBN-13:
DOWNLOAD EBOOKToward Category-Level Object Recognition
Author: Jean Ponce
Publisher: Springer
Published: 2007-01-25
Total Pages: 622
ISBN-13: 3540687955
DOWNLOAD EBOOKThis 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.
Clustered Stochastic Optimization for Object Recognition and Pose Estimation
Author: Jürgen Gall
Publisher:
Published: 2007
Total Pages: 18
ISBN-13:
DOWNLOAD EBOOKAbstract: "We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. The pose estimation without initial information is a challenging optimization problem in a high dimensional space and is essential for texture acquisition and initialization of model-based tracking algorithms. Our method is able to recognize the correct object in the case of multiple objects and estimates its pose with a high accuracy. The key component is a particle-based global optimization method that converges to the global minimum similar to simulated annealing. After detecting potential bounded subsets of the search space, the particles are divided into clusters and migrate to the most attractive cluster as the time increases. The performance of our approach is verified by means of real scenes and a quantative error analysis for image distortions. Our experiments include rigid bodies and full human bodies."
Visual Object Recognition
Author: Kristen Grauman
Publisher: Morgan & Claypool Publishers
Published: 2011
Total Pages: 184
ISBN-13: 1598299689
DOWNLOAD EBOOKThe visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Object Detection with Deep Learning Models
Author: S Poonkuntran
Publisher: CRC Press
Published: 2022-11-01
Total Pages: 345
ISBN-13: 1000686795
DOWNLOAD EBOOKObject 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
An Introduction to Object Recognition
Author: Marco Alexander Treiber
Publisher: Springer Science & Business Media
Published: 2010-07-23
Total Pages: 210
ISBN-13: 1849962359
DOWNLOAD EBOOKRapid 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.
Computational Problems in Image Analysis, Multiple Object Recognition, and Speech Recognition
Author: Basilis Gidas
Publisher:
Published: 1995
Total Pages: 9
ISBN-13:
DOWNLOAD EBOOKDuring the period of the grant, 2/1/93 - 1/15/95, we developed: (1) a Bayesian framework for object detection and tracking; the algorithm was successfully tested on real-data in the detection and tracking of vehicles on a highway: (2) a recognition algorithm based on stochastic hierarchical, context-free-grammars type, object representation; the study has required the development of feasible pruning techniques for dynamic programming; (3) a new acoustic model for speech recognition based on a wavelet representation of the acoustic-signal, and nonparametric prediction techniques. (AN).
Stochastic Algorithms for Visual Tracking
Author: John MacCormick
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 184
ISBN-13: 1447106792
DOWNLOAD EBOOKA central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.
