Appearance Based Object Recognition In Range Images With Partitioned Local Feature Histograms
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Appearance Based Object Recognition In Range Images With Partitioned Local Feature Histograms
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Author : Shan Jiang
language : en
Publisher:
Release Date : 2004
Appearance Based Object Recognition In Range Images With Partitioned Local Feature Histograms written by Shan Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Appearance Based Objet Recognition In Range Images With Partitioned Local Feature Histograms
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Author : Shan Jiang
language : en
Publisher:
Release Date : 2004
Appearance Based Objet Recognition In Range Images With Partitioned Local Feature Histograms written by Shan Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Local Feature Histograms For Object Recognition From Range Images
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Author : Bastian Leibe
language : en
Publisher:
Release Date : 2001
Local Feature Histograms For Object Recognition From Range Images written by Bastian Leibe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Local Semi Local And Global Models For Texture Object And Scene Recognition
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Author : Svetlana Lazebnik
language : en
Publisher:
Release Date : 2006
Local Semi Local And Global Models For Texture Object And Scene Recognition written by Svetlana Lazebnik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Local Feature Based Representation For Object Tracking
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Author : Feng Tang
language : en
Publisher:
Release Date : 2007
Local Feature Based Representation For Object Tracking written by Feng Tang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Visual Object Recognition
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Author : Kristen Thielscher
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Visual Object Recognition written by Kristen Thielscher and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
The 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
Appearance Based Statistical Object Recognition Including Color And Context Modeling
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Author : Marcin Grzegorzek
language : en
Publisher: Logos Verlag Berlin
Release Date : 2007
Appearance Based Statistical Object Recognition Including Color And Context Modeling written by Marcin Grzegorzek and has been published by Logos Verlag Berlin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Within the scope of this dissertation a system for appearance-based statistical classification and localization of 3-D objects in 2-D digital images is presented. The first three chapters define the object recognition task, present the mathematical background of the system, and discuss the known approaches for object recognition. Then the learning phase of the system is described. The training begins with the image acquisition, which can be done with a hand-held camera. The object poses required for the further object modeling are computed from the training image sequence with the structure-from-motion algorithm. In contrast to shape-based approaches, appearance-based methods do not use any segmentation steps to extract object features. The objects are described by 2-D local feature vectors computed directly from image pixel values using the wavelet transform. Both gray level and color images can be used for feature extraction. Finally, the object features are statistically modeled with the normal distribution and stored in the object models as density functions. Additionally, context modeling is also performed in the training phase. In the recognition phase the system classifies and localizes objects in scenes with real heterogeneous background, whereas the number of objects in a scene is unknown. First, feature vectors are calculated in the scene with the same method as in the training. Second, a maximization algorithm evaluates the learned density functions with the extracted feature vectors and yields classes and poses of objects found in the scene. Experiments made on a real data set with more than 40000 images compare the classification and localization rates for all algorithms discussed in the dissertation and show a very good performance of the system in a real world environment.
Representations And Techniques For 3d Object Recognition And Scene Interpretation
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Author : Derek Hoiem
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011
Representations And Techniques For 3d Object Recognition And Scene Interpretation written by Derek Hoiem and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
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
Towards A Local Global Visual Feature Based Framework For Recognition
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Author : Zhipeng Zhao
language : en
Publisher:
Release Date : 2009
Towards A Local Global Visual Feature Based Framework For Recognition written by Zhipeng Zhao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computer vision categories.
General object and activity recognition is a fundamental problem in computer vision that has been the subject of much research. Traditional approaches include model based and appearance template based methods. Recently, inspired by methods from the text retrieval literature, local visual feature-based models have shown a lot of success for recognition of objects or activities with large within-class geometric variability. There are several challenges in this approach, namely feature selection and target modeling using these features. This thesis proposes a local-global visual feature-based framework for general object and activity recognition with novel methods for these problems: 1) Combinatorial and statistical methods for selecting informative parts to build statistical models for part-based object recognition. First a combinatorial optimization formulation is used for clustering on a weighted multipartite graph. Second, a statistical method for selecting discriminative parts from positive images is used to localize objects. 2) An entropy based vocabulary selection method for "bag-of-words" model for activity recognition. 3) Integrating both spatial and temporal information with appearance feature for human activity recognition. This method models the human motions with the distribution of local motion features and their spatial-temporal arrangements. The effectiveness of the proposed methods is demonstrated by several object recognition and activity recognition data sets, which include human facial expressions and hand gestures, etc. This thesis also covers an interesting project regarding a framework of applying Discrete Fourier Transform to detect salient regions in images and video sequences. This framework generalizes the previous saliency detection methods and can be applied for saliency detection in the video sequences.
Generic Object Recognition By Histograms Of Local Features
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Author : Enrique Larios Delgado
language : en
Publisher:
Release Date : 2008
Generic Object Recognition By Histograms Of Local Features written by Enrique Larios Delgado and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Arthropoda categories.
