Download or read book Three dimensional Object Recognition and Registration Using Incremental Feature Extraction written by Hsianglung Wu and published by . This book was released on 1986 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Dimensional Object Recognition from Range Images written by Minsoo Suk and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Science Workbench is a monograph series which will provide you with an in-depth working knowledge of current developments in computer technology. Every volume in this series will deal with a topic of importance in computer science and elaborate on how you yourself can build systems related to the main theme. You will be able to develop a variety of systems, including computer software tools, computer graphics, computer animation, database management systems, and computer-aided design and manufacturing systems. Computer Science Workbench represents an important new contribution in the field of practical computer technology. T08iyasu L. Kunii PREFACE The primary aim of this book is to present a coherent and self-contained de scription of recent advances in three-dimensional object recognition from range images. Three-dimensional object recognition concerns recognition and localiza tion of objects of interest in a scene from input images. This problem is one of both theoretical and practical importance. On the theoretical side, it is an ideal vehicle for the study of the general area of computer vision since it deals with several important issues encountered in computer vision-for example, issues such as feature extraction, acquisition, representation and proper use of knowl edge, employment of efficient control strategies, coupling numerical and symbolic computations, and parallel implementation of algorithms. On the practical side, it has a wide range of applications in areas such as robot vision, autonomous navigation, automated inspection of industrial parts, and automated assembly.

Download or read book Three Dimensional Object Recognition Using an Unsupervised Neural Network Understanding the Distinguishing Features written by and published by . This book was released on 1992 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: A novel method for feature extraction has been applied to a problem of three-dimensional object recognition (Intrator and Gold, 1991). The method is related to recent statistical theory (Huber, 1985; Friedman, 1987) and is derived from a biologically motivated computational theory (Bienenstock et al., 1982). Results of an initial study replicating recent psychophysical experiments (Buelthoff and Edelman, 1991) demonstrated the utility of the proposed method for feature extraction. The authors describe further experiments designed to analyze the nature of the extracted features and their relevance to the theory and psychophysics of object recognition.

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.

Download or read book View based 3 D Object Retrieval written by Yue Gao and published by Morgan Kaufmann. This book was released on 2014-12-04 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic. View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications. Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions Presents the progression from general image retrieval techniques to view-based 3-D object retrieval Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications

Download or read book Three Dimensional Object Recognition Systems written by Anil K Jain and published by . This book was released on 1993-05-05 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design and construction of three-dimensional [3-D] object recognition systems has long occupied the attention of many computer vision researchers. The variety of systems that have been developed for this task is evidence both of its strong appeal to researchers and its applicability to modern manufacturing, industrial, military, and consumer environments. 3-D object recognition is of interest to scientists and engineers in several different disciplines due to both a desire to endow computers with robust visual capabilities, and the wide applications which would benefit from mature and robust vision systems. However, 3-D object recognition is a very complex problem, and few systems have been developed for actual production use; most existing systems have been developed for experimental use by researchers only. This edited collection of papers summarizes the state of the art in 3-D object recognition using examples of existing 3-D systems developed by leading researchers in the field. While most chapters describe a complete object recognition system, chapters on biological vision, sensing, and early processing are also included. The volume will serve as a valuable reference source for readers who are involved in implementing model-based object recognition systems, stimulating the cross-fertilisation of ideas in the various domains. The variety of topics on Image Communication is so broad that no one can be a specialist in all the topics, and the whole area is beyond the scope of a single volume, while the requirement of up to date information is ever increasing. This new closed-end book series is intended both as a comprehensive reference for those already active in the area of Image Communication, as well as providing newcomers with a foothold for commencing research. Each volume will comprise a state of the art work on the editor's/author's area of expertise, containing information until now scattered in many journals and proceedings.

Download or read book Three Dimensional Feature Extraction written by Darwin Kuan and published by . This book was released on 1983 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Range images offer a significant advantages over passive reflectance images because they preserve the 3-D information of the scene viewed from the sensor. Therefore, range data is becoming an increasingly important source of information for a variety of applications including 3-D target classification, autonomous vehicles, and robot vision. This research is part of an effort to develop a 3-d object recognition system for vehicle objects in air-to-ground laser range imagery. The full system includes image feature extraction, object modeling, model-driven prediction, and feature to model matching. This paper presents several three-dimensional feature extraction techniques for use on laser range imagery. These include object-ground segmentation, projection image generation from range data, and 3-D physical edge detection. We emphasize extracting 3-D physical features of the object from 3-D range data without restricting ourselves in a sensor-centered range image format. The object-ground segmentation and projection image generation techniques extract global object features from range data, and are useful for object orientation estimation and major structures identification. The 3-d physical edge detector directly calculates the physical angle of the object surface. It is not only useful for physical edge (convex, concave, occluding) detection, but also provides useful information for extracting planar and curved surfaces.

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Santhanam and published by Springer Nature. This book was released on 2022-05-31 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Three Dimensional Object Recognition Using Linear Features and Oriented Model Points written by Teresa Marie Silberberg and published by . This book was released on 1984 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Model based Three dimensional Object Recognition Using Generalized Features written by Dong-Liang Daniel Sheu and published by . This book was released on 1992 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three dimensional Object Feature Extraction and Classification Using Computational Holographic Imaging written by Sekwon Yeom and published by . This book was released on 2003 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three dimensional Object Description and Recognition Using Feature Vector Techniques written by Russell Williams Taylor and published by . This book was released on 1988 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Object Representation and Matching Based on Skeletons and Curves written by Christian Feinen and published by Logos Verlag Berlin GmbH. This book was released on 2016-05-31 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is dedicated to the problem of object recognition in the three-dimensional space. Instead of using exclusively the information typically transported by a two-dimensional image, the concept of this work additionally incorporates the third dimension, namely the depth. The depth data itself is captured by sensors capable of measuring the distance from the device's position to those objects residing inside its field of view. The actual recognition process is implemented in analogy to the Path Similarity Skeleton Graph Matching (PSSGM). Basically, this method represents a 2D object by its skeleton and uses the idea of shortest paths to describe it. Finally, the similarity between two objects is calculated based on the Hungarian method. The contribution of the current work maps this approach into the three-dimensional space and applies it to 3D objects. While one of the experiments aims at the recognition of 3D chairs and tables, another one is devoted to the registration of fully segmented vascular structures. Excellent and promising recognition results are achieved in challenging evaluation setups showing that the 3D version of the PSSGM has the potential to solve complex recognition tasks.

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

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 276 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

Download or read book Three Dimensional Object Recognition Using an Unsupervised BCM Network The Usefulness of Distinguishing Features written by and published by . This book was released on 1993 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biological motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical 3D object recognition experiments.

Download or read book Recognising Three dimensional Objects Using Parameterized Volumetric Models written by Dibio Leandro Borges and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addressed the problem of recognizing 3-D objects, using shape information extracted from range images, and parameterized volumetric models. The domains of the geometric shapes explored is that of complex curved objects with articulated parts, and a great deal of similarity between some of the parts. These objects are exemplified by animal shapes, however the general characteristics and complexity of these shapes are present in a wide range of other natural and man-made objects. In model-based object recognition three main issues constrain the design of a complete solution: representation, feature extraction, and interpretation. this thesis develops an integrated approach that addresses these three issues in the context of the above mentioned domain of objects. For representation I propose a composite description using globally deformable superquadratics and a set of volumetric primitives called geons: this description is shown to have representational and discriminative properties suitable for recognition. Feature extraction comprises a segmentation process which develops a method to extract a parts-based description of the objects as assemblies of defoemable superquadratics. Discontinuity points detected from the images are linked using 'active contour' minimization technique, and deformable superquadratic models are fitted to the resulting regions afterwards. Interpretation is split into three components: classification of parts, matching, and pose estimation. A Radical Basis Function [RBF] classifier algoritm is presented in order to classify the superquadratics shapes derived from the segmentation into one of twelve geon classes. The matching component is decomposed into two stages: first, an indexing scheme which makes effective use of the output of the [RBF] classifier in order to direct the search to the models which contain the parts identified. this makes the search more efficient, and with a model library that is organised in a meaningful and robust way, permits growth without compromising performance. Second, a method is proposed where the hypotheses picked from the index are searched using an Interpretation Tree algorithm combined with a quality measure to evaluate the bindings and the final valid hypotheses based on Possibility Theory, or Theory of Fuzzy Sets. The valid hypotheses ranked by the matching process are then passed to the pose estimation module. This module uses a Kalman Filter technique that includes the constraints on the articulations as perfect measurements, and as such provides a robust and generic way to estimate pose in object domains such as the one approached here. These techniques are then combined to produce an integrated approach to the object recognition task. The thesis develops such an integrated approach, and evaluates its perfomance inthe sample domain. Future extensions of each technique and the overall integration strategy are discussed.