Download or read book Object Recognition in Noisy Natural Images written by Prasanna Kannappan and published by . This book was released on 2017 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous robotic systems can operate in an unsupervised manner over remote or potentially dangerous domains. Object recognition is an important trait required for a robotic system to achieve autonomy. The task of object recognition involves understanding and labeling the different components in a robot's environment. This task becomes complicated for robots that operate in unstructured natural environments, like forests or deep sea, due to noise in sensor measurements. Noisy sensor measurements can potentially affect a robot's perception of the world. To avoid being misled by corrupted measurements, robots need to possess robust object recognition capabilities that can handle noise in sensor measurements. Such robust object recognition capabilities are valuable for processing large natural image datasets. One such case of image datasets are the underwater imagery data gathered by marine scientists and oceanographers; there, automatic object recognition capabilities could be invaluable. Such a capability would enable the automatic analysis of these datasets to understand natural phenomena, for instance to recognize different organisms of interest. Sifting through such big datasets, which can range into millions of images, and making inferences based on this data, is evolving into one of the biggest challenges in the field research community. This motivates the need for automated object recognition and image analysis tools. ☐ This dissertation focusses on object recognition techniques capable of operating in noisy natural environments. A series underwater object recognition problems have been solved as means to validate the developed object recognition algorithms. Each technique was developed to complement the shortcomings of the existing tools available to the research community. At first, eigen-value based shape descriptors were tasked to solve a submerged subway car recognition problem. Despite being successful in solving this problem, the eigen-value shape descriptor method cannot leverage textural cues for object identification. This primary drawback, among other shortcomings, lead to the development of a multi-layered object recognition architecture. This multilayered architecture was tested on an scallop enumeration problem. 60-70% of scallop instances were successfully identified. To improve the machine learning classifier of this multi-layered framework, and also to minimize false positives, a multi-view object classification approach is proposed. This multi-view approach combines histogram-based global cues from a series of images of a target, captured from different heights, to construct a machine learning classifier. This multi-view method was successful in classifying all specimens in the available dataset. In addition to the developed object recognition methods, a low cost ROV, named CoopROV, was designed for underwater data collection to support research experiments.
Download or read book Fast Object Recognition in Noisy Images Using Simulated Annealing written by M. Betke and published by . This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Active Perception and Robot Vision written by Arun K. Sood and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 747 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent robotics has become the focus of extensive research activity. This effort has been motivated by the wide variety of applications that can benefit from the developments. These applications often involve mobile robots, multiple robots working and interacting in the same work area, and operations in hazardous environments like nuclear power plants. Applications in the consumer and service sectors are also attracting interest. These applications have highlighted the importance of performance, safety, reliability, and fault tolerance. This volume is a selection of papers from a NATO Advanced Study Institute held in July 1989 with a focus on active perception and robot vision. The papers deal with such issues as motion understanding, 3-D data analysis, error minimization, object and environment modeling, object detection and recognition, parallel and real-time vision, and data fusion. The paradigm underlying the papers is that robotic systems require repeated and hierarchical application of the perception-planning-action cycle. The primary focus of the papers is the perception part of the cycle. Issues related to complete implementations are also discussed.
Download or read book Natural Object Recognition written by Thomas M. Strat and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Fast Object Recognition in Noisy Images Using Simulated Annealing written by M. Betke and published by . This book was released on 1994 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Object Recognition Based on Impulse Restoration for Images in Gaussian Noise written by Ahmed Abu-Naser and published by . This book was released on 1996 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Small Object Detection in Noisy and Cluttered Images written by Xin Sheng and published by . This book was released on 1999 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Object Recognition Of Digital Images In Wavelet Neural Network written by Arul Murugan R and published by Archers & Elevators Publishing House. This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Denoising of Photographic Images and Video written by Marcelo Bertalmío and published by Springer. This book was released on 2018-09-10 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference presents a detailed review of noise removal for photographs and video. An international selection of expert contributors provide their insights into the fundamental challenges that remain in the field of denoising, examining how to properly model noise in real scenarios, how to tailor denoising algorithms to these models, and how to evaluate the results in a way that is consistent with perceived image quality. The book offers comprehensive coverage from problem formulation to the evaluation of denoising methods, from historical perspectives to state-of-the-art algorithms, and from fast real-time techniques that can be implemented in-camera to powerful and computationally intensive methods for off-line processing. Topics and features: describes the basic methods for the analysis of signal-dependent and correlated noise, and the key concepts underlying sparsity-based image denoising algorithms; reviews the most successful variational approaches for image reconstruction, and introduces convolutional neural network-based denoising methods; provides an overview of the use of Gaussian priors for patch-based image denoising, and examines the potential of internal denoising; discusses selection and estimation strategies for patch-based video denoising, and explores how noise enters the imaging pipeline; surveys the properties of real camera noise, and outlines a fast approximation of nonlocal means filtering; proposes routes to improving denoising results via indirectly denoising a transform of the image, considering the right noise model and taking into account the perceived quality of the outputs. This concise and clearly written volume will be of great value to researchers and professionals working in image processing and computer vision. The book will also serve as an accessible reference for advanced undergraduate and graduate students in computer science, applied mathematics, and related fields. "The relentless quest for higher image resolution, greater ISO sensitivity, faster frame rates and smaller imaging sensors in digital imaging and videography has demanded unprecedented innovation and improvement in noise reduction technologies. This book provides a comprehensive treatment of all aspects of image noise including noise modelling, state of the art noise reduction technologies and visual perception and quantitative evaluation of noise.” Geoff Woolfe, Former President of The Society for Imaging Science and Technology. "This book on denoising of photographic images and video is the most comprehensive and up-to-date account of this deep and classic problem of image processing. The progress on its solution is being spectacular. This volume therefore is a must read for all engineers and researchers concerned with image and video quality." Jean-Michel Morel, Professor at Ecole Normale Supérieure de Cachan, France.
Download or read book Invariant Object Recognition Based on Elastic Graph Matching written by Raymond S. T. Lee and published by . This book was released on 2003 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Object Recognition from Range Images written by Richard Lee Hoffman and published by . This book was released on 1986 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Download or read book Visual Object Recognition written by Kristen Grauman and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Object Recognition written by Tam Phuong Cao and published by BoD – Books on Demand. This book was released on 2011-04-01 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs.
Download or read book Stochastic Resonance Investigation of Object Detection in Images written by and published by . This book was released on 2006 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection in images was conducted using a nonlinear means of improving signal to noise ratio termed "stochastic resonance" (SR). In a recent United States Patent application, it was shown that arbitrarily large signal to noise ratio gains could be realized when a signal detection problem is cast within the context of a SR filter. Signal-to-noise ratio measures were investigated. For a binary object recognition task (friendly versus hostile), the method was implemented by perturbing the recognition algorithm and subsequently thresholding via a computer simulation.
Download or read book Object Detection and Recognition in Digital Images written by Boguslaw Cyganek and published by John Wiley & Sons. This book was released on 2013-05-20 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.
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
