Download or read book Object Recognition Through Image Understanding for an Autonomous Mobile Robot written by Mark Joseph DeClue and published by . This book was released on 1993 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem addressed in this research was to provide a capability for sensing previously unknown rectilinear, polyhedral-shaped objects in the operating environment of the autonomous mobile robot Yamabico-11. The approach to the system design was based on the application of edge extraction and least squares line fitting algorithms of PET92 to real-time camera images with subsequent filtering based on the environmental model of STE92. The output of this processing was employed in the recognition of obstacles and the determination of object range and dimensions. These measurements were then used in path tracking commands, supported by Yamabico's Model-based Mobile Robot Language (MML), for performing smooth, safe obstacle avoidance maneuvers. This work resulted in a system able to localize objects in images taken from the robot, provide location and size data, and cause proper path adjustments. Accuracies on the order of one to ten centimeters in range and one-half to two centimeters in dimensions were achieved.

Download or read book Object Recognition Through Image Understanding for an Autonomous Mobile Robot written by Mark Joseph DeClue and published by . This book was released on 1993 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem addressed in this research was to provide a capability for sensing previously unknown rectilinear, polyhedral-shaped objects in the operating environment of the autonomous mobile robot Yamabico-11. The approach to the system design was based on the application of edge extraction and least squares line fitting algorithms of PET92 to real-time camera images with subsequent filtering based on the environmental model of STE92. The output of this processing was employed in the recognition of obstacles and the determination of object range and dimensions. These measurements were then used in path tracking commands, supported by Yamabico's Model-based Mobile Robot Language (MML), for performing smooth, safe obstacle avoidance maneuvers. This work resulted in a system able to localize objects in images taken from the robot, provide location and size data, and cause proper path adjustments. Accuracies on the order of one to ten centimeters in range and one-half to two centimeters in dimensions were achieved.

Download or read book Knowledge Based Vision Guided Robots written by Nick Barnes and published by Physica. This book was released on 2012-12-06 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many robotics researchers consider high-level vision algorithms (computational) too expensive for use in robot guidance. This book introduces the reader to an alternative approach to perception for autonomous, mobile robots. It explores how to apply methods of high-level computer vision and fuzzy logic to the guidance and control of the mobile robot. The book introduces a knowledge-based approach to vision modeling for robot guidance, where advantage is taken of constraints of the robot's physical structure, the tasks it performs, and the environments it works in. This facilitates high-level computer vision algorithms such as object recognition at a speed that is sufficient for real-time navigation. The texts presents algorithms that exploit these constraints at all levels of vision, from image processing to model construction and matching, as well as shape recovery. These algorithms are demonstrated in the navigation of a wheeled mobile robot.

Download or read book Machine Learning based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment written by Xiaochun Wang and published by Springer. This book was released on 2019-08-12 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

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 Image Understanding Workshop written by and published by . This book was released on 1988 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Animat Vision microform Active Vision in Artificial Animals written by Tamer F. (Tamer Farouk) Rabie and published by National Library of Canada = Bibliothèque nationale du Canada. This book was released on 1999 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Download or read book Learning Based Robot Vision written by Josef Pauli and published by Springer. This book was released on 2003-06-29 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can be planned during an - line phase. A continual control based on sensory feedback is at most necessary at e?ector positions near target locations utilizing torque or haptic sensors. It is desirable to develop new-generation robots showing higher degrees of autonomy for solving high-level deliberate tasks in natural and dynamic en- ronments. Obviously, camera-equipped robot systems, which take and process images and make use of the visual data, can solve more sophisticated robotic tasks. The development of a (semi-) autonomous camera-equipped robot must be grounded on an infrastructure, based on which the system can acquire and/or adapt task-relevant competences autonomously. This infrastructure consists of technical equipment to support the presentation of real world training samples, various learning mechanisms for automatically acquiring function approximations, and testing methods for evaluating the quality of the learned functions. Accordingly, to develop autonomous camera-equipped robot systems one must ?rst demonstrate relevant objects, critical situations, and purposive situation-action pairs in an experimental phase prior to the application phase. Secondly, the learning mechanisms are responsible for - quiring image operators and mechanisms of visual feedback control based on supervised experiences in the task-relevant, real environment. This paradigm of learning-based development leads to the concepts of compatibilities and manifolds. Compatibilities are general constraints on the process of image formation which hold more or less under task-relevant or accidental variations of the imaging conditions.

Download or read book Vision for Robotics written by Danica Kragic and published by Now Publishers Inc. This book was released on 2009 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of various tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing visionbased control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.

Download or read book Robot Vision written by Stefan Florczyk and published by John Wiley & Sons. This book was released on 2006-03-06 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is intended for advanced students in physics, mathematics, computer science, electrical engineering, robotics, engine engineering and for specialists in computer vision and robotics on the techniques for the development of vision-based robot projects. It focusses on autonomous and mobile service robots for indoor work, and teaches the techniques for the development of vision-based robot projects. A basic knowledge of informatics is assumed, but the basic introduction helps to adjust the knowledge of the reader accordingly. A practical treatment of the material enables a comprehensive understanding of how to handle specific problems, such as inhomogeneous illumination or occlusion. With this book, the reader should be able to develop object-oriented programs and show mathematical basic understanding. Such topics as image processing, navigation, camera types and camera calibration structure the described steps of developing further applications of vision-based robot projects.

Download or read book Vision Based Autonomous Robot Navigation written by Amitava Chatterjee and published by Springer. This book was released on 2012-10-13 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is devoted to the theory and development of autonomous navigation of mobile robots using computer vision based sensing mechanism. The conventional robot navigation systems, utilizing traditional sensors like ultrasonic, IR, GPS, laser sensors etc., suffer several drawbacks related to either the physical limitations of the sensor or incur high cost. Vision sensing has emerged as a popular alternative where cameras can be used to reduce the overall cost, maintaining high degree of intelligence, flexibility and robustness. This book includes a detailed description of several new approaches for real life vision based autonomous navigation algorithms and SLAM. It presents the concept of how subgoal based goal-driven navigation can be carried out using vision sensing. The development concept of vision based robots for path/line tracking using fuzzy logic is presented, as well as how a low-cost robot can be indigenously developed in the laboratory with microcontroller based sensor systems. The book describes successful implementation of integration of low-cost, external peripherals, with off-the-shelf procured robots. An important highlight of the book is that it presents a detailed, step-by-step sample demonstration of how vision-based navigation modules can be actually implemented in real life, under 32-bit Windows environment. The book also discusses the concept of implementing vision based SLAM employing a two camera based system.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book External Neural Network Augmentation for Fast Object Recognition written by Daniel Bonness and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time computer-based object recognition is necessary for useful computer vision, and is thus of critical importance to the development of autonomous robots. Biologically inspired artificial neural networks can perform object recognition with a high degree of accuracy, but often fail to achieve real-time execution speeds. This thesis presents two methods for reducing the computation time of a neural network, specifically using HMAX a neural network developed by MIT, with the end goal of achieving real-time object recognition in an autonomous mobile robot. First, this thesis presents a method for feature removal in linear SVMs that optimally selects appropriate features for object recognition. By removing unnecessary features, this method attempts to improve speed while maintaining accuracy, effectively introducing a feedback loop into a neural network. Second, this thesis presents an a priori estimation method that determines object location using superpixels, a type of image segmentation. By matching superpixels, which can identify visually consistent regions within an image, between frames, it is possible to estimate target object location without running the computationally intensive HMAX algorithm on every frame. These methods were validated using real-world images captured by a front-facing camera while driving on a dirt road at sunset. The results show these methods can significantly reduce the computation time of a neural network, while maintaining classification accuracy.

Download or read book Mobile Robot Navigation with Intelligent Infrared Image Interpretation written by William L. Fehlman and published by Springer Science & Business Media. This book was released on 2009-06-13 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. The resulting classification model complements an autonomous robot’s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment.

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 Visual Object Recognition written by Kristen Thielscher and published by Springer Nature. This book was released on 2022-05-31 with total page 163 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