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Robotics, Vision and Control : Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition
Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. The research community has developed a large body of such algorithms but for a newcomer to the field this can be quite daunting. For over 20 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in an accessible but informative style, easy to read and absorb, and includes over 1000 MATLAB and Simulink® examples and over 400 figures. The book is a real walk through the fundamentals of mobile robots, arm robots. then camera models, image processing, feature extraction and multi-view geometry and finally bringing it all together with an extensive discussion of visual servo systems. This second edition is completely revised, updated and extended with coverage of Lie groups, matrix exponentials and twists; inertial navigation; differential drive robots; lattice planners; pose-graph SLAM and map making; restructured material on arm-robot kinematics and dynamics; series-elastic actuators and operational-space control; Lab color spaces; light field cameras; structured light, bundle adjustment and visual odometry; and photometric visual servoing. \"An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished!\" OUSSAMA KHATIB, Stanford.
Book
Managing Transference and Countertransference in Cognitive Behavioral Supervision: Theoretical Framework and Clinical Application
Dysfunctional patterns, beliefs, and assumptions that affect a patient's perception of other people often affect their perceptions and behaviours towards the therapist. This tendency has been traditionally called transference for its psychoanalytical roots and presents an important factor to monitor and process. In supervision, it is important to put the patient's transference in the context of the conceptualization of the case. Countertransference occurs when the therapist responds complementary to the patient's transference based on their own dysfunctional beliefs or assumptions. Transference and countertransference provide useful insights into the inner world of the patient, therapist, and supervisor. Guided discovery is one of the most common approaches used by a supervisor and a supervisee to map all types and directions of transference and countertransference. Other options to map transference and countertransference are imagery and role-playing techniques. Keywords: supervision, cognitive behavioral therapy, therapeutic relationship, supervisory relationship, transference, countertransference
Journal Article
Robo-vision! 3D mesh generation of a scene for a robot for planar and non-planar complex objects
This paper presents a novel architecture to generate a world model in terms of mesh from a continuous image stream with depth information extracted from a robot’s ego-vision camera. We propose two algorithms for planar and non-planar mesh generation. A Cartesian grid-based mesh fitting algorithm is proposed for mesh generation of planar objects. For mesh generation of non-planar objects, we propose a Self Organization Map based algorithm. The proposed algorithm better approaches the boundary and overall shape of the objects compared to State-Of-the-Art (SOA). Extensive experiments done on three public datasets show that our method surpasses SOA both qualitatively and quantitatively.
Journal Article
Homework in Cognitive Behavioral Supervision: Theoretical Background and Clinical Application
The homework aims to generalize the patient's knowledge and encourage practicing skills learned during therapy sessions. Encouraging and facilitating homework is an important part of supervisees in their supervision, and problems with using homework in therapy are a common supervision agenda. Supervisees are encouraged to conceptualize the patient's lack of homework and promote awareness of their own beliefs and responses to non-cooperation. The supervision focuses on homework twice - first as a part of the supervised therapy and second as a part of the supervision itself. Homework assigned in supervision usually deals with mapping problems, monitoring certain behaviors (mostly communication with the patient), or implementing new behaviors in therapy.
Journal Article
Using surfaces and surface relations in an Early Cognitive Vision system
We present a deep hierarchical visual system with two parallel hierarchies for edge and surface information. In the two hierarchies, complementary visual information is represented on different levels of granularity together with the associated uncertainties and confidences. At all levels, geometric and appearance information is coded explicitly in 2D and 3D allowing to access this information separately and to link between the different levels. We demonstrate the advantages of such hierarchies in three applications covering grasping, viewpoint independent object representation, and pose estimation.
Journal Article
Brain Programming for the Evolution of an Artificial Dorsal Stream
This work describes the application of a new strategy called brain programming for automating the design of visual attention (VA) models. Nowadays, a term known as cognitive vision coined within the computer vision and cognitive research communities has been introduced to delimitate the kind of computer vision systems that are robust, resilient and adaptable to the task at hand through the incorporation of cognitive abilities. In particular, visual attention is considered as a critical factor whose main goal is to establish a relationship between the different properties or features of the scene with the aim of selecting the most suitable aspects for the task at hand. This paper follows a main trend in cognitive computation where the visual pathway is modeled through a succession of levels or layers. Here, the VA task is defined with the idea that several areas of the brain are in charge of its functionality in a hierarchical way. To achieve such functionality, we propose that an artificial process, mimicking the natural counterpart, would be charged of looking for a set of complex operations using an optimization/search process. The idea is to include such operations within a VA model that will be evolved according to a specific task. The aim of the whole process is to provide with the best solutions among the space of possible visual attention programs (VAPs) for a given problem. In this way, the article presents a methodology for automating the design of VAPs. Therefore, the final design can be seen as a cognitive vision system that is engaged in a purposive goal-directed behavior. The results obtained on a well-known testbed confirm that the proposal is able to automatically design VAPs that outperform previous man-made systems developed by VA experts, while providing readable results through a set of mathematical and computational structures.
Journal Article
Steps toward a Cognitive Vision System
An adequate natural language description of developments in a real‐world scene can be taken as proof of “understanding what is going on.” An algorithmic system that generates natural language descriptions from video recordings of road traffic scenes can be said to “understand” its input to the extent that algorithmically generated text is acceptable to the humans judging it. A fuzzy metric‐temporal Horn logic (fmthl) provides a formalism for representing both schematic and instantiated conceptual knowledge about the depicted scene and its temporal development. The resulting conceptual representation mediates in a systematic manner between the spatiotemporal geometric descriptions extracted from video input and a module that generates natural language text. This article outlines a 30‐year effort to create such a cognitive vision system, indicates its current status, summarizes lessons learned along the way, and discusses open problems against this background.
Journal Article
2D qualitative shape matching applied to ceramic mosaic assembly
A theory of shape recognition of 2D objects and its application in the ceramic industry for intelligent automation of the mosaic mural assembly process are presented in this paper. This theory qualitatively describes the shapes of the objects, considering: (i) shape boundary characteristics, such as angles, relative length, concavities, and curvature; and (ii) their color and size. The shapes to be recognized may be regular or irregular closed polygons, or closed curvilinear figures. Each figure is described as a symbolic character string that contains all its distinctive characteristics. This description is used to determine whether the shape of two figures matches. Then, given a design of a mosaic and given a set of physical ceramic tesserae, an application is developed in order to recognize the tesserae that form the mosaic, thus enabling the intelligent and automated assembly of ceramic mosaics.
Journal Article
Using a vision cognitive algorithm to schedule virtual machines
Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption
Journal Article