Biologically-inspired Computer Vision

Biologically-inspired Computer Vision

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As the statea€“ofa€“thea€“art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with todaya€²s applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, nona€“weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity. This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.First, codes are available in matlab [63] or C/C++/python [8] to extract and represent keypoints with the presented algorithms. Second, the source code to generate the experiments of the feature inversion algorithms are available online as well [64] and [65]. ... from noise, but also relies on the Gestalt theory (as formalized by Wertheimer and Kanizsa) to model the human perception. This allows the authors to derive robust, parameter-less algorithms to detect low-level features in images.

Title:Biologically-inspired Computer Vision
Author:Gabriel Cristobal, Matthias Keil, Laurent Perrinet
Publisher:John Wiley & Sons - 2015-11-02


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