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Biologically Inspired Computer Vision

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Author by : Gabriel Cristobal
Languange Used : en
Release Date : 2015-08-20
Publisher by : John Wiley & Sons

ISBN : 9783527680474

As the state-of-the-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 today's applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-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....

Biologically Inspired Computer Vision

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Author by : Morgon Kanter
Languange Used : en
Release Date : 2009
Publisher by :

ISBN : OCLC:1430590633

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Computer Vision

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Author by : Ian Overington
Languange Used : en
Release Date : 1992
Publisher by : North Holland

ISBN : UOM:39015025240451

This unique volume is a comprehensive, self-consistent coverage of one approach to computer vision, with many direct or implied links to human vision. The book is the result of many years spent by the author in research into the limits of human visual performance and the interactions between the observer and his environment. A wide-ranging and largely novel approach to computer vision is described. The treatment starts with a summary account of important aspects of human visual function. This is then followed by a progressive development of the computer image processing, from sub-pixel fragmentary edge determination to optical flow field analysis, local and global stereo analysis, colour imagery, edge-based region segmentation, perceptual texture segmentation and high fidelity contour form analysis. This treatment is considerably different from other more publicised approaches, and is highly recommended for those seeking a dynamic new approach to computer vision....

Developing And Applying Biologically Inspired Vision Systems Interdisciplinary Concepts

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Author by : Pomplun, Marc
Languange Used : en
Release Date : 2012-11-30
Publisher by : IGI Global

ISBN : 9781466625402

"This book provides interdisciplinary research that evaluates the performance of machine visual models and systems in comparison to biological systems, blending the ideas of current scientific knowledge and biological vision"--...

Probabilistic And Biologically Inspired Feature Representations

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Author by : Michael Felsberg
Languange Used : en
Release Date : 2018-05-29
Publisher by : Morgan & Claypool Publishers

ISBN : 9781681730240

Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks....

Bio Inspired Neurocomputing

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Author by : Akash Kumar Bhoi
Languange Used : en
Release Date : 2020-07-21
Publisher by : Springer Nature

ISBN : 9789811554957

This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchers as well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics....

Biologically Inspired Machine Vision

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Author by : Aristeidis Tsitiridis
Languange Used : en
Release Date : 2013
Publisher by :

ISBN : OCLC:862754252

This thesis summarises research on the improved design, integration and expansion of past cortex-like computer vision models, following biologically-inspired methodologies. By adopting early theories and algorithms as a building block, particular interest has been shown for algorithmic parameterisation, feature extraction, invariance properties and classification. Overall, the major original contributions of this thesis have been: 1. The incorporation of a salient feature-based method for semantic feature extraction and refinement in object recognition. 2. The design and integration of colour features coupled with the existing morphological-based features for efficient and improved biologically-inspired object recognition. 3. The introduction of the illumination invariance property with colour constancy methods under a biologically-inspired framework. 4. The development and investigation of rotation invariance methods to improve robustness and compensate for the lack of such a mechanism in the original models. 5. Adaptive Gabor filter design that captures texture information, enhancing the morphological description of objects in a visual scene and improving the overall classification performance. 6. Instigation of pioneering research on Spiking Neural Network classification for biologically-inspired vision. Most of the above contributions have also been presented in two journal publications and five conference papers. The system has been fully developed and tested in computers using MATLAB under a variety of image datasets either created for the purposes of this work or obtained from the public domain....

Biological And Computer Vision

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Author by : Gabriel Kreiman
Languange Used : en
Release Date : 2021-02-04
Publisher by : Cambridge University Press

ISBN : 9781108759267

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing visual systems....

Biologically Inspired Efficiencies In Computer Vision And Audition

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Author by : Mohammadkazem Ebrahimpour
Languange Used : en
Release Date : 2020
Publisher by :

ISBN : OCLC:1199025130

Computer perception is one of the fundamental problems in artificial intelligence. Given an image or a recorded audio, a human can quickly recognize and detect objects based on image or sound or both. In computer vision, Object Detection is concerned with recognizing objects in images and drawing a bounding box around them. Researchers have been working on developing algorithms to recognize, detect, and segment objects/scenes in images for decades. Numerous challenges make these problems significantly challenging in real-world scenarios, since objects usually appear in different conditions, such as viewpoints, scales, and with background noise, and they even may deform into different shapes, parts, or poses. Real-time object detection has many important applications, such as autonomous driving cars and video surveillance. In this dissertation, we approach visual understanding in the following ways: First, we utilize implicit information in trained neural networks to localize all objects of interest in an image using a sensitivity analysis approach. Second, we introduce a novel framework for object detection called "Ventral- Dorsal" Neural Networks, inspired by the structure of the human brain. Third, we expand the Ventral-Dorsal framework, focusing on attaining real-time performance needed for online applications. Forth, we compare human attention with deep neural network attention algorithms in order to understand whether neural network attention matches human attention. Also, auditory perception is crucial in artificial intelligence systems. Until recently, auditory object recognition pipelines were in need of substantial hand engineering for feature extraction. Engineered features need to be tuned for every individual problem. Also, some popular feature extraction methods are time-consuming, limiting real-time applications. Here we attempt to avoid these problems using end-to-end training. Due to the recent improvements in deep neural networks, we are able to eliminate feature learning by optimizing feature extraction and classification jointly in one network. In this dissertation, we approach the auditory object recognition problem in the following ways: we proposed a novel "end-to-end" deep neural network architecture that takes raw audio as input and maps it to class labels. We also applied our proposed architecture to a new dataset of infant vocalization sounds for further investigation...

Computational Vision And Bio Inspired Computing

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Author by : S. Smys
Languange Used : en
Release Date : 2021-06-14
Publisher by : Springer Nature

ISBN : 9789813368620

This book includes selected papers from the 4th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2020), held in Coimbatore, India, from November 19 to 20, 2020. This proceedings book presents state-of-the-art research innovations in computational vision and bio-inspired techniques. The book reveals the theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary optimization and big data modeling and management that make use of effectual computing processes in the bio-inspired systems. As such it contributes to the novel research that focuses on developing bio-inspired computing solutions for various domains, such as human–computer interaction, image processing, sensor-based single processing, recommender systems and facial recognition, which play an indispensable part in smart agriculture, smart city, biomedical and business intelligence applications....