I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition: A literature survey

The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

Human and machine recognition of faces: a survey

https://open.bu.edu/bitstream/handle/2144/2070/91.015.pdf?sequence=1

Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system

https://dash.harvard.edu/bitstream/handle/1/29005173/Formulation%20of%20Functionalized%20PLGA-PEG.pdf?sequence=1

Formulation of functionalized PLGA-PEG nanoparticles for in vivo targeted drug delivery

A method and system for processing two input digital image signals, each being representative of a different two dimensional view of the same scene, to produce three processed image signals A center-surround shunt processor transforms each input image signal to produce a respective processed image signal Another center-surround shunt processor produces the third processed image signal from the first two processed image signals In the latter processor, one of the first two processed image signals is selected as a center image and the other is selected as a surround image The processor performs a computation associated with each pixel in the center image with data selected from the center image to form a central element for the computation and data selected from the surround image to form a surround element for the computation The result of each computation is a pixel of the third processed image signal The three processed image signals may be applied to the respective RGB inputs of an RGB display device

Method and apparatus for generating a synthetic image by the fusion of signals representative of different views of the same scene

We introduce an apparatus and methodology to support realtime color imaging for night operations. Registered imagery obtained in the visible through near IR band is combined with thermal IR imagery using principles of biological color vision. The visible imagery is obtained using a Gen III image intensifier tube optically coupled to a conventional CCD, while the thermal IR imagery is obtained using an uncooled thermal imaging array, the two fields of view being matched and imaged through a dichroic beam splitter. Remarkably realistic color renderings of night scenes are obtained, and examples are given in the paper. We also describe a compact integrated version of our system in the form of a color night vision device, in which the intensifier tube is replaced by a high resolution low-light sensitive CCD. Example CCD imagery obtained under starlight conditions is also shown. The system described here has the potential to support safe and efficient night flight, ground, sea and search & rescue operations, as well as night surveillance.

Color night vision: fusion of intensified visible and thermal IR imagery

Neural processing of targets in visible, multispectral IR and SAR imagery

Publisher Summary This chapter focuses on learning and recognizing 3D objects from multiple views in a neural system In this approach of designing a 3D object recognition system, clues from biology and psychology are taken to develop an architecture that supports the learning of representations for 3D objects from view sequences and allows subsequent experience to refine the learned representations The computations are cast as a set of simultaneous coupled differential equations, rather than as algorithmic procedures The implementation is modular in the sense that the sets of coupled equations (that is, networks) are interconnected so that information flows from one network to another via feed forward and feedback connections between them Similar learning principles can be realized by analog electronic devices The attributes of an object's appearance that are extracted by the network architecture are increasingly abstract as higher levels of the architecture are activated The portion of the image that is fixated is first reduced to a feature set, then the effects of position are eliminated, followed by the effects of orientation and scale, followed by the effects of foreshortening and per­spective (to some extent), and followed by the effects of the particular vantage point The computational load is distributed throughout the modules

Learning and Recognizing 3D Objects from Multiple Views in a Neural System

In our effort to develop a modular neural system for invariant learning and recognition of 3D objects, we introduce here a new module architecture called an aspect network constructed around adaptive axo-axo-dendritic synapses. This builds upon our existing system (Seibert & Waxman, 1989) which processes 20 shapes and classifies them into view categories (i.e., aspects) invariant to illumination, position, orientation, scale, and projective deformations. From a sequence of views, the aspect network learns the transitions between these aspects, crystallizing a graph-like structure from an initially amorphous network. Object recognition emerges by accumulating evidence over multiple views which activate competing object hypotheses.

/pdf/learning-aspect-graph-representations-from-view-sequences-4qy6a3yp0n.pdf

Learning Aspect Graph Representations from View Sequences

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