HNC developed a unique context vector approach to image retrieval in image Content Addressable Retrieval System (ICARS). The basis for this approach is the context vector approach to image representation. A context ve...
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ISBN:
(纸本)0819427470
HNC developed a unique context vector approach to image retrieval in image Content Addressable Retrieval System (ICARS). The basis for this approach is the context vector approach to image representation. A context vector is a high dimensional vector of real numbers, derived from a set of features that are useful in discriminating between images in a particular domain. The image features are trained based upon the constrained two dimensional self-organizing learning law. The image context vector encodes both intra-image features and inter-image relationship. The similarity in the directions of the context vectors of a pair of images indicates their similarity of content. The context vector approach to image representation simplifies the image and retrieval indexing problem because simple Euclidean distance measurements between sets of context vectors are used as a measure of similarity.
Some important ideas of image recognition using neural network based on multi-valued neurons are being developed in this paper. We are going to discuss the recognition of color images, which is reduced to recognition ...
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ISBN:
(纸本)0819439835
Some important ideas of image recognition using neural network based on multi-valued neurons are being developed in this paper. We are going to discuss the recognition of color images, which is reduced to recognition of gray-scale images. An approach, which has been developed, is illustrated by simulation results. Recognition of distortion (blur) types, distortion parameters and recognition of images with distorted training set using the same neural network is also considered. At this time Gaussian blur and motion blur were taken as distortions. This part of work is also illustrated by simulation results.
In digital holography applications, the position of the object, which is in the system, substantially affects the quality and clarity of three dimensional reconstructed image obtained from hologram. Therefore, obtaini...
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For a one-layered-feedback neural network e.g., a Hopfield net, containing discrete sign-function neurons, the nonlinear properties of this network can be studied very efficiently using simple discrete mathematics. Th...
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ISBN:
(纸本)0819444081
For a one-layered-feedback neural network e.g., a Hopfield net, containing discrete sign-function neurons, the nonlinear properties of this network can be studied very efficiently using simple discrete mathematics. This paper summarizes the discrete-formulation of the problem as a matrix difference equation, the simple iterative method of solving this difference equation and the derivation of the major anomalous properties of the system from the solutions. These anomalous properties include, eigen-state storage, associative storage, domain of attraction, content-addressable recall, fault-tolerant recall, capacity of storage, binary oscillating states, limit-cycles in the state space, and noise-sensitive input states. The physical origin and the systematic trend of the derivation of these properties are easily seen in the numerical examples given.
A key function for an autonomous robot is recognition and tracking of pertinent objects observed through a camera. Real-time interpretation of camera images is critical to a robot's interaction with the physical w...
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ISBN:
(纸本)9780889867093
A key function for an autonomous robot is recognition and tracking of pertinent objects observed through a camera. Real-time interpretation of camera images is critical to a robot's interaction with the physical world. This paper presents preliminary results in using artificialneuralnetworks (ANN) to examine the pixels of an image. While processing all pixels through ANNs would jeopardize the real-time processing requirements, the accuracy gained facilitates use of algorithms that only need to examine a fraction of the pixels composing an image in order to recognize and track the objects of interest. Presented herein is a test environment and problem statement, description of a methodology that employed ANNs to address the problem, and details of the algorithms that interpret images. The results show a high degree of success within the domain of the test environment.
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text...
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ISBN:
(纸本)9781577358008
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neuralnetworks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neuralnetworks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.
We present a biologically-inspired early vision network that is well-suited to highly active and responsive vision platforms. The network exploits normally undesirable camera motion as a necessary step in detecting im...
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ISBN:
(纸本)0819412015
We present a biologically-inspired early vision network that is well-suited to highly active and responsive vision platforms. The network exploits normally undesirable camera motion as a necessary step in detecting image contrast. It also detects visual motion, producing distinctive signals from which useful image motion parameters are extracted. The network remains sensitive over a very wide dynamic range of inputs, and has self-calibrating properties that make it amenable to analog VLSI implementation. The results also support the hypothesis that vertebrate cones function primarily as detectors of contrast and motion, rather than intensity. Experiments verify that naturally occurring jitter in a motor-mounted camera, instead of being avoided, can be exploited in early visual processing.
The hybrid evolutionary algorithm is used for image registration formulated as an optimization problem of finding a vector of parameters minimizing the difference between images. The reproduction phase of the algorith...
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ISBN:
(纸本)081944815X
The hybrid evolutionary algorithm is used for image registration formulated as an optimization problem of finding a vector of parameters minimizing the difference between images. The reproduction phase of the algorithm is enhanced with a two-level operation of local correction performed on the best genes in the reproduction pool. Random search is performed in the neighborhood of a gene until the time interval reaches a pre-set threshold. If the gene still retains its position in the pool, a refined multi-step search is performed using the Downhill simplex method. In order to improve the computational performance of the local search, local response analysis is used in the following way. All domains of the given reference image are classified according to their local response to a unit variation of the parameter vector. The classification scheme is based on a self-organizing neural network. During the local correction of the reproduction pool, the step size in the Downhill simplex search is modified according to the class of the image domain.
The paper presents an approach to localize human body joints in 3D coordinates based on a single low resolution depth image. First a framework to generate a database of 80k realistic depth images from a 3D body model ...
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ISBN:
(纸本)9788362065301
The paper presents an approach to localize human body joints in 3D coordinates based on a single low resolution depth image. First a framework to generate a database of 80k realistic depth images from a 3D body model is described. Then data preprocessing and normalization procedure, and DNN and MLP artificialneuralnetworks architectures and training are presented. The robustness against camera distance and image noise is analysed. Localization accuracy for each joint is reported and application for low resolution and large distance pose estimation is proposed. A very fast regression on body joints locations in 3D space is achieved, even in case of sensor noise, large distance and reaching off the screen.
作者：
Wang, SJChinese Acad Sci
Inst Semicond Artificial Neural Networks Lab Beijing 100083 Peoples R China
Digitization is the main feature of modern Information Science. Conjoining the digits and the coordinates, the relation between Information Science and high-dimensional space is consanguineous, and the information iss...
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ISBN:
(纸本)0780394224
Digitization is the main feature of modern Information Science. Conjoining the digits and the coordinates, the relation between Information Science and high-dimensional space is consanguineous, and the information issues are transformed to the geometry problems in some high-dimensional spaces. From this basic idea, we propose Computational Information Geometry (CIG) to make information analysis and processing. Two kinds of applications of CIG are given, which are blurred image restoration and pattern recognition. Experimental results are satisfying. And in this paper, how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is also introduced. Lots of the algorithms have been realized using software.
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