A VISUAL MODEL FOR PATTERN RECOGNITION

1992 ◽  
Vol 03 (supp01) ◽  
pp. 31-39
Author(s):  
Luigi Stringa
Keyword(s):  
Pattern Recognition ◽  
Visual System ◽  
Human Visual System ◽  
General Model ◽  
Recognition System ◽  
Experimental Results ◽  
Visual Model ◽  
Optical Recognition ◽  
Processing Networks

A general model for an optical recognition system capable of simultaneous recognition of patterns at different resolution levels is outlined. The model is based on two hierarchic stages of processing networks and presents interesting analogies with the human visual system. Illustrative applications and preliminary experimental results are also briefly discussed.

scholarly journals Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

2016 ◽  
Vol 24 (1) ◽  
pp. 143-182 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Genetic Programming ◽  
Visual System ◽  
Image Classification ◽  
Human Visual System ◽  
Binary Classification ◽  
Programming Approach ◽  
Data Sets ◽  
New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

Digital Watermarking Payload Estimation Based on Human Visual System

2013 ◽  
Vol 798-799 ◽  
pp. 785-789
Author(s):  
Na Na Zhang ◽  
Jia Fa Mao ◽  
Jing Yin ◽  
Xiao Fang Yang
Keyword(s):  
Visual System ◽  
Digital Watermarking ◽  
Human Visual System ◽  
Estimation Method ◽  
Spatial Domain ◽  
Matlab Simulation ◽  
Simulation Experiments ◽  
Visibility Function ◽  
Visual Model ◽  
Maximum Payload

This paper proposes the estimation method for the maximum payload on spatial domain, concentrates on digital watermarking payload in the spatial domain image, on the constraint of perceptual invisibility research, the influence under the factors in Human Visual System. The maximum payload is influenced by the factors which include the size of image, the brightness masking, contrast masking and texture masking of the image. with such as noise visibility function visual model, gets the just noticeable different value to calculate the payload of the image, finally we get the watermarking payload, test and verify it with Matlab simulation experiments.

Ant-Inspired Visual Saliency Detection in Image

2011 ◽  
pp. 263-272
Author(s):  
Jing Tian ◽  
Weiyu Yu
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
Experimental Results ◽  
Superior Performance ◽  
Local Phase ◽  
Visual Saliency Detection ◽  
Pheromone Information

Visual saliency detection aims to produce saliency map of images via simulating the behavior of the human visual system (HVS). An ant-inspired approach is proposed in this chapter. The proposed approach is inspired by the ant’s behavior to find the most saliency regions in image, by depositing the pheromone information (through ant’s movements) on the image to measure its saliency. Furthermore, the ant’s movements are steered by the local phase coherence of the image. Experimental results are presented to demonstrate the superior performance of the proposed approach.

Technical Vision Model of the Visual Systems for Industry Application

2021 ◽  
pp. 288-310
Author(s):  
Oleg Sytnik ◽  
Vladimir Kartashov
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Visual System ◽  
Mathematical Models ◽  
Human Visual System ◽  
Vision System ◽  
Vision Systems ◽  
Industrial Systems ◽  
Visual Systems ◽  
Technical Vision

The problems of highlighting the main informational aspects of images and creating their adequate models are discussed in the chapter. Vision systems can receive information about an object in different frequency ranges and in a form that is not accessible to the human visual system. Vision systems distort the information contained in the image. Therefore, to create effective image processing and transmission systems, it is necessary to formulate mathematical models of signals and interference. The chapter discusses the features of perception by the human visual system and the issues of harmonizing the technical characteristics of industrial systems for receiving and transmitting images. Methods and algorithms of pattern recognition are discussed. The problem of conjugation of the characteristics of the technical vision system with the consumer of information is considered.

A Wavelet-Based Watermarking Algorithm Using the Human Visual System

2012 ◽  
Vol 424-425 ◽  
pp. 304-308
Author(s):  
Yu Feng Chen ◽  
Gang Yin
Keyword(s):  
Visual System ◽  
Frequency Band ◽  
Human Visual System ◽  
Low Frequency ◽  
Jpeg Compression ◽  
Experimental Results ◽  
Frequency Bands

A wavelet based multiresolution watermarking method using the human visual system (HVS) is proposed. The watermark is added to the large coefficients at the middle frequency bands and low frequency band of the DWT of an image. The experimental results show that the proposed method is robust for some common image distortions, such as cutting, filtering and the JPEG compression

Performance Optimization of DWT-Based Image Watermarking Using Genetic Algorithms

2009 ◽  
pp. 210-221
Author(s):  
Ali Al-Haj ◽  
Aymen Abu-Errub
Keyword(s):  
Genetic Algorithms ◽  
Visual System ◽  
Human Visual System ◽  
Performance Optimization ◽  
Image Watermarking ◽  
Spatial Localization ◽  
Theoretical Models ◽  
Experimental Results ◽  
Wavelets Transform ◽  
Discrete Wavelets

The excellent spatial localization, frequency spread, and multi-resolution characteristics of the discrete wavelets transform (DWT), which are similar to the theoretical models of the human visual system, facilitated the development of many imperceptible and robust DWT-based watermarking algorithms. However, there has been extremely few proposed algorithms on optimized DWT-based image watermarking that can simultaneously provide perceptual transparency and robustness Since these two watermarking requirements are conflicting, in this paper we treat the DWT-based image watermarking problem as an optimization problem, and solve it using genetic algorithms. We demonstrate through the experimental results we obtained that optimal DWT-based image watermarking can be achieved only if watermarking has been applied at specific wavelet sub-bands and by using specific watermarkamplification values.

HCNN: A Neural Network Model for Combining Local and Global Features Towards Human-Like Classification

2015 ◽  
Vol 30 (01) ◽  
pp. 1655004 ◽  
Author(s):  
Tielin Zhang ◽  
Yi Zeng ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Image Classification ◽  
Network Model ◽  
Neural Network Model ◽  
Human Visual System ◽  
Experimental Results ◽  
Natural Images ◽  
Global Features ◽  
Local And Global Features

Brain-inspired algorithms such as convolutional neural network (CNN) have helped machine vision systems to achieve state-of-the-art performance for various tasks (e.g. image classification). However, CNNs mainly rely on local features (e.g. hierarchical features of points and angles from images), while important global structured features such as contour features are lost. Global understanding of natural objects is considered to be essential characteristics that the human visual system follows, and for developing human-like visual systems, the lost of consideration from this perspective may lead to inevitable failure on certain tasks. Experimental results have proved that well-trained CNN classifier cannot correctly distinguish fooling images (in which some local features from the natural images are chaotically distributed) from natural images. For example, a picture that is composed of yellow–black bars will be recognized as school bus with very high confidence by CNN. On the contrary, human visual system focuses on both the texture and contour features to form representation of images and would not mis-take them. In order to solve the upper problem, we propose a neural network model, named as histogram of oriented gradient (HOG) improved CNN (HCNN), that combines local and global features towards human-like classification based on CNN and HOG. The experimental results on MNIST datasets and part of ImageNet datasets show that HCNN outperforms traditional CNN for object classification with fooling images, which indicates the feasibility, accuracy and potential effectiveness of HCNN for solving image classification problem.

scholarly journals Visual Cryptography Scheme with Essential Participants

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 838 ◽  
Author(s):  
Peng Li ◽  
Liping Yin ◽  
Jianfeng Ma
Keyword(s):  
Integer Programming ◽  
Visual System ◽  
Human Visual System ◽  
Programming Model ◽  
Visual Cryptography ◽  
Optimal Choice ◽  
Experimental Results ◽  
Secret Image ◽  
Visual Cryptography Scheme ◽  
Computational Resources

Visual cryptography scheme (VCS) shares a binary secret image into multiple shadows printed on transparencies. Stacking shadows can visually decode the secret image without computational resources. Specifically, a (k, n) threshold VCS ((k, n)-VCS) shares a secret image into n shadows, stacking any k shadows can reveal the secret image by human visual system, while any less than k shadows cannot decode any information regarding the secret image. In practice, some participants (essentials) play more important roles than others (non-essentials). In this paper, we propose a (t, s, k, n) VCS with essential participants (so called (t, s, k, n)-EVCS). The secret image is shared into n shadows with s essentials and n-s non-essentials. Any k shadows, including at least t essentials, can reveal the secret image. The proposed scheme is constructed from a monotonic (K, N)-VCS. The condition and optimal choice of (K, N)-VCS to construct (t, s, k, n)-EVCS are given by solving integer programming model. The experimental results are conducted to verify the feasibility of our scheme.

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