Image Processing In The Human Visual System

Author : Li-Ming Chen
Publisher :
ISBN 13 :
Total Pages : 104 pages
Book Rating : 4.:/5 (128 download)

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Download or read book A Study on Image Processing Method Combined with Real Human Visual System written by Li-Ming Chen and published by . This book was released on 2021 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: In image processing technology, the human visual system model (HVS model) is a simplified method when technology faces such complex problems as biology and psychology. The HVS model will continue to improve as we understand the real vision system. However, there are some problems because of our insufficient understanding of the real visual system, which further neglects important features. In the situation where important features are ignored, the problems that can be easily distinguished in real vision in the prior art become difficult and complicated. In order to further understand the real visual system, this dissertation proposes basic improvement methods for some outdoor fire alarm systems and trademark image retrieval systems based on the human eye recognition pattern proposed by Gestalt psychologist K. Koffka. In the existing outdoor fire alarm systems, the image fire detection system, which is not restricted by the terrain range, is used. However, after the fire detection system detects and locates the fire, a responsible firefighter still needs to assist in observing whether it is a real. The reason for this is that in the real human visual system, we observe not only fire but also some other useful information. Fire and other useful information human experience are enough to accurately predict whether the detected fire will deteriorate. Fire triangle, coming from human experience, includes three ingredients, namely : oxygen, heat, and fuel, that are required for a fire to burn, the three elements of oxygen, fuel and heat are the important elements that make up the fire. In fire-related work, after the fire triangle system is enlarged in time and space, the corresponding three elements are also transformed into climate, vegetation and ignition. Removal of climate is a feature that cannot be captured by ordinary cameras, and vegetation and ignition are features that can often be obtained. However, existing fire detection systems cannot take vegetation features into consideration. The main reason is that the disturbing factors of the vegetation feature are very large. This dissertation is based on one of the feature of Convolutional Neural Network (CNN) in deep learning. This feature is that the neural network has a certain resistance to environmental changes. This dissertation proposes a fire alarm system similar to human vision. The proposed system performs transfer learning and system testing on the image data reclassified by CNN using the proposed method. The test results show that the proposed method reduces the false positive rate (FPR) of the past system from 40.47% to 4.15% when false negative (FN) is 0. This result confirms that the reclassification of the proposed method can indeed enable the existing system to obtain fire alarm accuracy similar to that of the real human visual system. In the existing trademark image retrieval system, the mathematical model of trademark retrieval can effectively and quickly find similar trademarks. However, in the courts of various countries, many actual trademark judgments often fail to use these systems as the criteria for judging whether there is confusion between trademarks. The main reason is that although the mathematical model can find the features of the two trademarks is similar, it cannot explain the correlation between the used features and the real human visual system. Such a result makes the judge must use his own subjective cognition as the criterion of judgment, and make the judgment deviate from justice. Based on K. Koffka's seven-oriented explanation of the recognition patterns of human eyes and the principles of trademark design, this dissertation proposes seven corresponding features. At the same time, we propose a new mathematical model to implement these seven features, and use the database to test. The experimental results show that the seven-feature system proposed in this dissertation can not only accurately judge the similarity as the existing trademark image retrieval system, but also explain which features of the trademarks are similar. Through the distance of the features, it further explains why the real visual system considers the two trademarks to be the same or different. According to the HVS model, this dissertation proposes two systems in different fields. In the experimental tests, this dissertation proves that the proposed HVS model system can effectively assist humans, and link image processing, visual psychology, and trademark design to achieve technologies that enhance human well-being.