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|Title:||BTS information signs analysis based on image compression and classification for virtual blind man multimedia guidance system||Authors:||Kantawong, S.
|Keywords:||BNN;BTS Information Signs Guidance;DWT;IDWT;Principle Component Analysis (PCA);Shape Analysis;SPTA;VQ||Issue Date:||2009||Publisher:||University of the Thai Chamber of Commerce||Source:||S. Kantawong, T. Phanprasit, S. Kiattisin (2009) BTS information signs analysis based on image compression and classification for virtual blind man multimedia guidance system., 1119-1124.||Conference:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||Abstract:||This paper presents the information signs compression and classification in visionbased guidance system that apply for Bangkok Train Sky (BTS) virtual blind man tourism navigation system which can have two main roles that first for signs compression and next for signs classification. The algorithms are described here take an advantage of information sign features that their colors and shapes are very different from natural environments. The system are mainly divided into three parts, first forimage compression that are proposed the enhanced image coding algorithms called principle component analysis (PCA) plus wavelet transform with system error compensate via vector quantization techniques (VQ). The small bit rates for highspeed data transmission with a small space for data storage are required on WiFi channel. Simultaneously, the peak signal to noise ratio (PSNR) has to be maintained. The shape analysis with a continuous thinning algorithms and image binary data encodingalgorithm are used in second part for reduced the sized of data and can be representatives for suitable features data to classify.Finally the back propagation Neural Network (BNN) techniques are used in image recognition and classification the BTS signs. By applying the proposed method, performance has been improved which indicated by lower bit rate and better PSNR, while classify results are satisfied. Some results from the real BTS station scenes are shown that system performance can work well and would be train the virtual blind man guidance to perform some task at that place.||URI:||https://scholar.utcc.ac.th/handle/6626976254/3628||Rights:||This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.|
|Appears in Collections:||RSO: Conference Papers|
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