The Highly Lose Image Inpainting Method Based on Human Vision

[[abstract]]Currently, noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to restore damaged image data effectively, we propose a novel image inpainting technique based on wavelet transformation. The primary feature of our proposed technique is to separate the given image into two principal components which encompass image texture and color respectively. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The experimental results were perfect, even if the distortion portions of the repaired images were higher than 90%[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20060901~20060901[[conferencelocation]]Beijing, Chin

Use of a Self-Learning Neuro-Fuzzy System for Syllabic Labeling of Continuous Speech

[[abstract]]For reducing the requirement of large memory and minimizing computation complexity in a large-vocabulary continuous speech recognition system, speech segmentation plays an important role. In this paper, the authors formulate the speech segmentation as a two-phase problem. Phase 1 (frame labelling) involves labeling frames of speech data. Frames are classified into three types: (1) silence; (2) consonants; and (3) vowels according to two segmentation features. In phase 2 (syllabic unit segmentation) the authors apply the concept of transition states to segment continuous speech data into syllabic units based on the labeled frames. The novel class of hyperrectangular composite neural networks (HRCNs) is used to cluster frames. The HRCNNs integrate the rule-based approach and neural network paradigms, therefore, this special hybrid system may neutralize the disadvantages of each alternative. The parameters in the trained HRCNNs are utilized to extract both crisp and fuzzy classification rules. Four speakers' continuous reading-rate Mandarin speech are given to illustrate the proposed two-phase speech segmentation model. In the authors' experiments, the performance of the HRCNNs is better than the “distributed fuzzy rule” approach based on the comparisons of the number of rules and the correct recognition rate[[conferencetype]]國際[[conferencedate]]19950320~19950324[[conferencelocation]]Yokohama, Japa

Handwritten numeral recognition using fuzzy agglomeration kohonen clustering networks

[[notice]]本書目待補正[[conferencetype]]國

Minutiae Verification with Principal Component Analysis For Fingerprint Image Postprocessing

[[abstract]]Minutiae are the two most prominent and well-accepted classes of fingerprint features arising from local ridge discontinuities: ridge endings and ridge bifurcations. However, the preprocessing stage doesn't eliminate all possible defects in the original gray-level image and the orientation estimation in a poor image is extremely unreliable. In order to further eliminate the false minutiae caused by low quality, a minutiae verification mechanism is proposed to improve the identification performance. The minutiae verification mechanism uses the concept of eigen-codebook to find the optimal projection bases for true minutiae regions and false minutiae regions. Experimental results show that the performance is improved efficiently even less training data.[[sponsorship]]銘傳大學資訊傳播工程學系; 中華民國影像處理與圖形識別學會[[conferencetype]]國內[[conferencedate]]20030817~20030819[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]金門縣, 臺

[[alternative]]利用遺傳演算法以分散式模糊規則為基礎的語音切割的前處理

[[abstract]]Most of the speech segmentation works are based on the thresholds of parameters to segment the speech data into phonemic units or syllabic units. In this paper, we formulate the threshold decision as a clustering problem. Feature parameters extracted from the analysis frame are clustered into three types: silence, consonants, and vowels. Distributed fuzzy rules which have been used in clustering the numerical data are used for this task. The distributed fuzzy rules, which do not need many training data, have good performance in clustering problems and are beneficial for clustering the features of speech data. Such a method, however, has many fuzzy if-then rules. So, we propose a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. Effectiveness of this approach has been substantiated by classification experiments for continuous radio news speech samples uttered by two females and two males[[conferencetype]]國際[[conferencedate]]19970701~19970705[[iscallforpapers]]Y[[conferencelocation]]Barcelona, Spai

A Fingerprint Identification System Based on Fuzzy Encoder and Neural Network

[[abstract]]The extracting correct minutiae from fingerprint images is very important steps in automatic fingerprint identification system. However, the presence of noise in poor-quality images will cause many extraction faults, such as the dropping of true minutiae and inclusion of false minutiae. The ridge minutiae in poor-quality fingerprint images are not always well defined and cannot be correctly detected. Because fingerprint patterns are fuzzy in nature and ridge endings are changed easily by scars, we try to only use ridge bifurcation as fingerprints minutiae and also design a “fuzzy feature image” encoder by using cone membership function to represent the structure of ridge bifurcation features extracted from fingerprint. Nowadays, most fingerprint identification systems are based on precise mathematical models, but they cannot handle such faults properly. As we know, human beings are good at recognizing fingerprint pattern. Then, we integrate the fuzzy encoder with back-propagation neural network (BPNN) as a recognizer which has variable fault tolerances for fingerprint recognition. Therefore, a human-like method is applied. This paper presents an adaptive fuzzy logic and neural network method which has variable fault tolerance. And our experimental results have shown that this fingerprint identification method is robust, reliable, efficiency and our algorithm is faster.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]TW

[[alternative]]Image Compression Coding Via Adaptive Classified of Wavelet Coefficients Set

[[abstract]]本文提出以小波轉換為基礎之一套新的影像編碼技術，是利用經由小波轉換係數 值有接近於灰階度零的效果，及其擁有正負二值的類型將小波係數以四個係數為 一組的小波係數組做為編碼的依據。這種設計方式可避免因訓練產生區域性的最 佳化。其訓練方式是利用歐幾里得最小距離，經由反覆訓練產生最佳的小波係數 組。而這些小波係數組再利用Huffman編碼法進行編碼。實驗結果證明本方法與 傳統的小波轉換編碼技術比較，可達到更高品質、更低位元率的影像壓縮。[[abstract]]Image compression coding has high potential on some applications in our life. It can be used in a wide range of application. Such as the storage of the digital camera, multimedia transmission in network, image database, etc. As a result, in the recent years many researchers have proposed relevant methods and systems for image compression. In this paper, we proposed the image compression system is based on Discrete Wave-let Transform (DWT). The wavelet coefficients is divide into Wavelet Coefficient Set (WCS) that will be coded. This system is composed of three parts: In the first part is image spatial transform. It extract wavelet coefficient by mutual orthogonal feature with wavelet function and scaling function of DWT. In the second part is training WCS. It can be replace original coefficient by trained coefficient set. In the third stage is coding method. It is coded by huffman coding to replace with original one to reduce to the bit rate of compression. In this system, we use 3 popular testing graphics for compression. Compare with the other compression method in PSNR's and bpp's, it has the better effect, too. We use subband with rim detection by DWT in application. It put to use the advantage sufficiently, and extract better effect, too.[[sponsorship]]銘傳大學; 國際教育兼管理科學基金會[[conferencetype]]國際[[conferencedate]]20000323~20000323[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]桃園縣, 臺

Integration of FCM and agglomeration with kohonen clustering networks

[[notice]]本書目待補正[[conferencetype]]國