• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.63-66
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• 1996

In this paper, we propose a new content-based color image retrieval algorithm. The algorithm makes use of two features; colors as global features and block classification results as local features. More specifically, we obtain R, G, B color histograms and classify nonoverlapping small image blocks into texture, monotone, and various edges, then using these histograms and classification results were make a similarity measure. Experimental results show that retrieval rate of the proposed algorithm is higher than the previous method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.81-89
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• 1997

In this paper, we propose a new image retrieval algorithm using the block classification. More specifically, we classify nonoverlappint small image blocks into texture, monotone, and various edges. Using these classification results and the RGB color histogram, we propose a new similarity measure which considers both local and global fretures. According to our experimental results using 232 color images, the retrieval efficiencies of the proposed and the previous methods were 0.610 and 0.522, respectively, which implies that the proposed algorithm yields better performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.592-601
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• 2002

A postprocessing method for blocking artifact reduction in block coded images is presented. The proposed method consists of classification, adaptive inter-block filtering, and intra-block filtering. First, each block is classified as one of seven classes according to the characteristics of 8x8 DCT coefficients. Then each block boundary is faltered by adaptive inter-block filters, which use the block classes. Finally to the blocks which are classified as edge block classes, intra-block filtering is performed. Experimental tests produced that the proposed method gives better results than the convectional methods from both a subjective and an objective viewpoint.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.3
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• pp.149-157
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• 2017

In this paper, we propose BCMR (Block Classification with Monitor and Restriction) to ensure the isolation and to reduce the interference of blocks between a garbage collection and a wear leveling. The proposed BCMR monitors an endurance variation of blocks during the garbage collection and detects hot blocks by making a restriction condition based on this information. The proposal induces a block classification by its update frequency for the garbage collection and the wear leveling, so we will get a prolonged lifetime of NAND flash memory systems. In a performance evaluation, BCMR prolonged the lifetime of NAND flash memory systems by 3.95%, on average and reduced a standard deviation per block by 7.4%, on average.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.179-185
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• 2019

This paper proposes a block-type classification based image binarization for the implementation of the low-power feature extraction algorithm. The proposed method can be implemented with threshold value re-use technique approach when the image divided into $64{\times}64$ macro blocks size and calculating the threshold value for each block type only once. The algorithm is validated based on quantitative results that only a threshold value change rate of up to 9% occurs within the same image/block type. Existing algorithms should compute the threshold value for 64 blocks when the macro block is divided by $64{\times}64$ on the basis of $512{\times}512$ images, but all suggestions can be made only once for best cases where the same block type is printed, and for the remaining 63 blocks, the adaptive threshold calculation can be reduced by only performing a block type classification process. The threshold calculation operation is performed five times when all block types occur, and only the block type separation process can be performed for the remaining 59 blocks, so 93% adaptive threshold calculation operation can be reduced.

• The Korean Journal of Applied Statistics
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• v.2 no.1
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• pp.54-64
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• 1989

The paper by Kurkjian and Zelen(1963) introducted the Property A which related to a structural property of concordance matrix of the column incidence matrix. On the other hand, Paik(1985) showed the property of the concordance matrix, which has multinested block circulant pattern matrix, and this structural property was termed Property C by Paik(1985). This paper classifies the incomplete block designs according to the pattern of the concordence matrix which has multi-nested block circulant pattern. The purpose of this classification simplified the solution of reduced normal equation and plan of the design.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.3
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• pp.391-406
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• 1993

The purpose of this study was to utilize of apparel CAD System by the classification of the basic pattern block in pattern making process. Gerber AM-300 CAD System was used for this study. The results form this study were as follows; 1. New shirts block were developed. 2. The sensory test was applied to evaluate the new shirts block for women by comparing it with Japanese Bunka shirts blouse pattern making method. According to a statistical analysis of the result of the 20 items on the questionnaire, the 19 items showed significant difference(${\alpha}{\leq}0.01$)between the two, with the new shirts block having higher scores. 3. A basic pattern block was selected by the design sketch. 4. P/D/S were enabled to be constructed directly from a block pattern. The drawing, deletion. duplication, and movement of all points and lines in the pattern might be made freely, and the split, pivot, and movement of the pattern, and the attachment of two patterns were possible. 5. Automatic grading of finished pattern have been developed by creation and modification of grading rules of block pattern.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.568-572
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• 2006

This paper presents the device of recognizing image of the studded paving blocks, transmitting, the information by vibration to a visually handicapped person. Usually the blind uses the walking stick to recognize the studded paving block. This research uses a PCA (Principal Component Analysis) based image processing approach for recognizing the paving blocks. We classify the studded paving blocks into 5 classes, that is, vertical line block, right-declined line block, left-declined line block, dotted block and flat block. The 8 images for each of 5 classes are captured for each block by 112*120 pixels, then the eigenvectors are obtained in magnitude order of eigenvectors by using principal component analysis. The principal components for images can be calculated using projection of transformation matrix composed of eigenvectors. The classification has been executed using Euclidean's distance, so the block having minimum distance with a image is chosen as matched one. The result of classification is transmitted to the blind by electric vibration signals with different magnitudes and frequencies.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.102-112
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• 2012

In this paper, we propose a texture classification using wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features with WPCA (whitened principal component analysis) classifier. In the proposed method, the wavelet transform is first applied to a query image. The BDIP and BVLC operators are next applied to the wavelet subbands. Global moments for each subband of BDIP and BVLC are then computed and fused into a feature vector. In classification, the WPCA classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the query feature vector. Experimental results show that the proposed method yields excellent texture classification with low feature dimension for test texture image DBs.