• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.370-380
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• 2002

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.103-114
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• 2005

This paper presents a content-based image retrieval (CBIR) method using the combination of color and texture features. As a color feature, a color autocorrelogram is chosen which is extracted from the hue and saturation components of a color image. As a texture feature, BDIP(block difference of inverse probabilities) and BVLC(block variation of local correlation coefficients) are chosen which are extracted from the value component. When the features are extracted, the color autocorrelogram and the BVLC are simplified in consideration of their calculation complexity. After the feature extraction, vector components of these features are efficiently quantized in consideration of their storage space. Experiments for Corel and VisTex DBs show that the proposed retrieval method yields 9.5% maximum precision gain over the method using only the color autucorrelogram and 4.0% over the BDIP-BVLC. Also, the proposed method yields 12.6%, 14.6%, and 27.9% maximum precision gains over the methods using wavelet moments, CSD, and color histogram, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5522-5536
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• 2017

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1748-1759
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• 2018

Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the $5{\times}5$ neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.

• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.33-38
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• 2013

Image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes an efficient image descriptor which uses a local color and texture in the non-overlapped block images. To evaluate the performance of the proposed method, we assessed the retrieval efficiency in terms of ANMRR with common image dataset. The experimental trials revealed that the proposed algorithm exhibited a significant improvement in ANMRR, compared to Dominant Color Descriptor and Edge Histogram Descriptor.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1418-1429
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• 2014

In this paper we suggest a new two-wheelers detection algorithm using local cell features. The first, we propose new feature vector matrix extraction algorithm using the correlation two cells based on local cell histogram and shifting from the result of histogram of oriented gradients(HOG). The second, we applied new weighting values which are calculated by the modified histogram intersection showing the similarity of two cells. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.39-45
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• 2017

Conventional Color-Based Face Recognition Methods are Sensitive to Illumination Changes, and there are the Possibilities of Forgery and Falsification so that it is Difficult to Apply to Various Industrial Fields. In This Paper, we propose a Face Recognition Method Based on LBP(Local Binary Pattern) using the Depth Images to Solve This Problem. Face Detection Method Using Depth Information and Feature Extraction and Matching Methods for Face Recognition are implemented, the Simulation Results show the Recognition Performance of the Proposed Method.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.372-379
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• 2013

In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.179-186
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment and co-occurrence matrix features are calculated. For the defect classification, multilayer neural network is used. The proposed algorithm showed 15% error rate.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.