• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.578-591
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• 2016

In this paper, the fast encoding algorithm in High Efficiency Video Coding (HEVC) encoder was studied. For the encoding efficiency, the current HEVC reference software is divided the input image into Coding Tree Unit (CTU). then, it should be re-divided into CU up to maximum depth in form of quad-tree for RDO (Rate-Distortion Optimization) in encoding precess. But, it is one of the reason why complexity is high in the encoding precess. In this paper, to reduce the high complexity in the encoding process, it proposed the method by determining the maximum depth of the CU using a hierarchical clustering at the pre-processing. The hierarchical clustering results represented an average combination of motion vectors (MV) on neighboring blocks. Experimental results showed that the proposed method could achieve an average of 16% time saving with minimal BD-rate loss at 1080p video resolution. When combined the previous fast algorithm, the proposed method could achieve an average 45.13% time saving with 1.84% BD-rate loss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.86-97
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• 2011

This paper presents a novel algorithm to detect more accurate road lane with image sensor-based color classification and directional edge clustering. With treatment of road region and lane as a recognizable color object, the classification of color cues is processed by an iterative optimization of statistical parameters to each color object. These clustered color objects are taken into considerations as initial kernel information for color object detection and recognition. In order to improve the limitation of object classification using the color cues, the directional edge cures within the estimated region of interest in the lane boundary (ROI-LB) are clustered and combined. The results of color classification and directional edge clustering are optimally integrated to obtain the best detection of road lane. The characteristic of the proposed system is to obtain robust result to all real road environments because of using non-parametric approach based only on information of color and edge clustering without a particular mathematical road and lane model. The experimental results to the various real road environments and imaging conditions are presented to evaluate the effectiveness of the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.147-157
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• 2017

In recent years, Super-pixels have become very popular for use in computer vision applications. Super-pixel algorithm transforms pixels into perceptually feasible regions to reduce stiff features of grid pixel. In particular, super-pixels are useful to depth estimation, skeleton works, body labeling, and feature localization, etc. But, it is not easy to generate a good super-pixel partition for doing these tasks. Especially, super-pixels do not satisfy more meaningful features in view of the gestalt aspects such as non-sum, continuation, closure, perceptual constancy. In this paper, we suggest an advanced algorithm which combines simple linear iterative clustering with fuzzy clustering concepts. Simple linear iterative clustering technique has high adherence to image boundaries, speed, memory efficient than conventional methods. But, it does not suggest good compact and regular property to the super-pixel shapes in context of gestalt aspects. Fuzzy similarity measures provide a reasonable graph in view of bounded size and few neighbors. Thus, more compact and regular pixels are obtained, and can extract locally relevant features. Simulation shows that fuzzy similarity based super-pixel building represents natural features as the manner in which humans decompose images.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1604-1611
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• 2007

In a process of insect footprint recognition, basic footprint segments should be extracted from a whole insect footprint image in order to find out appropriate features for classification. In this paper, we used a clustering method as a preprocessing stage for extraction of basic insect footprint segments. In general, sizes and strides of footprints may be different according to type and sire of an insect for recognition. Therefore we proposed an improved ART2 algorithm for extraction or basic insect footprint segments regardless of size and stride or footprint pattern. In the proposed ART2 algorithm, threshold value for clustering is determined automatically using contour shape of the graph created by accumulating distances between all the spots of footprint pattern. In the experimental results applying the proposed method to two kinds of insect footprint patterns, we could see that all the clustering results were accomplished correctly.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.235-244
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• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.487-494
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• 2013

Medical image watermarking has received extensive attention as wide security services in the healthcare information system. This paper proposes a blind medical image watermarking approach on the segmented gray-matter (GM) images by utilizing discrete wavelet transform (DWT) and discrete cosine transform (DCT) along with enhanced suppressed fuzzy C-means (EnSFCM) for the optimal selection of sub-blocks position to insert a watermark. Experimental results show that the proposed approach outperforms other methods in terms of peak signal to noise ratio (PSNR) and M-SVD. In addition, the proposed approach shows better robustness than other methods in normalized correlation (NC) values against several attacks, such as noise addition, filtering, JPEG compression, blurring, histogram equalization, and cropping.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.7
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• pp.833-842
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• 1998

The purpose of this study is to (1) investigate on the impulse purchase tendency of women who buy women's character clothing brands and (2) to clarify the relationship between the women's impulse purchase tendency and the marketing factors, and the brand image. The subjects were 203 women in their twenties and thirties who had purchased the women's character clothing brands in last six months living in Seoul. The data were collected by self-administered questionnaire and were analyzed by frequency, percentage, t-test, clustering analysis and MDS(Multi-Dimensional Scale). The results of the study were as follows: (1)66.0% of the women were categorized as the group of high-degree of impulse purchase tendency and 34.0% of the women were identified as the group of low-degree of impulse purchase tendency. (2) In identifying the relationship between the marketing factors and the impulse purchase tendency, the group of the high-degree of impulse purchase tendency was affected by the marketing factors such as new and fashionable products, status of display, advertising and discout selling. (3) Brand image was not statistically significant according to the impulse purchase tendency. As a whole, 'Obzee' was recognized as unique-style and sexy brand, 'Time' and 'Mine' were thought as the reasonable-prince and basic-style brands, 'Deco' were clarified as the intellectual-image brand, and 'Ego' and 'Telegraph' were revealed as high-price-image brands.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1104-1113
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• 2015

In this paper, we propose Two-Dimensional Robust Face Recognition System Realized with the Aid of Facial Symmetry with Illumination Variation. Preprocessing process is carried out to obtain mirror image which means new image rearranged by using difference between light and shade of right and left face based on a vertical axis of original face image. After image preprocessing, high dimensional image data is transformed to low-dimensional feature data through 2-directional and 2-dimensional Principal Component Analysis (2D)2PCA, which is one of dimensional reduction techniques. Polynomial-based Radial Basis Function Neural Network pattern classifier is used for face recognition. While FCM clustering is applied in the hidden layer, connection weights are defined as a linear polynomial function. In addition, the coefficients of linear function are learned through Weighted Least Square Estimation(WLSE). The Structural as well as parametric factors of the proposed classifier are optimized by using Particle Swarm Optimization(PSO). In the experiment, Yale B data is employed in order to confirm the advantage of the proposed methodology designed in the diverse illumination variation