• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.243-248
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• 2011

In this paper, we propose a new method to extract palm lines and read it with simple techniques from one photo. We use morphological information and 8-directional contour tracking algorithm. From the digitalized image, we transform original RGB information to YCbCr color model which is less sensitive to the brightness information. The palm region is extracted by simple threshold as Y:65~255, Cb:25~255, Cr:130~255 of skin color. Noise removal process is then followed with morphological information of the palm such that the palm area has more than quarter of the pixels and the rate of width vs height is more than 2:1 and 8-directional contour tracking algorithm. Then, the stretching algorithm and Sobel mask are applied to extract edges. Another morphological information that the meaningful edges(palm lines) have between 10 and 20 pixels is used to exclude noise edges and boundary lines of the hand from block binarized image. Main palm lines are extracted then by labeling method. This algorithm is quite effective even reading the palm from a photographed by a mobile phone, which suggests that this method could be used in various applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2068-2086
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• 2011

Based on the quincunx sub-sampling grid, the New Interleaved Hierarchical INTerpolation (NIHINT) method is recognized as a superior pyramid data structure for the lossless and progressive coding of natural images. In this paper, we propose a new image interpolation algorithm, Edge Adaptive Hierarchical INTerpolation (EAHINT), for a further reduction in the entropy of interpolation errors. We compute the local variance of the causal context to model the strength of a local edge around a target pixel and then apply three statistical decision rules to classify the local edge into a strong edge, a weak edge, or a medium edge. According to these local edge types, we apply an interpolation method to the target pixel using a one-directional interpolator for a strong edge, a multi-directional adaptive weighting interpolator for a medium edge, or a non-directional static weighting linear interpolator for a weak edge. Experimental results show that the proposed algorithm achieves a better compression bit rate than the NIHINT method for lossless image coding. It is shown that the compression bit rate is much better for images that are rich in directional edges and textures. Our algorithm also shows better rate-distortion performance and visual quality for progressive image transmission.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.110-116
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• 2017

This paper presents an effective demosaicking algorithm for color filter array (CFA) images acquired from single-sensor devices based on directional interpolation and nonlocal properties of the image. We interpolate the G channel considering diagonal directions as well as horizontal and vertical directions, using a small number of pixels to reflect local properties of the image. Then, we overcome image degradations, such as zipper effects near edges and false colors, by applying nonlocal means (NLM) filtering to the interpolated pixels. R and B channels are reproduced by using directional interpolation with information of the reconstructed G channel and NLM filtering. Experimental results for various McMaster images with high saturation and color changes show that the proposed algorithm accomplishes high PSNR compared with conventional methods. Moreover, the proposed method demonstrates better subjective quality compared with existing methods in terms of reduction of quality degradation, like false colors, and preservation of the image structures, such as edges and textures.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.315-319
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• 2002

The study on shilla monumental inscription has been accomplished by many historians. However, the research on segmentation of monumental inscription image using digital image processing technique is not sufficient. The preprocessing using computer is needed for accurate interpretation of history. In this paper, A morphological filtering using directional information is presented. Directional filtering is effective in reducing noises and preserving edges. The opening and closing operations in the 1st stage are performed for the pixel is aligned to the vertical, horizontal and two diagonal directions. The Opening operation supresses the positive impulse noise while the closing operation the negative ones. Then Directional filter and post-processing are applied to the image. Experimental result shows outstanding performance for interpretation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2982-2988
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• 2014

The edge detection is a pre-processing of such as image segmentation, image recognition, etc, and many related studies are being conducted both in domestic and abroad. Representative edge detection methods are Sobel, Prewitt, Laplacian, Roberts and Canny edge detectors. Such existing methods are possible for superb detections of edges if edges are detected from videos without noises. However, for video degraded by the salt-and-pepper noise, the edge detection characteristic is shown to be insufficient due to the noise influence. Therefore, in this study, the area is separated as the top, down, left and right from the mask's center pixel first to acquire a superb edge detection characteristic from the video damaged by the salt-and-pepper noise. And the algorithm that detects the final edge by applying the directional mask on the assumed factor of mask that is obtained according to the result of determination for the noise status of representative pixel value of each area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.278-286
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• 2007

In this paper, an efficient algorithm is proposed for the interpolation of interlaced images. First of all, by efficiently estimating the directional spatial correlations of neighboring pixels, increased interpolation accuracy can be achieved. And then using the gradient vector which was obtained by Sobel operation, enables to consider the fine directional edges and make it possible to estimate the accurate direction of edges. In other words, it is possible to interpolate the interlaced images with considering the characteristics of images. In addition, by altering the conventional edge detector for the purpose of a easy De-interlacing and multiplying the optimal translation coefficients to each of the gradient vectors, an efficient interpolation for images can be achieved. Comparing with the conventional De-interlacing algorithms, proposed algorithm not only reduced the complexity but also estimated the accurate edge direction and the proposed scheme have been clearly verified that it enhances the objective and subjective image quality by the extensive simulations for various images.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.329-330
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• 2006

This paper presents a recovery technique for images with block-based corruption by transmission losses. Conventional methods that do not consider edge directions can cause blocked blurring artifacts. In this paper, we present a block recovery scheme using Haar wavelet features. The adaptive selection of neighboring blocks is performed based on the energy ratio f wavelet subbands. The lost blocks are recovered by linear interpolation in the spatial domain using selected block pairs. The proposed directional recovery method is effective for the strong edge because it exploits the varying neighboring blocks adaptively according to the edges and the directional information in the image. The proposed method outperforms the previous methods that used only a predefined set of neighboring blocks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.257-266
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• 2007

In many multimedia applications, image compression is required to substantially reduce the amount of image data. This compression, however, sometimes brings artifacts. Typical artifacts are blocking artifacts and mosquito noise in DCT-coded images, and ringing artifacts around edges in wavelet-coded images. We propose a new directional postprocessing algorithm, which includes detection of the edge direction, interpolation scheme, and directional nonlinear filtering, to enhance the quality of decoded images. Simulation results show that the proposed algorithm is as effective as or more effective than other nonlinear filtering techniques.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.89-101
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• 2014

In this paper, we proposed a new local micro pattern, Signed Local Directional Pattern(SLDP). SLDP uses information of edges to represent the face's texture. This can produce a more discriminating and efficient code than other state-of-the-art methods. Each micro pattern of SLDP is encoded by sign and its major directions in which maximum edge responses exist-which allows it to distinguish among similar edge patterns that have different intensity transitions. In this paper, we divide the face image into several regions, each of which is used to calculate the distributions of the SLDP codes. Each distribution represents features of the region and these features are concatenated into a feature vector. We carried out facial expression recognition with feature vectors and SVM(Support Vector Machine) on Cohn-Kanade and JAFFE databases. SLDP shows better classification accuracy than other existing methods.

• 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.95-102
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• 2005

When images compressed with block-based compression techniques are transmitted over a noisy channel, unexpected block losses occur. In this paper, we present a post-processing-based block recovery scheme using Haar wavelet features. No consideration of the edge-direction, when recover the lost blocks, can cause block-blurring effects. The proposed directional recovery method in this paper is effective for the strong edge because exploit the varying neighboring blocks adaptively according to the edges and the directional information in the image. First, the adaptive selection of neighbor blocks is performed based on the energy of wavelet subbands (EWS) and difference of DC values (DDC). The lost blocks are recovered by the linear interpolation in the spatial domain using selected blocks. The method using only EWS performs well for horizontal and vertical edges, but not as well for diagonal edges. Conversely, only using DDC performs well diagonal edges with the exception of line- or roof-type edge profiles. Therefore, we combined EWS and DDC for better results. The proposed methods out performed the previous methods using fixed blocks.