• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.1-9
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• 2008

One of the problems for implementing the spot detection phase in the 2-DE gel image analysis program is the eliminating noises in the image. Remained noises after the preprocessing phase cause the over-segmented regions by the segmentation phase. To identify and exclude the over-segmented background regions, if we use the fixed thresholding method that is choosing an intensity value for the threshold, the spots that is invisible by the eyes but mean a very small amount proteins which have important role in the biological samples could be eliminated. This paper propose an adaptive thresholding method that come from an idea that is got on statistical analysing for the prominences of the peaks. The adaptive thresholding method works as following. Firstly we calculate an average prominence value curve and fit it to exponential function curve, as a result we get parameters for the exponential function. And then we calculate a threshold value by using the parameters and probability distribution of errors. Lastly we apply the threshold value to the region for determining the region is a noise or not. According to the probability distribution of errors, the reliability is 99.85% and we show the correctness of the proposed method by representing experiment results.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.602-606
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• 2008

LCD is supplied light by BLU(Back Light Unit) and the light represents color by each color filter. Also LCD adjusts the amount of light by controlling liquid crystal between the glass of upper plate and one of lower. However, it is impossible to completely exclude light due to the structural and physical characteristic of liquid crystal. Therefore, on transfering light through optical sheet and liquid crystal, many problems are generated. They are related with energy efficiency and get effective for the contrast of LCD to have lower contrast ratio than other display devices. To solve the problems, many techniques have been studied and developed but don't exist keys to solution for them. Among methods, local dimming is one example to be applied to LCD. In this paper we propose image processing algorithm for local dimming of BLU of LED used as light source. The proposed algorithm extracts maximum luminance signal and lights using each extracted signal on segmented region of BLU. Also the proposed algorithm generates image signal in corresponding to luminance of the segmented region and supplies them with LCD panel to represent image with improving luminance ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.1023-1032
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• 2003

Object-based coding, such as MPEG-4, enables various content-based functionalities for multimedia applications. In order to support such functionalities, as well as to improve coding efficiency, each frame of video sequences should be segmented into video objects. In this paper. we propose an effective video object segmentation method using nonlinear multiscale filtering and spatio-temporal information. Proposed method performs a spatial segmentation using a nonlinear multiscale filtering based on the stabilized inverse diffusion equation(SIDE). And, the segmented regions are merged using region adjacency graph(RAG). In this paper, we use a statistical significance test and a time-variant memory as temporal segmentation methods. By combining of extracted spatial and temporal segmentations, we can segment the video objects effectively. Proposed method is more robust to noise than the existing watershed algorithm. Experimental result shows that the proposed method improves a boundary accuracy ratio by 43% on "Akiyo" and by 29% on "Claire" than A. Neri's Method does.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.39-42
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• 1994

An automatic multi-threshold algorithm for segmentation of 2D ultrasound images based on average filtering and the characteristics of speckle noise in 2D ultrasound image is proposed. To do this, we investigate the histogram of difference between $7{\times}7$ averaging histogram and $3{\times}3$ averaging histogram. And, we find zero crossing points in the positive portion of the differenced histogram and select middle points of the zero crossing points. We assign these selected points to characteristic points. The thresholds are the center of two characteristic points. Then we segment 2D ultrasound image by using these thresholds and extract edges from applying edge operator to optimal segmented image. Experimental results show that the segmented regions are devided accurately around the homogeneous region.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1314-1321
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• 2010

In this paper, we study about the vehicle detection algorithm which is in the process of travelling from the road. An input image is segmented by means of split and merge algorithm. And two largest segmented regions are removed for reducing search region and speed up processing time. In order to detect the back side of the front vehicle considers a vertical/horizontal component, uses an integral image with to apply Haar-like methods which are the possibility of shortening a calculation time, classified with SVM. The simulation result of the method which is proposed appeared highly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11C
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• pp.1076-1082
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• 2005

Recently, there have been much efforts in the image segmentation using morphological approach. Among them, the watershed algorithm is one of powerful tools which can take advantages of both of the conventional edge-based segmentation and region-based segmentation. The concept of watershed is based on topographic analogy. But, its high sensitivity to noise yields a very large number of resulting segmented regions which leads to oversegmentation. So we suggest the restricted waterfall algorithm which reduce the oversegmentation by eliminate not only local minima but also local maxima. As a result, the restricted waterfall algorithm has a good segmented image than the other methods, and has a better binary image than the histogram thresholding method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.211-230
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• 2022

In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.183-186
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• 2001

This paper for the method that automatically extracts moving object of the video image is presented. In order to extract moving object, it is that velocity vectors correspond to each frame of the video image. Using the estimated velocity vector, the position of the object are determined. the value of the coordination of the object is initialized to the seed, and in the image plane, the moving object is automatically segmented by the region growing method and tracked by the range of intensity and information about Position. As the result of an application in sequential images, it is available to extract a moving object.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.175-180
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• 1996

In this paper, a new image segmentation method based on merging of two low contrast neighbor regions iteratively is proposed. It is suitable for very low bit rate coding. The proposed method reduces efficiently contour information and preserves subjective and objective image quality. It consists of image segmentation using 4-level hierarchical structure based on mathematical morphology and 1-level region merging structure using the contrast difference of two adjacent neighbor regions. For each segmented region of the third level, two adjacent neighbor regions having low contrast difference value in fourth level based on contrast difference value is merged iteratively. It preserves image quality and shows the noticeable reduction of the contour information, so that it can improve the bottleneck problem of segmentation-based coding at very low bit rate.