• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1080-1088
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• 2004

In this paper, the interesting moving objects tracking algorithm using color information on Multi-Video camera against variance of intensity, shape and background is proposed. Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area, after converting RGB color coordination of image which is input from multi-video camera into HSI color coordination. Hue information of the detected moving area are segmented to 24 levels from $0^{\circ}$ to $360^{\circ}$. It is used to the feature parameter of the moving objects that are three segmented hue levels with the highest distribution and difference among three segmented hue levels. To examine propriety of the proposed method, human images with variance of intensity and shape and human images with variance of intensity, shape and background are targeted for moving objects. As surveillance results of the interesting human, hue distribution level variation of the detected interesting human at each camera is under 2 level, and it is confirmed that the interesting human is tracked and surveilled by using feature parameters at cameras, automatically.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.28-34
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• 2007

A histogram-based road border line extractor is proposed for an efficient road extraction from the high-resolution satellite imagery. The road border lines are extracted from an edge strength map based on the directional histogram difference between the road and the non-road region. The straight and the curved roads are extracted hierarchically from the edge strength map of the original image and the segmented road cluster images, and the road network is constructed based on the connectivity. Unlike the conventional approaches based on the spectral similarity, the proposed road extraction method is more robust to noise because it extracts roads based on the histogram, and is able to extract both the location and the width of roads. In addition, the proposed method can extract roads with various spectral characteristics by identifying the road clusters automatically. Experimental results on IKONOS multi-spectral satellite imagery with high spatial resolution show that the proposed method can extract the straight and the curved roads as well as the accurate road border lines.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.744-749
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• 2007

물체 추적시스템은 비디오 감시 시스템, 화상회의 시스템과 같은 다양한 비전 응용 분야에서 점점 비중이 높아지고 있다. 이 시스템에서 가장 널리 사용되고 있는 방법 중 하나로 Particle-Filter를 들 수 있다. 하지만, 이 Particle-Filter의 단점은 유사한 여러 물체를 추적할 때에 그 물체들이 겹치거나 사라질 경우 정확한 추적을 하기 어렵다는 것이다. 이 단점을 극복하기 위해 많은 연구가 진행되고 있으며, 본 논문에서는 이 문제를 극복하기 위한 새로운 방법을 제안하고자 한다. 다중 물체 추적에서 빈번히 일어나는 문제는 두 가지로 요약할 수 있는데, 동일한 다중 물체가 부분적으로 엇갈리거나 다른 객체에 완전히 겹친 후 떨어질 때 한 물체를 중복하여 추적하는 문제(merge and split problem)와 이 때 분리되어 추적은 됐지만, 물체를 혼동하여 추적하는 문제(Labeling problem)이다. 본 논문에서는 이 러한 문제들을 풀기 위해 이미지 필드에서 보다 정확한 확률분포를 만들고, 이 확률분포의 신뢰성을 높이기 위해서 물체의 특징정보를 표현하는 몇 가지 방법을 제안한다. 전자의 문제는 두 가지 문제로 나누어 생각해 보았다. 첫째, 복잡환 환경에서의 분포를 찾아내는 것과 둘째, 추적 중인 물체를 잃어버릴 경우 새로운 샘플을 생성함으로써 나누어 보았다. 이 문제 중 첫번째는 K-means 클러스터링을 이용하여 유사한 물체가 주변에 퍼져 있을 때, 하나의 후보 위치가 아닌, K개의 후보 위치들을 만들어 내어 보다 정확한 추적이 가능하게 하였으며, 두 번째 문제는 추적 중인 물체가 다른 커다란 물체에 가려질 경우이다. 이 상황에서 샘플을 생성하는 방법은 지금까지 해왔던 간단한 환경에서의 생성 범위와는 다르게 넓게 해야 생성시켜야 한다. 이 때 샘플링의 수를 늘리지 않으면서, 최대한 정확하게 추적하기 위해서 동영상에서 물체의 모션을 이용한 모션 히스토그램을 얻어내고, 그 정보를 이용하여 샘플을 생성하는 위치를 조절함으로써 이 문제를 풀어 보았다. 그리고, 후자의 문제인 이미지 필드상에서 확률분포의 신뢰성을 높이기 위한 특징 정보는 기존에 많이 사용하던 칼라 히스토그램에 공간정보의 의미를 부여하는 칼라 히스토그램을 분할하는 방법과 SIFT에서 사용하는 방향정보와 크기정보를 사용했다. 이것들을 사용하여 보다 정확한 물체추적시스템을 다음과 같이 제안한다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.89-97
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• 2015

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.65-72
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• 2006

The traffic lights detection and recognition system is an essential module of the driver warning and assistance system. A method which is a color vision-based real time detection and recognition of traffic lights is presented in this paper This method has four main modules : traffic signals lights detection module, traffic lights boundary candidate determination module, boundary detection module and recognition module. In traffic signals lights detection module and boundary detection module, the color thresholding and the subtraction value of saturation and intensity in HSI color space and detection probability mask for lights detection are used to segment the image. In traffic lights boundary candidate determination module, the detection mask of traffic lights boundary is proposed. For the recognition module, the AND operator is applied to the results of two detection modules. The input data for this method is the color image sequence taken from a moving vehicle by a color video camera. The recorded image data was transformed by zooming function of the camera. And traffic lights detection and recognition experimental results was presented in this zoomed image sequence.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.333-340
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• 2011

The Human-Computer Interaction(HCI) is a study of the method for interaction between human and computers that merges the ergonomics and the information technology. The intelligent space, which is a part of the HCI, is an important area to provide effective user interface for the disabled, who are alienated from the information-oriented society. In the intelligent space for the disabled, the method supporting information depends on types of disability. In this paper, we only support the hearing-impaired. It is material to the gaze direction detection method because it is very efficient information provide method to present information on gazing direction point, except for the information provide location perception method through directly contact with the hearing-impaired. We proposed the gaze direction detection method must be necessary in order to provide the residence life application to the hearing-impaired like this. The proposed method detects the region of the user from multi-view camera images, generates candidates for directions of gaze for horizontal and vertical from each camera, and calculates the gaze direction of the user through the comparison with the size of each candidate. In experimental results, the proposed method showed high detection rate with gaze direction and foot sensing rate with user's position, and showed the performance possibility of the scenario for the disabled.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.9-16
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• 2006

This Paper proposes an adaptive error diffusioThis paper proposes an adaptive error diffusion algorithm for text enhancement followed by an efficient text segmentation that uses the maximum gradient difference (MGD). The gradients are calculated along with scan lines, and the MGD values are filled within a local window to merge the potential text segments. Isolated segments are then eliminated in the non-text region filtering process. After the left segmentation, a conventional error diffusion method is applied to the background, while the edge enhancement error diffusion is used for the text. Since it is inevitable that visually objectionable artifacts are generated when using two different halftoning algorithms, the gradual dilation is proposed to minimize the boundary artifacts in the segmented text blocks before halftoning. Sharpening based on the gradually dilated text region (GDTR) prevents the printing of successive dots around the text region boundaries. The error diffusion algorithm with edge enhancement is extended to halftone color images to sharpen the tort regions. The proposed adaptive error diffusion algorithm involves color halftoning that controls the amount of edge enhancement using a general error filter. The multiplicative edge enhancement parameters are selected based on the amount of edge sharpening and color difference. Plus, the additional error factor is introduced to reduce the dot elimination artifact generated by the edge enhancement error diffusion. By using the proposed algorithm, the text of a scanned image is sharper than that with a conventional error diffusion without changing background.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.