• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.579-582
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• 2015

The Mean-Shift is a robustness algorithm that can be used for tracking the object using the similarity of histogram distributions of target model and target candidate. However, Mean-shift using hue information has disadvantage of tracking a wrong target when the target and background has similar hue distributions. We then propose a robust Mean-Shift tracking algorithm using new image that combined upper 4bit-planes in hue and saturation, respectively.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.472-475
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• 2003

본 논문에서는 칼라맵 인덱스와 Hue 정보를 이용하여 반복적 병합을 통해 분할하는 칼라 영상 분할 방법을 제안하였다. 먼저 영상을 벡터 양자화 한 후 양자화 칼라맵 인덱스를 이용해 초기 영역을 설정한다. 초기 영역으로 선택된 영역들은 Hue 정보를 이용하여 영역을 병합하였고 그 후 미소영역을 병합하였다. 이때 반복처리로 인해 수행시간이 많이 소요되는 것을 개선하기 위해 Hue 정보를 이용한 영역 병합 처리에서 두 개의 테이블을 이용하여 속도를 개선하였다. 후처리에서는 과분할된 영역을 제거하기 위해 RGB 칼라 성분의 유클리디언 거리를 이용하여 주변유사 영역에 병합하였다. 제안 방법은 다수의 칼라 영상에 적용하여 좋은 분할 결과와 빠른 처리속도를 보여주었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.715-718
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• 2004

본 논문에서는 컬러 영상으로부터 얼굴을 검출하는 방법으로서, RGB 색상 공간을 Hue와 Tint 정보로 분리하여 자동으로 검출하는 방법을 제시한다. 다양한 색상 공간으로의 변환은 얼굴 검출에 필요한 보다 정확한 정보를 만들어낼 수 있다. 피부 색상에 대해 Hue와 Tint 색상 정보가 직선 형태의 분포를 이루는 특징을 이용하도록 한다. 색상 및 조명의 변화에 영향을 덜 받는 두 색상 값으로부터 직선 방정식을 생성하여 입력 데이터와의 비교를 통해 얼굴 영역을 검출한다. 실험을 통해 본 논문에서 제시한 방법이 보다 빠르고 정확한 결과를 보여줌을 증명한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.591-594
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• 2004

In this paper, The identity distinction of the moving objects using distance among hue normalization levels was proposed. Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area. Hue information of the detected moving area are normalized by 24 levels from 0$^{\circ}$ to 360$^{\circ}$. A distance in between normalized levels with a hue distribution chart of the normalized moving objects is used for the identity distinction feature parameters of the moving objects. To examine proposed method in this paper, image of moving cars are obtained by setting up three cameras at different places every 1 km on outer motorway. The simulation results of identity distinction show that it is possible to distinct the identity a distance in between normalization levels of a hue distribution chart without background.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2011-2025
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• 2021

In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.

• 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 Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.21-27
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• 2006

Among various image fusion methods, intensity-hue-saturation(IHS) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, IHS can yield satisfactory 'spatial' enhancement but may introduce 'spectral' distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To solve this problem a fast IHS fusion technique with spectral adjustment is presented. The experimental results demonstrate that the proposed approach can provide better performance than the conventional IHS method, in both processing speed and image quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2417-2422
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• 2015

Mean-Shift tracking using hue has a problem that it fail in the object tracking when background has similar hue to the object. This paper proposes an improved Mean-Shift tracking algorithm using new data instead of a hue. The new data is generated by adaptive mixture of hue and saturation which have low interrelationship . That is, the proposed algorithm selects a main attribute of color that is able to distinguish the object and background well and a secondary one which don't, and places their upper 4 bits on upper 4 bits and lower 4 bits on the mixture data, respectively. The proposed algorithm properly tracks the object, keeping tracking error maximum 2.0~4.2 pixel and average 0.49~1.82 pixel, by selecting the saturation as the main attribute of color under tracking environment that background has similar hue to the object.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.149-152
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• 2016

In this paper, we propose an algorithm to track human body of input video from a single camera. The proposed method gets the difference image between gray image of input image and one of background image and also the difference image between hue image of input image and one of background image. Then we combine the results, splits foreground and background and detect human body objects. Then each object is numbered and is tracked. The proposed method tracks each object using the intensity and hue histogram of local area in objects. The proposed method is applied to video from a camera and tracked well the hided objects and the overlapped objects.