• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.901-911
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• 2009

In this paper, we propose a construction technique of two-level composite gradient map to automatically track a face with large movement in successive frames. Our method is composed of three main steps. First, the gradient maps with two-level resolution are generated for fast convergence of active contour. Second, to recognize the variations of face between successive frames and remove the neighbor background, weighted composite gradient map is generated by combining the composite gradient map and difference mask of previous and current frames. Third, to prevent active contour from converging local minima, the energy slope is generated by using closing operation. In addition, the fast closing operation is proposed to accelerate the processing time of closing operation. For performance evaluation, we compare our method with previous active contour model-based face tracking methods using a visual inspection, robustness test and processing time. Experimental results show that our method can effectively track the face with large movement and robustly converge to the optimal position even in frames with complicated background.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.464-469
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• 2006

본 논문은 연속 영상에서 빠르게 움직이거나 변형이 있는 물체를 추출하기 위한 개선된 활성 외곽선 모델을 제안한다. 제안 방법은 프레임간 물체의 변위를 식별하기 위한 합성 기울기 맵 생성 단계와 큰 변위에도 지역적 최저에 빠지지 않고 견고하게 수렴하기 위한 에너지 비탈면 생성 단계로 이루어진다. 이를 위하여 우선, 두 프레임의 기울기 맵을 합성하고, 불필요한 배경과 잡음을 제거하기 위해 두 프레임의 차를 마스크로 적용하여 합성 기울기 맵을 생성한다. 생성된 합성 기울기 맵에 수학적 형태학 연산의 하나인 닫기 연산을 적용하여 활성 외곽선이 매끄럽게 이동할 수 있는 에너지 비탈면을 만든다. 본 논문에서는 제안방법을 평가하기 위하여 움직임 변위가 큰 연속 영상을 사용하여 기존 활성 외곽선 모델 방법과 비교하여 육안평가, 에너지 최소화 과정 및 수행시간 측면에서 비교분석한다. 본 제안방법은 기존 활성 외곽선 모델 방법보다 큰 움직임 변위와 변형에도 빠르고 정확하게 물체 추적이 가능하다.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.359-364
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• 2014

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.