• IEIE Transactions on Smart Processing and Computing
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• 제4권1호
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• pp.1-9
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• 2015

Template matching is used for many applications in image processing. One of the most researched topics is object tracking. Normalized Cross Correlation (NCC) is the basic statistical approach to match images. NCC is used for template matching or pattern recognition. A template can be considered from a reference image, and an image from a scene can be considered as a source image. The objective is to establish the correspondence between the reference and source images. The matching gives a measure of the degree of similarity between the image and the template. A problem with NCC is its high computational cost and occasional mismatching. To deal with this problem, this paper presents an algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking. The SSD provides low computational cost, while the adaptive template matching increases the accuracy matching. The experimental results showed that the proposed algorithm is quite efficient for image matching. The effectiveness of this method is demonstrated by several situations in the results section.

• 한국멀티미디어학회논문지
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• 제22권1호
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• pp.35-43
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• 2019

Template tracking refers to the procedure of finding the most similar image patch corresponding to the given template through an image sequence. In order to obtain more accurate trajectory of the template, the template requires to be updated to reflect various appearance changes as it traverses through an image sequence. To do that, appearance images are used to model appearance variations and these are obtained by the computation of the principal components of the augmented image matrix at every iteration. Unfortunately, it is prohibitively expensive to compute the principal components at every iteration. Thus in this paper, we suggest a new Sliding Window based truncated URV Decomposition (TURVD) algorithm which enables updating their structure by recycling their previous decomposition instead of decomposing the image matrix from the beginning. Specifically, we show an efficient algorithm for updating and downdating the TURVD simultaneously, followed by the rank-one update to the TURVD while tracking the decomposition error accurately and adjusting the truncation level adaptively. Experiments show that the proposed algorithm produces no-meaningful differences but much faster execution speed compared to the typical algorithms in template tracking applications, thereby maintaining a good approximation for the principal components.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.174-177
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• 2009

In object tracking, the template matching methods have been developed and frequently used. It is fast enough, but not robust to an object with the variation of size and shape. In order to overcome the limitation of the template matching method, this paper proposes a template update technique. After finding an object position using the correlation-based adaptive predictive search, the proposed method selects blocks which contain object's boundary. It estimates the motion of boundary using block matching, and then updates template. We applied it to IR image sequences including an approaching object. From the experimental results, the proposed method showed successful performance to track object.

• 로봇학회논문지
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• 제7권2호
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• pp.142-149
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• 2012

The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.

• 한국지능시스템학회논문지
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• 제18권5호
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• pp.666-671
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• 2008

본 논문은 비정형 객체를 추적함에 있어서 다른 객체와 겹쳐진 후 계속 추적할 수 있는 방법으로 지역 정보와 객체의 모션 템플리트 그리고 색 정보를 계층적으로 사용하는 방안을 제안한다. 기본적으로 색 정보 기반의 CAMshift 알고리즘을 바탕으로 각 프레임마다 color template를 업데이트하여 현재의 객체와 template를 비교하고, 업데이트 된 color template를 바탕으로 색 분포를 사용하여 CAMshift 결과를 비교하여 추적하는 물체를 보다 정확하게 판별할 수 있도록 한다. 지역정보, 컬러 정보, 모션 템플리트 정보를 융합한 객체추적은 기존의 객체추적 방법의 장점을 모두 유지하면서 추적하는 객체를 보다 정확하게 인식할 수 있다. 이러한 성능 향상은 기존의 객체추적 시스템에 추가하기도 용이 할 백만 아니라 감시시스템 및 객체 추적 시스템의 연구에서 정확성의 향상에 기여할 것으로 기대된다.

• 한국정보통신학회논문지
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• 제11권5호
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• pp.977-984
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• 2007

동영상으로부터 움직임 분석 및 색상정보를 이용한 실시간 얼굴 추적 방법을 제안한다. 시간미분연산에 의하여 실시간으로 입력되는 동영상으로부터 움직임 영역을 검출한 후, 컬러공간 융합 필터링에 의하여 얼굴 영역 후보 화소를 검출하고 눈, 입등 얼굴 구성 요소 검출에 의하여 얼굴 영역의 실시간 추적을 행하였다. 얼굴 구성요소의 참조 템플릿을 구축한 후 신규 입력되는 연속영상의 얼굴 영역으로부터 템플릿 매칭을 행함으로써 추출된 얼굴 영역의 신뢰도를 판정하는 방법으로 얼굴 영역 추적의 안정도를 향상시켰다.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.25-36
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• 2020

Motivation: Fibers as the extracellular filamentous structures determine the shape of the cytoskeletal structures. Their characterization and reconstruction from a 3D cellular image represent very useful quantitative information at the cellular level. In this paper, we presented a novel automatic method to extract fiber diameter distribution through a pipeline to reconstruct fibers from 3D fluorescence confocal images. The pipeline is composed of four steps: segmentation, skeletonization, template fitting and fiber tracking. Segmentation of fiber is achieved by defining an energy based on tensor voting framework. After skeletonizing segmented fibers, we fit a template for each seed point. Then, the fiber tracking step reconstructs fibers by finding the best match of the next fiber segment from the previous template. Thus, we define a fiber as a set of templates, based on which we calculate a diameter distribution of fibers.

• 전기학회논문지
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• 제59권8호
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• pp.1477-1481
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• 2010

In this paper, we propose the classification and tracking method of the hand region using deformable template and condensation. To do this, first, we extract the hand region by using the fuzzy color filter and HCbCr color model. Second, we extract the edge of hand by applying the Canny edge algorithm. Third, we find the first template by calculating the conditional probability between the extracted edge and the model edge. If the accurate template of the first object is decided, the condensation algorithm tries to track it. Finally, we demonstrate the effectiveness and feasibility of the proposed method through some experiments.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.707-720
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• 2014

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.