• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.372-374
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• 2003

The optical flow theory can be utilized for automatically tracking and positioning homologous points in digital video (DV) image sequences. In this paper, the Lucas-Kanade optical flow estimation (LKOFE) method and the normalized cross-correlation (NCC) method are compared and analyzed using the DV image sequences acquired by our SONY DCRPC115 DV camera. Thus, an improved optical flow estimation procedure, called 'Iterated Optical Flow Estimation (IOFE)', is presented. Our test results show that the trackable range of 3${\sim}$4 pixels in the LKOFE procedure can be apparently enlarged to 30 pixels in the IOFE.

• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.300-308
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• 2001

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.12-15
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• 1996

The purpose of image sequence coding is to reduce the spatio-temporal redundancies. The transform coding such as DCT is used for the spatial redundancies. In this paper, the optical flow method is applied to solve the problem of temporal redundancies. So far, pixel intensity conservation has been used to solve the optical flow. We used the neighborhood information as well as pixel intensity conservation. And we compared the merits and demerits of the conventional method and the proposed method in this paper.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.843-846
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• 2000

An optical flow-based motion estimation algorithm is proposed for video coding. The algorithm uses block-matching motion estimation with an adaptive search region. The search region is computed from motion fields that are estimated based on the optical flow. The algorithm is based on the fact that true block-motion vectors have similar characteristics to optical flow vectors. Thereafter, the search region is computed using these optical flow vectors that include spatial relationships. In conventional block matching, the search region is fixed. In contrast, in the new method, the appropriate size and location of the search region are both decided by the proposed algorithm. The results obtained using test images show that the proposed algorithm can produce a significant improvement compared with previous block-matching algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.56-59
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• 1997

Vision system is mainly consist of three parts of perception, action. One of these parts, perception system detects visual target in surrounding environment. Block-based motion estimation with compensation is one of the popular approaches without accuracy. The hierarchical method the optical flow with gradient is used to improve optical flow time delay.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1350-1352
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• 1996

The purpose of image sequence coding is to reduce the spatio-temporal redundancies. For the spatial redundancies, we can use the transform coding such as DCT. In this paper, the optical flow method is applied to solve the problem of temporal redundancies. There are several estimation methods like block matching method and pel-recursive method. Block matching method is easy for a hardware implementation because of the computational simplicity. So, it is now used as the estimation method in MPEG-l, MPEG-2, and H.261. We compared the merits and demerits of the optical flow method and the block matching method in this paper.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.48-56
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• 1995

This paper presents a method of implementing efficient optical flow estimation for dynamic scene analysis using the hierarchical Hopfield neural networks. Given the two consequent inages, Zhou and Chellappa suggested the Hopfield neural network for computing the optical flow. The major problem of this algorithm is that Zhou and Chellappa's network accompanies self-feedback term, which forces them to check the energy change every iteration and only to accept the case where the lower the energy level is guaranteed. This is not only undesirable but also inefficient in implementing the Hopfield network. The another problem is that this model cannot allow the exact computation of optical flow in the case that the disparities of the moving objects are large. This paper improves the Zhou and Chellapa's problems by modifying the structure of the network to satisfy the convergence condition of the Hopfield model and suggesting the hierarchical algorithm, which enables the computation of the optical flow using the hierarchical structure even in the presence of large disparities.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.241-248
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• 2007

An L-curve corner detection method is proposed for the determination of the regularization parameter in optical flow estimation. The method locates the positive peak whose curvature difference from the just right-hand negative valley is the maximum in the curvature plot of the L-curve. while the existing curvature-method simply finds the maximum in the plot. Experimental results show that RMSE of the estimated optical flow is greater only by 0.02 pixels-per-frame than the least in the average sense. The proposed method is also compared with an existing curvature-method and the adaptive pruning method, resulting in the optical flow estimation closest to the least RMSE.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.539-544
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• 2009

An estimation of 3D velocity field including occluded parts without maxing tracer to the fluid had not only never been proposed but also impossible by the conventional computer vision algorithm. In this paper, we propose a new method of three dimensional optical flow of the fluid based on physical model, where some boundary conditions are given from a priori knowledge of the flow configuration. Optical flow is obtained by minimizing the mean square errors of a basic constraint and the matching error terms with visual data using Euler equations. Here, Navier-Stokes motion equations and the differences between occluded data and observable data are employed as the basic constrains. we verify the effectiveness of our proposed method by applying our algorithm to simulated data with partly artificially deleted and recovering the lacking data. Next, applying our method to the fluid of observable surface data and the knowledge of boundary conditions, we demonstrate that 3D optical flow are obtained by proposed algorithm.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.780-785
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• 2006

HCI, 비전 기반 사용자 인터페이스 또는 제스쳐 인식과 같은 많은 분야에서 3 차원 얼굴 모션을 추정하는 것은 중요한 작업이다. 연속된 2 차원 이미지로부터 3 차원 모션을 추정하기 위한 방법으로는 크게 외형 기반 방법이나 모델을 이용하는 방법이 있다. 본 연구에서는 동영상으로부터 3 차원 실린더 모델과 Optical flow를 이용하여 실시간으로 얼굴 모션을 추정하는 방법을 제안하고자 한다. 초기 프레임으로부터 얼굴의 피부색과 템플릿 매칭을 이용하여 얼굴 영역을 검출하고 검출된 얼굴 영역에 3 차원 실린더 모델을 투영하게 된다. 연속된 프레임으로 부터 Lucas-Kanade 의 Optical flow 를 이용하여 얼굴 모션을 추정한다. 정확한 얼굴 모션 추정을 하기 위해 IRLS 방법을 이용하여 각 픽셀에 대한 가중치를 설정하게 된다. 또한, 동적 템플릿을 이용해 오랫동안 정확한 얼굴 모션 추정하는 방법을 제안한다.