• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.24-32
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• 2010

In this study, we have proposed a pre-processing algorithm newly developed for improving the tracking efficiency of the optical flow method. The developed pre-processing algorithm consists of a median filter, binarization, morphology, canny edge, contour detecting and an approximation method. In order to evaluate whether the optical flow tracking capacity increases, this study applied the pre-processing algorithm to the Lucas-Kanade(LK) optical flow algorithm, and comparatively analyzed its images and tracking results with those of optical flow without the pre-processing algorithm and with the existing pre-processing algorithm(composed of median filter and histogram equalization). As a result, it was observed that the tracking performance derived from the LK optical flow algorithm with the pre-processing algorithm, shows better tracking accuracy, compared to the one without the pre-processing algorithm and the one with the existing pre-processing algorithm. It seems to have resulted by successful segmentation for characteristic areas and subdivision into inner and outer contour lines.

• 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.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1680-1691
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• 2004

One of the representative methods of optical flow is a gradient method which estimates the movement of an object based on the differential of image brightness. However, the method is ineffective for large displacement of the object and many improved methods have been proposed to copy with such limitations. One of these improved techniques is the multigrid processing, which is used in many optical flow algorithms. As an alternative novel technique we have been proposing an orthogonal functional expansion method, where whole displacements are expanded from low frequency terms. This method is expected to be applicable to flow estimation with large displacement and deformation including expansion and contraction, which are difficult to cope with by conventional optical flow methods. In the orthogonal functional expansion method, the apparent displacement field is calculated iteratively by a projection method which utilizes derivatives of the invariant constraint equations of brightness constancy. One feature of this method is that differentiation of the input image is not necessary, thereby reducing sensitivity to noise. In this paper, we apply our method to several real images in which the objects undergo large displacement and/or deformation including expansion. We demonstrate the effectiveness of the orthogonal functional expansion method by comparing with conventional methods including our optimally scaled multigrid optical flow algorithm.

• International Journal of Aerospace System Engineering
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• v.2 no.1
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• pp.12-17
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• 2015

Aero-optical effects induced by the flow around the optical window degrade the performance of the IR seeker, especially for the hypersonic flow. For the thermochemical non-equilibrium flow, index of refraction model and optical transmission calculation method are developed to predict the aero-optical effects. The optical distortion is discussed for the typical optical widow shape and flow condition. The influence on aero-optical effects is analyzed.

• 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 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.

• 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.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.129-135
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• 2011

In this paper, we propose the algorithm using optical flow and machine learning-based segmentation for the 3D conversion of 2D video. For the segmentation allowing the successful 3D conversion, we design a new energy function, where color/texture features are included through machine learning method and the optical flow is also introduced in order to focus on the regions with the motion. The depth map are then calculated according to the optical flow of segmented regions, and left/right images for the 3D conversion are produced. Experiment on various video shows that the proposed method yields the reliable segmentation result and depth map for the 3D conversion of 2D video.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.91-98
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• 2008

This paper describes the recognition method of moving objects in mobile robot with an omnidirectional camera. The moving object is detected using the specific pattern of an optical flow in omnidirectional image. This paper consists of two parts. In the first part, the pattern of an optical flow is investigated in omnidirectional image. The optical flow in omnidirectional image is influenced on the geometry characteristic of an omnidirectional camera. The pattern of an optical flow is theoretically and experimentally investigated. In the second part, the detection of moving objects is presented from the estimated optical flow. The moving object is extracted through the relative evaluation of optical flows which is derived from the pattern of optical flow. In particular, Focus-Of-Expansion (FOE) and Focus-Of-Contraction (FOC) vectors are defined from the estimated optical flow. They are used as reference vectors for the relative evaluation of optical flows. The proposed algorithm is performed in four motions of a mobile robot such as straight forward, left turn, right turn and rotation. Experimental results using real movie show the effectiveness of the proposed method.