• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.287-293
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• 2004

This paper proposes an efficient motion compensation algorithm for video sequences with brightness variations. In the proposed algorithm, the brightness variation parameters are estimated and local motions are compensated. To detect the frame with large brightness variations, we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than that of the conventional methods, with a low computational load, when the video scene contains large brightness changes.

• Journal of Broadcast Engineering
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• v.7 no.4
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• pp.291-299
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• 2002

This paper proposes an efficient motion compensation algorithm for video sequences with brightness variations. In the proposed algorithm, the brightness variation parameters are estimated and local motions are compensated. To detect the frame with large brightness variations. we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than the conventional methods, with a low computational load, when the video scene contains large brightness changes.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.74-81
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• 2005

As the multimedia data increases, various scene change detection algorithms for video indexing and sequence matching have been proposed to efficiently manage and utilize digital media. In this paper, we propose a robust scene change detection algorithm for video sequences with abrupt luminance variations. To improve the accuracy and to reduce the computational complexity of video indexing with abrupt luminance variations, the proposed algorithm utilizes edge features as well as color features, which yields a remarkably better performance than conventional algorithms. In the proposed algorithm first we extract the candidate shot boundaries using color histograms and then determine using edge matching and luminance compensation if they are shot boundaries or luminance changes. If the scene contains trivial brightness variations, the edge matching and luminance compensation are performed only for shot boundaries. In experimental results, the proposed method gives remarkably a high performance and efficiency than the conventional methods with the similar computational complexity.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.117-126
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• 2010

In the case of failure images with controlling lighting like backlighting and excessive frontlinghting, the compensation scheme for a specific area in an image is required. The interested region is first selected by user in our method to compensate the first frame. Then we define the matching function of brightness and energy function is proposed with weight of matching function and the relationship among the neighbors. Finally, the energy is minimized by the graph-cut algorithm to compensate the brightness of the first frame. Other frames are straightforwardly compensated using the results of the first frame. The brightness variations of the previous frame is transmitted to the next frame via motion vectors. The reliability of the brightness variation is calculated based on the motion vector reliability. Video compensation result is achieved by the process of the image case. Simulation show that the proposed algorithm provides more natural results than the conventional algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.35-44
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• 2004

With increase in digitalmedia data, various video indexing and video sequence matching algorithms have been proposed to efficiently manage and utilize digital media. In this paper, we propose a robust video indexing algorithm to detect scene changes for video sequences with abrupt luminance variations and an efficient video sequence matching algorithm for video sequence query. To improve the accuracy and to reduce the computational complexity for video indexing with abrupt luminance variations, the proposed algorithm utilizes edge features as well as color features, which yields a remarkably better performance than conventional algorithms. In the proposed algorithm first we extract the candidate shot boundaries using color histograms and then determine using edge matching and luminance compensation if they are shot boundaries or luminance changes. If the scene contains trivial brighness variations, the edge matching and luminance compensation are performed only for shot boundaries. In experimental results, the proposed method gives remarkably a high performance and efficiency than the conventional methods with the similar computational complexity.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.497-502
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• 1997

In this study an automatic rolling-coil labeling system using robot vision system and peripheral mechanism is proposed and implemented, which instead of the manual labor to attach labels Rolling-coils in a steel mill. The binary image process for the image processing is performed with the threshold, and the contour line is converted to the binary gradient which detects the discontinuous variation of brightness of rolling-coils. The moments invariant algorithm proposed by Hu is used to make it easy to recognize even when the position of the center are different from the trained data. The position error compensation algorithm of six degrees of freedom industrial robot manipulator is also developed and the data of the position of the center rolling-coils, which is obtained by floor mount camera, are transferred by asynchronous communication method. Therefore, even if the position of center is changed, robot moves to the position of center and performs the labeling work successfully. Therefore, this system can be improved the safety and efficiency.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1266-1268
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• 1996

In this study an automatic roiling-coli labeling system using robot vision system and peripheral mechanism is proposed and implemented, which instead of the manual labor to attach labels Rolling-coils in a steel miil. The binary image process for the image processing is performed with the threshold, and the contour line is converted to the binary gradient which detects the discontinuous variation of brightness of rolling-coils. The moment invariants algorithm proposed by Hu is used to make it easy to recognize even when the position of the center are different from the trained data. The position error compensation algorithm of six degrees of freedom industrial robot manipulator is also developed and the data of the position of the center rolling-coils, which is obtained by floor mount camera, are transfered by asynchronous communication method. Therefore even if the position of center is changed, robot moves to the position of center and performs the labeling work successfully. Therefore, this system can be improved the safety and efficiency.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.48-55
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• 2016

Ambient-light sensor system, which changes the brightness of a display as ambient light change, was studied to reduce the power consumption of the mobile applications such as note PC, tablet PC and smart phone. The ambient-light sensor system should be integrated on a display panel to improve the complexity and cost of mobile applications, so the ambient-light sensor and readout circuit was integrated on a display panel using low-temperature poly-silicon thin film transistors (LTPS-TFT). We proposed the new compensation method to correct the panel-to-panel variation of the ambient-light sensors, without additional equipment. We designed and investigated the new readout circuit with the proposed compensation method and the analog-to-digital converter for the final digital output of ambient light. The readout circuit has very simple structure and control timing to be integrated with LTPS-TFT, and the input luminance ranges from 10 to 10,000 lux. The readout rate is 100 Hz, and maximum differential non-uniformity with 20 levels of the final output below 0.5 LSB.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.369-374
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• 2002

In this paper, we propose the react-time car license plate recognition algorithm using intensity variation and geometric pattern vector. Generally, difference of car license plate region between character and background is more noticeable than other regions. And also, car license plate region usually shows high density values as well as constant intensity variations. Based on these characteristics, we first extract car license plate region using intensity variations. Secondly, lightness compensation process is performed on the considerably dark and brightness input images to acquire constant extraction efficiency. In the proposed recognition step, we first pre-process noise reduction and thinning steps. And also, we use geometric pattern vector to extract features which independent on the size, translation, and rotation of input values. In the experimental results, the proposed method shows better computation times than conventional circular pattern vector and better extraction results regardless of irregular environment lighting conditions as well as noise, size, and location of plate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.