• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.275-280
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• 2002

A new algorithm for gradual scene change detection in MPEG based frame sequences is proposed in this paper. The proposed algorithm is based on the fact that most of gradual curves can be characterized by variance distributions of edge information in the frame sequences. Average edge frame sequences are obtained by performing "sober" edge detection. Features are extracted by comparing variances with those of local blocks in the average edge frames. Those features are further processed by the opening operation to obtain smoothing variance curves. The lowest variance in the local frame sequences is chosen as a gradual detection point. Experimental results show that the proposed method provides 85% precision and 86% recall rate fur gradual scene changes.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.1
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• pp.37-55
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• 2023

In this paper we propose a new algorithm for detecting and counting flowers in a complex background based on digital images. The algorithm mainly includes the following parts: edge contour extraction of flowers, edge contour determination of overlapped flowers and flower counting. We use a contour detection technique in Computer Vision (CV) to extract the edge contours of flowers and propose an improved algorithm with a concave point detection technique to find accurate segmentation for overlapped flowers. In this process, we first use the polygon approximation to smooth edge contours and then adopt the second-order central moments to fit ellipse contours to determine whether edge contours overlap. To obtain accurate segmentation points, we calculate the curvature of each pixel point on the edge contours with an improved Curvature Scale Space (CSS) corner detector. Finally, we successively give three adaptive judgment criteria to detect and count flowers accurately and automatically. Both experimental results and the proposed evaluation indicators reveal that the proposed algorithm is more efficient for flower counting.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.265-267
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• 2013

Edges include various important informations of the objects. These edges are being applied in numerous areas and there is a detection method using mask in existing edge detection methods. These existing edge detection methods are simple to realize. However, because the fixed mask is used, edge detection characteristics in complicated noise environments are somewhat unsatisfactory. Therefore, to compensate for the weakness in the existing detection methods, edge detection algorithm which uses the standard deviation of local mask and noise elimination was proposed.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.85-97
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• 1998

The existing edge detection methods can not represent the real edge of object at fitting point or detect the edge which has unsufficient connecting trait. Especially, the two-fold thick edge detected by these methods cannot coincide real boundary of subject and it's location. To overcome these problems, we introduce the Genetic Algorithm(GA) in edge detection. The energy function is the value of fixel's satisfaction degree to edge condition. And it consists of the fitness value to image formation type, fitness value to connecting trait to it's neighboring edge and evalulation function which can represents the edge at fitting point as one fixel. This method is superior to remove the noise in edge detection than the existing methods. And it also detects the clear and exact edge because it can find the one fixel which is located at fitting point and has strong connecting trait.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.52-57
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• 2003

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland can evaluate the usefulness of rules represented by classifiers with repeated learning. FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. It is that the antecedent and consequent of classifier is same as a fuzzy rule. In this paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented into fuzzy set. Then a pixel is decided whether it is edge pixel or not using fuzzy if-then rules. Depending on the average of gray levels, a number of fuzzy rules can be activated, and each rules makes the output. These outputs are aggregated and defuzzified to take new gray value of the pixel. To evaluate this edge detection, we will compare the new gray level of a pixel with gray level obtained by the other edge detection method such as Sobel edge detection. This comparison provides a reinforcement signal for FCS which is reinforcement learning. Also the FCS employs the Genetic Algorithms to make new rules and modify rules when performance of the system needs to be improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.804-807
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• 2002

Edge of image have a many information about input image. There is a many applications to using a edge detection and uses by variable special effect. Edge detection is a field of image analysis, image segmentation using a pixel make the one field for decision of image construction. In this paper, image segmentation through many ways of edge detection for image segmentation. First of all, it analyze feature of image and extract by feature of each image, to adopt way of edge detection to selective. It realize edge detection efficiently, consider to feature of language through using a java image segmentation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1635-1644
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• 1993

In this paper, we examine the statistical characteristics of edge enhancing filters and their efficacy as preprocessing operator before edge detection. In particular, edge enhancing filters called the Comparison and Selection(CS), Hachimura-kuwahara(HK), and Selective Average(SA) filters are considered. These filters can reduce noise while producing step-type edges, thus seem to be effective for preprocessing noisy images prior to applying edge detecors. The ability of edge enhancing filters to suppress white Gaussian noise and the error probabilities occured during the edge detection following SA prefiltering are evaluated statistically through numerical analysis. The effect of prefiltering on edge detection is assessed by applying the edge enhancing fitters to a noise image degraded by additive white noise prior to applying the Sobel operator and the Laplacian of Gaussian( LoG ) operator, respectively. It is shown that the edge enhancing filters tend to produce ideal step-type edges while reducing the noise reasonably well, and the use of edge enhancing filters prior to edge detection can improve the performance of subsequent edge detector.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1337-1345
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• 1996

Edge detection is the first step and very important step in image nalyisi. In this paper, proposed edge detection algorithm based on informations of edge structures and it is different from other classical edge detection operators such asgradient and surface fitting algorithm. The firs, we defined characteristics of edge structures such as continuity, thinness, localization, length. The second, we defined valid edge structures and ideal edge pixel positions in $3\times3$ window based on edge characteristics of edge structures. And we proposed twelve windows for enhance dissimilarity regions based on valid edge structures and ideal edge pixel positions. In specially, proposed algorithm was shown better performance of edge detection than other operators such as gradient operator and the LoG(Laplacian of gradient) operator of zero crossings in noisy test image with $\sigma=30$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1680-1686
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• 2015

Edge detection is an essential preprocessing for most image processing application, and there are several existing detection methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) operators, etc. Those existing edge detection methods have not given satisfactory results since they do not offer enough pixel brightness change in low light level environment. Therefore, in this study new algorithms using brightness transfer function in the preprocessing and for edge detection applying standard deviation and average-weighted local masks are proposed. In addition, the performance of proposed algorithms was evaluated in comparison with the existing edge detection methods such as Sobel, Roberts, Prewitt, Laplacian, LoG operators.