• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.359-360
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• 2021

The pixel-based processing of an image refers to a process of converting a value of one pixel only depending on the value of the current pixel, regardless of the value of another pixel. Pixel-based processing is used as the most basic operation in many fields such as image conversion, image enhancement, and image synthesis. There are processing methods such as arithmetic operation, histogram smoothing, and contrast stretching. In this paper, in order to clearly distinguish the tidal flat region from the tidal flat image of the west coast taken with a drone, we seek a method to find an efficient outline using pixel-based processing in the boundary detection part of the pre-processing process.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.551-562
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• 1998

Amultiresolution filtering algorithm based on the physical SPECT lesion detachability provides and optimal solution for SPECT reconstruction problem. Related to the previous study, we estimated the SPECT lesion detection capability by m minimum detectable lesion sizes (MDLSs), and generated m reconstruction filters which are designed to maximize the smoothing effect at a fixed MDLS-dependent resolution level $\frac{MDLS}{4\sqrt{2In2}}$. The proposed multiresolution filtering algorithm used a coarse-to-fine approach for the m-level resolution filter images obtained from these m filters for a given projection image. First, the local homogeneity is determined for every pixel of the filter images, by comparing the local variance value computed in a window centered at the pixel and the mode determined from the distribution of the local variances. Based on the local homogeneity, the pixels declared as homogeneous are chosen from the filter image of the lowest resolution, and for the other pixels the same process is repeated for the higher resolution filter images. For the non-homogeneous pixels after this pixels after this repetition process ends, the pixel values of the highest resolution filter image are substituted. From the results of the simulated experiments, the proposed multiresolution filtering algorithm showed a strong smoothing effect in the homogeneous regions and a significant resolution improvement near the edge regions of the projection images, and so produced good adaptability effects in the reconstructed images.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.403-408
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• 2003

A general meaning of caricaturing is that a representation, especially pictorial or literary, in which the subject's distinctive features or peculiarities are deliberately exaggerated to produce a comic or grotesque effect. In other words, a caricature is defined as a rough sketch(dessin) which is made by detecting features from human face and exaggerating or warping those. There have been developed many methods which can make a caricature image from human face using computer. In this paper, we propose a new caricaturing system. The system uses a real-time image or supplied image as an input image and deals with it on four processing steps and then creates a caricatured image finally. The four Processing steps are like that. The first step is detecting a face from input image. The second step is extracting special coordinate values as facial geometric information. The third step is deforming the face image using local warping method and the coordinate values acquired in the second step. In fourth step, the system transforms the deformed image into the better improved edge image using a fuzzy Sobel method and then creates a caricatured image finally. In this paper , we can realize a caricaturing system which is simpler than any other exiting systems in ways that create a caricatured image and does not need complex algorithms using many image processing methods like image recognition, transformation and edge detection.

• Journal of Korea Multimedia Society
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• v.12 no.2
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• pp.191-199
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• 2009

In this paper, the improved edge extract methods is proposed in order to extract edge. For the correct and fast detect, the binary image using the threshold value is applied for a experiment. For the experimental analysis, we compare the existing edge methods with the improved methods. Hereby, the exist methods are the sobel, robert, and prewitt. and the improved methods use the existing methods which is applied mask variations. The merits of the improved mothods have a result of a little erosion, a apparent edge. Specially, we use the grey image of medical image for the experimental analysis and then apply threshold value for a result image. After that, we acquire a apparent edge. For a quantitative analysis of the each methods, the each images was applied a histogram. As a result, we prove the merit of the improved methods using a analytical graph of the medical images.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.239-245
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• 2014

In this paper, a weighted value wrinkle detection method is suggested based on the analysis on the entire facial features such as face contour, face size, eyes and ears. Firstly, the main facial elements are detected with AAM method entirely from the input screen images. Such elements are mainly composed of shape-based and appearance methods. These are used for learning the facial model and for matching the face from new screen images based on the learned models. Secondly, the face and background are separated in the screen image. Four points with the biggest possibilities for wrinkling are selected from the face and high wrinkle weighted values are assigned to them. Finally, the wrinkles are detected by applying Canny edge algorithm for the interested points of weighted value. The suggested algorithm adopts various screen images for experiment. The experiments display the excellent results of face and wrinkle detection in the most of the screen images.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.183-189
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• 2015

In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.507-516
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• 2008

Cardiac disease is the leading cause of death in the world. Quantification of cardiac function is performed by manually calculating blood volume and ejection fraction in routine clinical practice, but it requires high computational costs. In this study, an automatic left ventricle (LV) segmentation algorithm using short-axis cine cardiac MRI is presented. We compensate coil sensitivity of magnitude images depending on coil location, classify edge information after extracting edges, and segment LV by applying region-growing segmentation. We design a weighting function for intensity signal and calculate a blood volume of LV considering partial voxel effects. Using cardiac cine SSFP of 38 subjects with Cornell University IRB approval, we compared our algorithm to manual contour tracing and MASS software. Without partial volume effects, we achieved segmentation accuracy of $3.3mL{\pm}5.8$ (standard deviation) and $3.2mL{\pm}4.3$ in diastolic and systolic phases, respectively. With partial volume effects, the accuracy was $19.1mL{\pm}8.8$ and $10.3mL{\pm}6.1$ in diastolic and systolic phases, respectively. Also in ejection fraction, the accuracy was $-1.3%{\pm}2.6$ and $-2.1%{\pm}2.4$ without and with partial volume effects, respectively. Results support that the proposed algorithm is exact and useful for clinical practice.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.45-51
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• 2006

Recently, the IDS(Intrusion Detection System) using a video camera is an important part of the home security systems which start gaining popularity. However, the video intruder detection has not been widely used in the home surveillance systems due to its unreliable performance in the environment with abrupt illumination change. In this paper, we propose an effective moving edge extraction algerian from a sequence image. The proposed algorithm extracts edge segments from current image and eliminates the background edge segments by matching them with reference edge list, which is updated at every frame, to find the moving edge segments. The test results show that it can detect the contour of moving object in the noisy environment with abrupt illumination change.