• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.658-665
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• 2015

In the production of medical images, noise reduction and contrast enhancement are important methods to increase qualities of processing results. By using the edge-based denoising and adaptive nonlinear histogram stretching, a novel medical image enhancement algorithm is proposed. First, a medical image is decomposed by wavelet transform, and then all high frequency sub-images are decomposed by Haar transform. At the same time, edge detection with Sobel operator is performed. Second, noises in all high frequency sub-images are reduced by edge-based soft-threshold method. Third, high frequency coefficients are further enhanced by adaptive weight values in different sub-images. Finally, an adaptive nonlinear histogram stretching method is applied to increase the contrast of resultant image. Experimental results show that the proposed algorithm can enhance a low contrast medical image while preserving edges effectively without blurring the details.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.109-112
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• 2007

Definition-Enhancement of the digitalized image has been being made researches continuously due to application a camera to a mobile-apparatus and the advent of a digital camera. In particular, the inputted image from a sensor goes through the process of ISP(Image Signal Process) prior to output as a visual image. The high-frequency components are offset by LPF(Low Pass Filter) that eliminates the noise of high spatial-frequency at the moment. In this paper, we propose an algorithm that outputs more vivid image by using adaptive-HPF(High Pass Filter) that has apt coefficients for diverse conditions of an image edge, nevertheless we do not employ any Edge-Detection algorithm to enhance a blurred image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.1-11
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• 2008

In this paper, we propose the new eye detection method by using adaboost (adaptive boosting) method. Also, to reduce the false alarm rate which identifies the non-eye region as genuine eye that is the Problems of previous method using conventional adaboost, we proposed the post processing methods which used the cornea specular reflection and determined the optimized ratio of eye detecting box. Based on detected eye region by using adaboost, we performed the double circular edge detector for localizing a pupil and an iris region at the same time. Experimental results showed that the accuracy of eye detection was about 98% and the processing time was less than 1 second in mobile device.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.49-58
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• 2012

Buildings become complex and diverse with time. It is difficult to extract individual buildings using only an optical image, because they have similar spectral characteristics to objects such as vegetation and roads. In this study, we propose a method to extract building area and boundary through integrating airborne Light Detection and Ranging(LiDAR) data and aerial images. Firstly, a binary edge map was generated using Edison edge detector after applying Adaptive dynamic range linear stretching radiometric enhancement algorithm to the aerial image. Secondly, building objects on airborne LiDAR data were extracted from normalized Digital Surface Model and aerial image. Then, a temporary building areas were extracted by overlaying the binary edge map and building objects extracted from LiDAR data. Finally, some building boundaries were additionally refined considering positional accuracy between LiDAR data and aerial image. The proposed method was applied to two experimental sites for validation. Through error matrix, F-measure, Jaccard coefficient, Yule coefficient, and Overall accuracy were calculated, and the values had a higher accuracy than 0.85.

• Journal of Sensor Science and Technology
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• v.12 no.2
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• pp.57-65
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• 2003

When a vision chip for edge detection using CMOS process is designed, there is a necessity to implement local light adaptive-function for detecting distinctive features of an image at a wide range of light intensities. Local light adaptation is to achive the almost same output level by changing the size of receptive-fields of the local horizontal cell layers according to input light intensities, based on the lateral inhibitive-function of the horizontal cell. Thus, the almost same output level can be obtained whether input light intensities are much or less larger than background. In this paper, the horizontal cells using a resistive network which consists of p-MOSFETs were modeled and analyzed, and the local light adaptive-mechanism of the designed vision chip using the resistive network was verified.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.186-191
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• 2019

In this paper, we propose a implementation approach of the high-speed feature extraction algorithm. The proposed method is based on the block type classification algorithm which reduces the computation time when target macro block is divided to smooth block type that has no image features. It is quantitatively identified that occurs at 29.5% of the total image using 200 standard test images with $64{\times}64$ macro block size. This means that within a standard test image containing various image information, 29.5% can reduce the complexity of the operation. When the proposed approach is applied to the Canny edge detection, the required latency of the edge detection can be completely eliminated, such as 2D derivative filter, gradient magnitude/direction computation, non-maximal suppression, adaptive threshold calculation, hysteresis thresholding. Also, it is expected that operation time of the feature detection can be reduced by applying block type classification algorithm to various feature extraction algorithms in this way.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.7-21
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• 2019

In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast experiments are implemented in different image database. The robustness of the proposed model for segmentation of images with intensity inhomogeneity and complicated edges is verified.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.6
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• pp.213-224
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• 2003

The proposed algorithm can always set up the optimal threshold value regardless of channel characteristics using an adaptive threshold establishment method that determines the threshold level according to channel noise power, and then it uses the specially designed training symbols that can make the algorithm's estimation performance be less sensitive to power delay profile variation in a multipath channel. In result, the estimation performance of the proposed technique is less affected by channel characteristic variation.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.67-76
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• 2012

This paper proposes an efficient method that helps a visually impaired person to detect a pavement borderline. A pedestrian is equipped with a camera so that the front view of a natural scene is captured. Our approach analyzes the captured image and detects the borderline of a pavement in a very robust manner. Our approach performs the task in two steps. In a first step, our approach detects a vanishing point and vanishing lines by applying an edge operator. The edge operator is designed to take a threshold value adaptively so that it can handle a dynamic environment robustly. The second step is to determine the borderlines of a pavement based on vanishing lines detected in the first step. It analyzes the vanishing lines to form VRays that confines the pavement only. The VRays segments out the pavement region in a radial manner. We compared our approach against Canny edge detector. Experimental results show that our approach detects borderlines of a pavement very accurately in various situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.863-870
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• 2009

Bilateral filter (BF) is a nonlinear filter for sharpness enhancement and noise removal. The BF performs the function by the two Gaussian filters, the domain filter and the range filter. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF with the adaptive standard deviation based on the analysis of the edge component of the local window, also having the variable filter size. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is robust and efficient than the conventional methods.