• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.29-36
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• 2015

In this paper, we propose the horizontal line detection using the Haar-like features and linear regression in infrared images. In the marine environment horizon image is very useful information on a variety of systems. In the proposed method Haar-like features it was noted that the standard deviation be calculated in real time on a static area. Based on the pixel position, calculating the standard deviation of the around area in real time and, if the reaction is to filter out the largest pixel can get the energy map of the area containing the straight horizontal line. In order to select a horizontal line of pixels from the energy map, we applied the linear regression, calculating a linear fit to the transverse horizontal line across the image to select the candidate optimal horizontal. The proposed method was carried out in a horizontal line detecting real infrared image experiment for day and night, it was confirmed the excellent detection results than the legacy methods.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.25-32
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• 2015

In this paper, we propose a license plate detection method of running cars in various road images. The proposed method first classifies the road image into day and night images to improve detection accuracy, and then the tail-light regions are detected by finding red color areas in RGB color space. The candidate regions of the license plate areas are detected by using symmetrical property, size, width and variance of the tail-light regions, and to find the license plate areas of the various sizes the morphological operations with adaptive size structuring elements are applied. Finally, the plate area is verified and confirmed with the geometrical and image features of the license plate areas. The proposed method was tested with the various road images and the detection rates (precisions) of 84.2% of day images and 87.4% of night images were achieved.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.35-44
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• 2010

In this paper, we propose a method about producing skin color model adaptively in input image and face detection. The principle process which we proposed is finding eyes candidates by applying the eye features to neural network, and then using the around color to find the distribution of color value. There will be a verification process that producing face region by using color value distribution which is detected as skin region and find mouth candidate in corresponding face region; if eye candidate and mouth candidate's connection structure is similar with face structure, then it can be judged as a face. Because this method can detect skin region adaptively by finding eyes, we solve the rate of false positive about the distorted skin color which is used by existing face detection methods. The experiment was performed about detecting the eye, the skin, the mouth and the face individually. The results revealed that the proposed technique is better than the traditional techniques.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1117-1124
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• 2011

This paper proposes a plate number extraction method which uses the information of both the colors and the arrangement of numbers in a vehicle image with complex background. In a number plate, color of the numbers is usually black or white, and numbers are arranged in a row. At first, a raw image is partitioned into the plate number candidate regions and non-interest region. The number candidate regions are thresholded in mean binarization. After eliminating the illegal candidate regions using the aspect ratio of the plate number, the plate number region is finally extracted by using the arrangement information among the numbers. To evaluate the proposed mothed, 292 images are taken in various places and at different times. The experimental results show that the rate of the proposed number regions extraction is about 89.8%, 95.5% for the plate of green and white backgrounds, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2231-2238
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• 2011

In this paper, the extract method of eye and eye-line from picture of a person is proposed. Most of existing papers are to extract the position of eyeball but in this paper, by extracting not only the position of eyeball but also eye-line, it can be applied to the face application program variously. The experimental data of the input picture is a full face photograph taken by smart phone, basically the picture is limited to the face of one person and back ground can be taken from every where and no restriction of race. The proposed method is to extract face candidated area by using Harr Classifier and set up the candidate area of eye position from face candidate area. To extract high value from eye candidate area using dilate operation, and proposed the method to classify eye and eyelash by local thresholding of the picture. After that, using thresholding image from eyemapC that Hsu's suggested, and separated the area with eye and without eye. Finally extract the contour of eye and detect eye-line using optimum ellipse estimation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.162-170
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• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.72-79
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• 2002

This paper presents an efficient face detection algorithm for color images with a complex background. The presented algorithm utilizes the color information and eigenface that is calculated by PCA-LDA (Principle Component Analysis - Linear Discriminant Analysis). The method of using the color information is faster than any other methods. Eigenface includes average information of the whole test faces. Therefore eigenface can decide that the candidate region is a face. The whole process is composed of two steps. First, it finds first face candidates region of skin tone using a color information in image. We can get a size and position of face candidate region. Second, we compare first face candidate region with eigenface, so decide that an image whether include a face or not. The advantages of the proposed approach include that increasing the detection speed by deciding a size and position of first face candidates region. Also, Betting 97% of the detection rate by comparing the eigenfaces calculated in PCA-LDA.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.57-66
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• 2011

In this paper, it is proposed for algorithm to detect human efficiently using a edge symmetry and gradient directional characteristics in realtime by the feature extraction in a single input image. Proposed algorithm is composed of three stages, preprocessing, region partition of human candidates, verification of candidate regions. Here, preprocessing stage is strong the image regardless of the intensity and brightness of surrounding environment, also detects a contour with characteristics of human as considering the shape features size and the condition of human for characteristic of human. And stage for region partition of human candidates has separated the region with edge symmetry for human and size in the detected contour, also divided 1st candidates region with applying the adaboost algorithm. Finally, the candidate region verification stage makes excellent the performance for the false detection by verifying the candidate region using feature vector of a gradient for divided local area and classifier. The results of the simulations, which is applying the proposed algorithm, the processing speed of the proposed algorithms is improved approximately 1.7 times, also, the FNR(False Negative Rate) is confirmed to be better 3% than the conventional algorithm which is a single structure algorithm.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3683-3693
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• 1999

Most recognition methods of car licence plate have difficulties concerning plate recognition rates and system stability in that restricted car images are used and good image capture environment is required. To overcome these difficulties, I proposed a new recognition method of car licence plates, in which both intensity variation and color information are used. For a captured car image, multiple candidate plate-bands are extracted based on the number of intensity variation. To have an equal performance on abnormally dark and bright Images. plate lightness is calculated and adjusted based on the brightness of plate background. Candidate plate regions are extracted using contour following on plate color pixels in oath plate band. A candidate region is decided as a real plate region after extracting character regions and then recognizing them. I recognize characters using template matching since total number of possible characters is small and they art machine printed. To show the efficiency of the proposed method, I tested it on 200 car images and found that the method shows good performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.941-944
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• 2009

In this paper, we present a improved algorithm for thinning vein pattern. The vein image scanned using near infra-red is sliced in the horizontality or verticality direction, and then average value is gained from each slice image. Using curvature value changed from average value, search candidate part and extract vein pattern through re-searched candidate part in overlapping area. For the extracted vein pattern connecting, we proposed a DAL method that verify distance, angle and luminosity on pattern before tracking pattern extracted. The proposed improved thinning method is helpful for accurate connecting of vein pattern more than method used distance material in candidate part.