• 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 Korea Multimedia Society
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• v.8 no.2
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• pp.181-191
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• 2005

The classification error of 'ㅁ', 'ㅇ' is one of the main causes of incorrect recognition in Korean characters, but there haven't been enough researches to solve this problem. In this paper, a new feature extraction method from Korean grapheme is proposed to recognize 'ㅁ', 'ㅇ'effectively. First, we defined an optimal neighbor-distance selection measure using modified Hausdorff distance, which we determined the optimal neighbor-distance by. And we extracted neighbor-angle feature which was used as the effective feature to classify the two graphemes 'ㅁ', 'ㅇ'. Experimental results show that the proposed feature extraction method worked efficiently with the small number of features and could recognize the untrained patterns better than the conventional methods. It proves that the proposed method has a generality and stability for pattern recognition.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.457-466
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• 2010

In this paper, we propose a new method for detecting digital forgery that identify interpolated region between digital composited images. For detecting the interpolation factor and the tampered regions, we perform two algorithms: The first algorithm is to estimate the interpolation factors using the differential equation for forgery image along the horizontal, vertical, and diagonal directions, respectively; The second algorithm is to scan the interpolation factors along each direction for detection areas as the mask of the optical window size($64{\times}64$) in order to find out the forgery region. A detection map of the forgery is classified with the magnitude of estimated interpolation factors into colors. This detection map can be used to find out interpolated regions from the tampered image. Experimental results demonstrate the proposed algorithms are proven on several examples. We also show the proposed approach is to accurately detect interpolated regions from digital composite images.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.54-61
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• 2009

This paper proposes a skin segmentation method based on region histograms of the color quantization map. First, we make a quantization map of the image using the JSEG algorithm and detect the skin pixel. For the skin region detection, the similar neighboring regions are set by its similarity of the size and location between the previous frame and the present frame from the each region of the color quantization map. Then we compare the similarity of histogram between the color distributions of each quantized region and the skin color model using the histogram distance. We select the skin region by the threshold value calculated automatically. The skin model is updated by the skin color information from the selected result. The proposed algorithm was compared with previous algorithms on the ECHO database and the continuous images captured under time varying illumination for adaptation test. Our approach shows better performance than previous approaches on skin color segmentation and adaptation to varying illumination.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.451-457
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• 2001

This paper describes automated methods for the detection of lung nodules and their volume calculation on CT scans. Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchymes from the thoracic wall and mediastinum. A scanning-ball algorithm was applied to more accurately delineate the lung boundaries, thereby incorporating peripheral nodules contiguous to pleural surface within the segmented lung parenchymes. The lesions which have the high gray value were extracted from the segmented lung parenchymes. The selected lesions include nodules, blood vessels and partial volume effects. The discriminating features such as size, solid-shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudo-lesions. Volume and circularity calculation were performed for each identified nodules. The identified nodules were sorted in descending order of the volume. These method were applied to 621 image slices of 19 cases. The sensitivity was 95% and there was no false-positive result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1574-1585
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• 2011

This paper proposes a real-time algorithm for speed limit sign recognition for advanced safety vehicle system. The proposed algorithm uses Block-DCT in extracting features from a given ROI(Region Of Interest) instead of using entire pixel values as in previous works. The proposed algorithm chooses parts of the DCT coefficients according to the proposed discriminant factor, uses correlation coefficients and variances among ROIs from training samples to reduce amount of arithmetic operations without performance degradation in classification process. The algorithm recognizes the speed limit signs using the information obtained during training process by calculating LDA and Mahalanobis Distance. To increase the hit rate of recognition, it uses accumulated classification results computed for a sequence of frames. Experimental results show that the hit rate of recognition for sequential frames reaches up to 100 %. When compared with previous works, numbers of multiply and add operations are reduced by 69.3 % and 67.9 %, respectively. Start after striking space key 2 times.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1766-1774
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• 2010

This paper proposes a real-time algorithm of recognizing speed limit signs for intelligent vehicles. Contrary to previous works which use all the pixel values in the ROI (Region Of Interest) after preprocessing image at ROI and need a lot of operations, the proposed algorithm uses fewer DCT coefficients in the ROI as features of each image to reduce the number of operations. Choosing a portion of DCT coefficients which satisfy discriminant criteria for recognition, the proposed algorithm recognizes the speed limit signs using the information obtained in the selected features through LDA and MD. It selects one having the highest probability among the recognition results calculated by accumulating the classification results of consecutive individual frames. Experimental results show that the recognition rate for consecutive frames reaches to 100% with test images. When compared with the previous algorithm, the numbers of multiply and add operations are reduced by 58.6% and 38.3%, respectively.

• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.194-198
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• 2007

본 연구의 주제는 텍스트를 어떻게 회화적이미지로 전환시킬 수 있을까?에 대한 것이다. 오늘날 디지털 환경 속에 있는 우리는 이미지 중심의 세계 속에 살고 있다. 과거 텍스트가 해왔던 정보의 생산과 전달 그리고 저장의 역할을 오늘날은 상당부분 이미지가 하게 되었다. 하지만 그럼에도 불구하고 아날로그 방식과 정서는 여전히 우리 곁에 남아있는 것도 사실이다. "Yesterday to Today"는 문자의 최소 단위인 알파벳을 통하여 이미지를 재현하려는 작품이다. 이 작품은 크게 두 가지 Idea로 이루어지는데 하나는 복수개의 실시간 영상 소스를 이용하여 이미지를 구성해내는 것과, 다른 하나는 텍스트에 의한 이미지의 변환이다. 복수개의 실시간 영상 소스는 프로그램이 작동하는 컴퓨터와 직/간접적으로 연결되어 전달되는데, 직접적으로 연결되어 근거리의 특정한 지점으로부터 영상 소스를 받을 수 있고, 또 하나의 방법은 인터넷을 통한하나 이상의 원거리 지점으로부터 보내어지는 영상을 조합하여 받을 수 있다. 프로그램 구현 개념은 픽셀 소스 카메라에서 캡쳐된 최초 이미지를 명도, 색상, 채도로 분류하고, 이것의 각각을 26개의 구간-자판의 개수에 의하여 정해짐-으로 나누고, 다시 그 각각의 구간을 26단계로 나눈다. 이렇게 나누어진 구간들은 알파벳과 1:1로 대응시켜 결과이미지의 해당 부분을 수치대로 재현하도록 프로그램 시킨다. 이미지의 부분들을 지정하기 위하여 특정한 텍스트로부터 알파벳의 빈도수를 조사했는데, 이 조사를 바탕으로 빈도수의 많고 적음에 따라 이미지부분들이 26개 구간으로 정해졌다. 이미지 재현 방법은 사용자가 모니터 위의 Result Image Cam을 통하여 얻고자 하는 이미지를 캡쳐한 후, 특정한 텍스트를 타이핑하면 이미지를 재현할 수 있는데, -입력된 텍스트의 알파벳은 프로그램이 지정한 HSI 컬러 모델의 영역과 1:1로 대응하게 하였다-이 이미지는 특정 장소에 실시간으로 받아진 영상을 데이터화 한 소스에 의하여 만들어진다. 이미지를 재현할 때 텍스트에 따라 알파벳 빈도수는 달라질 수 있으므로 비록 최초 이미지가 동일할지라도 얻고자하는 결과 이미지가 달라진다. 그러므로 사용자는다양한 창조적 경험을 할 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.