• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1411-1418
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• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.203-207
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• 2006

This paper deals with a problem of detecting BCC using confocal raman spectrum. Specially, we propose Fisher measure based filtering for rejection of frequency components being noisy or non-discriminative. we use PCA (principal component analysis) for reduction of feature space dimension. Also, we apply MAP detector for classification of BCC raman spectrum. The experimental results shows that our proposed method can reduce the feature dimension and also raise the detection ratio.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1195-1207
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• 2005

In this paper, we describe image matching techniques which is automatically retrieving the exact matching area using geometry-phase information. We proposed a Matching Method which is rapidly estimating the correspondent points between adjacent images that included big-rotation and top-bottom movement element. It is a method that reduce computation quantity to be required to find an exact correspondent position using geometry-phase information of extracted points in images and DT map which set the distance value among feature points and other points on the basis of each feature point of a image. The proposed method shows good performance especially in the part to search a exact correspondent position between adjacent images that included big-rotation and top-bottom movement element.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.736-746
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• 2016

In this paper, we propose an automatic prostate cancer detection method using position, signal intensity and texture feature based on SVM in multi-parametric MR images. First, to align the prostate on DWI and ADC map to T2wMR, the transformation parameters of DWI are estimated by normalized mutual information-based rigid registration. Then, to normalize the signal intensity range among inter-patient images, histogram stretching is performed. Second, to detect prostate cancer areas in T2wMR, SVM classification with position, signal intensity and texture features was performed on T2wMR, DWI and ADC map. Our feature classification using multi-parametric MR imaging can improve the prostate cancer detection rate on T2wMR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.2
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• pp.331-336
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• 2017

The need for early fire detection technology is increasing in order to prevent fire disasters. Sensor device detection for heat, smoke and fire is widely used to detect flame and smoke, but this system is limited by the factors of the sensor environment. To solve these problems, many image-based fire detection systems are being developed. In this paper, we implemented a system to detect fire and smoke from camera input images using a convolution neural network. Through the implemented system using the convolution neural network, a feature map is generated for the smoke image and the fire image, and learning for classifying the smoke and fire is performed on the generated feature map. Experimental results on various images show excellent effects for classifying smoke and fire.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.452-460
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• 1994

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading To automatically extract a road map directly from more complicated topographical maps, a very complicated algorithm is needed, since the image generally involves such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.370-372
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• 2006

본 논문에서 제안하는 방법은 영상에서 중심 객체를 추출하기 위해 에지와 색상 정보에서 추출한 특집 지도와 배경의 영향을 줄이기 위친 창조 지도(reference map)를 제안한 것이 특징이다. 특징 지도는 다른 영역과 현저하게 구분되는 영역을 검출하기 위해서 영상의 특징 값(feature)들을 이용해서 구성한 영상이라고 할 수 있다. 그리고 창조 지도는 배경의 영향을 최소화하면서, 객체가 존재할 확률이 높은 부분을 나타내는 지도이다. 제안하는 방법은 밝기 차 정보를 가지고 있는 에지와 YCbCr 컬러모델과 HSV 컬러모델의 색상 성분을 특징 값으로 사용한다. 이들 특징 값을 이용해서 특징 지도를 구성하는 방법으로 영상 내 색상 차에 의해서 나타나는 경계부분을 구하는 방법을 사용한다. 이 방법을 사용하여 에지 지도와 두 개의 색상 지도의 3가지 특징 지도를 생성한다. 다음으로, 영상 배경의 영향을 줄이기 위해 참조 지도를 구한다. 구해진 참조 지도와 특징 지도들을 이용해서 결합 지도(combination map)를 생성한다. 결함 지도로부터 다각형의 객체 후보 영역을 구하고, 객체 후보 영역에 영상분할을 적용하여 중심 객체를 추출한다. 실험에 사용된 영상들은 Corel DB를 사용하였으며, 실험결과로써 precision은 84.3%, recall은 81.3%의 성능을 보인다.

• International Journal of Automotive Technology
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• v.7 no.3
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• pp.361-367
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• 2006

This paper describes a novel stereo vision based localization of free parking site, which recognizes the target position of automatic parking system. Pixel structure classification and feature based stereo matching extract the 3D information of parking site in real time. The pixel structure represents intensity configuration around a pixel and the feature based stereo matching uses step-by-step investigation strategy to reduce computational load. This paper considers only parking site divided by marking, which is generally drawn according to relevant standards. Parking site marking is separated by plane surface constraint and is transformed into bird's eye view, on which template matching is performed to determine the location of parking site. Obstacle depth map, which is generated from the disparity of adjacent vehicles, can be used as the guideline of template matching by limiting search range and orientation. Proposed method using both the obstacle depth map and the bird's eye view of parking site marking increases operation speed and robustness to visual noise by effectively limiting search range.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.