• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.199-205
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• 2011

This paper presents an algorithm capable of detecting free space for the autonomous vehicle navigation. The algorithm consists of two main steps: 1) estimation of longitudinal profile of road, 2) detection of free space. The estimation of longitudinal profile of road is detection of v-line in v-disparity image which is corresponded to road slope, using v-disparity image and hough transform, Dijkstra algorithm. To detect free space, we detect u-line in u-disparity image which is a boundary line between free space and obstacle's region, using u-disparity image and dynamic programming. Free space is decided by detected v-line and u-line. The proposed algorithm is proven to be successful through experiments under various traffic scenarios.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.428-435
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• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2343-2352
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• 2015

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.107-115
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• 2002

In this paper, we describe a novel method to generate an intermediate scene using BDM (Bidirectional Disparity Morphing) from the parallel stereopair. Because an image is composed of several layers and each layer has a similar disparity, it is available to use the block based disparity estimation. In order to prevent the false correspondence, however, we closely investigate the corresponding block as we adaptively vary the block size according to the estimation error. Therefore, we can detect the occlusion because of larger estimation error of the occluded region. We define three occluding patterns, which ate derived from the peculiar property of the disparity map, in order to smooth the computed disparity map. The filtered disparity map using these patterns presents that the false disparities ate well corrected and the boundary between foreground and background becomes sharper. As a result, we can improve the quality of the intermediate scenes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.353-361
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• 2001

A new stereoscopic codec. structure using MPEG-2 multiview profile is presented in this paper. In the suggested codec., the left image is coded with motion estimation in the base layer and the right image is coded with disparity estimation in the enhancement layer. Since it is possible to calculate rough motion of the right image sequence with disparity and motion of the left image sequence, motion compensation of the enhancement layer is performed without motion estimation. To apply this mathod to MVP codec., the prediction mode of base layer and enhancement layer is restricted, and B picture mode in the base layer is removed. Since the proposed codec. does not perform motion estimation in the enhancement layer encoding and prediction mode of base layer is restricted, it's structure is simple and reduces the encoding time. We compared the SNR of encoded image with three different structured codec., and the experimental results show suggested codec. have comparable result.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2477-2479
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• 2004

본 논문에서는 차선관련 정보의 변이도함수(DLI, Disparity of Lane-related Information)를 기반으로 하는 선행차량 인식 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘을 이용하여, 주행차선내에 있는 선행차량의 유무 검출과 위치 유추 및 선행차량 인식을 수행한다. DLI를 이용하는 방법은 특징점의 탐색공간을 현저히 줄여 실시간 처리문제를 해결한 수 있는 장점을 가지고 있다. 본 논문에서는 제안된 선행차량 인식알고리즘의 성능을 검증하기 위하여 다양한 환경의 도로영상에 알고리즘을 적용하여, 제안된 선행차량 인식기법의 우수함을 확인하였다.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2459-2461
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• 2003

본 논문은 차량 내에 설치된 두 대의 CCD 카메라를 이용하여 도로 영상으로부터 주행차선내에 있는 장애물을 인식하는 새로운 알고리즘을 제시한다. 제안된 알고리즘은 주행하는 차선과 관련이 있는 차선 정보만을 이용하여, 스테레오 영상에서 변이도를 추출할 수 있는 변이도 함수인 DLI(Disparity of lane-related information)를 정의하였다. DLI는 선행 차량과 같은 장애물은 주위보다 상대적으로 큰 에지값을 가진다는 특성을 이용하여, 주행차선 내에 있는 장애물의 유무를 검출하고 위치를 유추한다. 제안된 방법은 특징점의 탐색공간을 현저히 줄여 실시간 처리문제를 해결한 수 있는 장점을 가지고 있다. 본 논문에서는 DLI를 이용한 선행차량 인식기법의 성능을 검증하기 위하여 다양한 환경의 도로영상에 알고리즘을 적용하여 제안한 방법의 우수함을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2251-2259
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• 2009

We propose an algorithm and system which generates 3D stereo image by composition of 2D image from 4 multiple clusters which 1 cluster was composed of 4 multiple cameras based on network. Proposed Schemes have a network-based client-server architecture for load balancing of system caused to process a large amounts of data with real-time as well as multiple cluster environments. In addition, we make use of JPEG compression and RAM disk method for better performance. Our scheme first converts input images from 4 channel, 16 cameras to binary image. And then we generate 3D stereo images after applying edge detection algorithm such as Sobel algorithm and Prewiit algorithm used to get disparities from images of 16 multiple cameras. With respect of performance results, the proposed scheme takes about 0.05 sec. to transfer image from client to server as well as 0.84 to generate 3D stereo images after composing 2D images from 16 multiple cameras. We finally confirm that our scheme is efficient to generate 3D stereo images in multiple view and multiple clusters environments with real-time.