• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.168-176
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• 2014

Detecting moving objects from a video sequence is a fundamental and critical task in video surveillance, traffic monitoring and analysis, and human detection and tracking. It is very difficult to detect moving objects in a video sequence degraded by the environmental factor such as fog. In particular, the color of an object become similar to the neighbor and it reduces the saturation, thus making it very difficult to distinguish the object from the background. For such a reason, it is shown that the performance and reliability of object detection and tracking are poor in the foggy weather. In this paper, we propose a novel method to improve the performance of object detection, combining a haze removal algorithm and a local histogram-based object tracking method. For the quantitative evaluation of the proposed system, information retrieval measurements, recall and precision, are used to quantify how well the performance is improved before and after the haze removal. As a result, the visibility of the image is enhanced and the performance of objects detection is improved.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.31-39
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• 2010

The paper proposes the median filter based Retinex algorithm to detect the lanes in a foggy image. Whether an input image is foggy or not is determined by analyzing the histogram in the pre-defined ROI(Region of Interest). If the image is determined as a foggy one, then it is improved by the median filter based Retinex algorithm. By replacing the Gaussian filter by the median filter in the Retinex algorithm, the processing time can be reduced and the lane features can be detected more robustly. Once the enhanced image is acquired, the binarization based on multi-threshold and the labeling operations are applied. Finally, it detects the lane information using the size and direction parameters of the detected lane features. The proposed algorithm has been evaluated by using various foggy images collected on different road conditions to prove that it detects lanes more robustly in most cases than the conventional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.376-380
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• 2015

This paper proposes new recognition method of road lanes misty day. The method enables autonomous-driving of cars and the safety of the drivers while driving with bad visibility in foggy roads. The proposed method, firstly, determines whether the foggy or not according to pixel number distributions and starting point of the fog period from input images. If it is foggy then the median filter's size of the Retinex algorithm is set to 1000 or more and it performs histogram equalization and normalization. The computer simulation results show that the proposed method can recognize better long distances and fine detection than earlier methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.260-263
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• 2017

본 논문은 single image에서 측정한 빛 전달량 값과 local contrast 값을 사용하여 안개 량을 수치화하는 방법을 제안한다. 제안하는 방법은 빛 전달량 값을 사용하여 안개로 예측되는 지역을 추정하고, 추정된 안개 예측지역의 넓이와 해당 지역의 local contrast 크기의 범위를 사용하여 안개 정도를 수치화 한다. single image에서 측정 가능한 안개 의 물리적 특성들을 고려하였기 때문에 기존의 안개 검출 알고리즘들이 구분하지 못했던 영상들에서도 안개 량을 정확하게 측정하였다. 실제 빛의 산란 정도를 측정하는 감광 계수 측정계를 사용하여 측정한 안개 량과 제안하는 방법의 수치를 비교했을 때, 다양한 환경과 물체를 포함한 영상들에서 95%이상의 정확도로 안개 정도를 수치화 하였다. 또한 빛 전달량 추정 과정에서 local contrast 값을 추출하여 사용하기 때문에 기존의 빛 전달량을 측정하는 방법에서 복잡도를 거의 증가시키지 않는다.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.272-275
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• 2016

This paper presents a method of single image dehazing and surface-based feature detection for remote sensing images. In the conventional dark channel prior (DCP) algorithm, the resulting transmission map invariably includes some block artifacts because of patch-based processing. This also causes image blur. Therefore, a refined transmission map based on a hidden Markov random field and expectation-maximization algorithm can reduce the block artifacts and also increase the image clarity. Also, the proposed algorithm enhances the accuracy of image matching surface-based features in an remote sensing image. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal. Moreover, the proposed algorithm is suitable for the problem of image matching based on feature extraction.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.359-362
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• 2013

최근 지능형 차량에 대한 관심이 늘어나면서 차선검출에 대한 관심도 많이 늘어나고 있다. 그 중에서도 실시간 적용을 위하여 연산량이 적은 허프변환을 이용한 차선검출 방법이 많이 연구되고 있다. 하지만 허프변환은 안개와 같은 열악한 환경에서와 같이 에지성분이 두드러지게 나타나지 않은 경우에 대해서는 정확한 차선검출이 어렵다는 단점을 가지고 있다. 따라서 본 논문에서는 열악한 환경의 영상에 차선의 에지를 강조하는 전처리를 수행하고 허프변환을 이용하여 차선검출을 수행하는 방식을 제시한다. 제안하는 전처리 방법은 처리 속도와 성능에서 기존의 전처리 기법과 비교하여 높은 처리속도와 차선 검출률을 보였다. 특히 안개와 같은 열악한 영상에서의 결과에서 기존의 전처리 방법보다 제안한 전처리 방법이 더 높은 검출률을 보였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.70-70
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• 2017

영상처리 기법을 이용한 유속 측정 방법인 표면영상유속계는 비접촉식으로 간편하게 유속을 측정할 수 있다는 장점이 있지만 영상 내 추적자의 움직임을 식별하기 어려운 야간의 경우와 새벽의 안개가 발생하는 경우에 대한 유속 측정의 어려움이 있었다. 표면영상유속계를 이용한 야간 유속 측정은 조명과 적외선 카메라를 이용하여 수표면을 가시화하는 방법을 통해 현장 적용성을 검증하였으나, 안개 발생 상황에서는 적용하기 어렵다는 한계가 있었다. 야간과 안개 등의 한계를 동시에 극복하기 위한 방법으로 원적외선 카메라를 이용한 연구들이 이루어지고 있지만 아직 시작단계이고, 원적외선의 경우 주변 환경 변화에 따라 물체의 표면온도가 검출되는 파장이 달라져 영상의 품질에 차이가 발생하기 때문에 이에 대한 다양한 실험적 연구가 필요하다. 이에 본 연구에서는 야외 개수로에서 드라이아이스를 이용하여 안개 조건을 재현하고, 다양한 흐름 조건에서 원적외선 영상을 이용한 표면유속 측정 적용성을 검토하였다. 안개가 발생하는 경우 원적외선 표면영상 유속계를 적용한 결과 안개가 없을 때의 유속 측정 결과와 거의 일치하는 것을 확인하였다. 따라서 원적외선 카메라를 이용한 표면유속 측정 방법은 야간과 안개가 발생하는 상황에 모두 사용하기에 적합한 것을 나타났다. 향후 하천 유량조사에 원적외선 카메라를 활용한다면 기존의 표면영상유속계의 비가시 환경에 대한 한계들을 많은 부분 극복할 수 있을 것으로 기대한다.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.375-380
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• 2017

This paper has proposed a single image based fog degree quantification method by measuring both transmission and local contrast. The proposed method estimates the foggy expected regions from transmission, and then assesses the size of regions of which transmission values are foggy expected ones and the range of local contrast value on such regions. Compared with fog degree gauged by the scattering coefficient measurement sensor, the proposed method quantifies the fog degree with more than 95% accuracy for images containing various objects and environments. We also developed a technique that measures the local contrast values in process of measuring transmission values. So, the proposed method does not increase complexity compared to the existing transmission method.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1635-1640
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• 2017

In this paper, a hierarchical image tree model for weather classification is defined in a single outdoor image, and a weather classification algorithm using image intensity and k-mean segmentation image is proposed. In the first level of the hierarchical image tree model, the indoor and outdoor images are distinguished. Whether the outdoor image is daytime, night, or sunrise/sunset image is judged using the intensity and the k-means segmentation image at the second level. In the last level, if it is classified as daytime image at the second level, it is finally estimated whether it is sunny or foggy image based on edge map and fog rate. Some experiments are conducted so as to verify the weather classification, and as a result, the proposed method shows that weather features are effectively detected in a given image.