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The vision-based lane detection is an important component of advanced driver assistance systems and it is essential for lane departure warning, lane keeping, and vehicle localisation. However, it is a challenging problem to improve the robustness of multi-lane detection due to factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues,...
The authors propose a novel and efficient method for single image dehazing. To accelerate the transmission estimation process, a block-to-pixel interpolation method is used for fine dark channel computation, in which the block-level dark channel is first computed, and then the fine pixel-level dark channel is obtained by a weighted voting of the block-level dark channel to preserve edges and smooth...
This paper introduces a discriminative framework for the task of vehicle detection based on Hough Forest. The leaf nodes in Hough Forest framework are not discriminative enough, which means that they do not have the ability to classify whether the test patches ended up in each leaf are positive or negative. Hough votes are assigned to all test patches by Hough forest, including negative test patches,...
In this paper, we propose an effective method for the detection of objects, such as vehicles or pedestrians, in static images. Hough Forest based object detection methods have received a lot of attention in recent years, and have achieved the state-of-the-art detection accuracy. In this study, rather than treating each voting element in the leaf equally as most of the previous works have done, we...
The idea of safe and smart vehicles has been thoroughly researched over the past decades to ensure drivers' safety from possibly dangerous situations. This paper presents a brief review of different applications of image processing and computer vision techniques in smart vehicles. To detect other on-road vehicles, researchers have approached the problem from various angles; with solutions ranging...
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