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Semantic segmentation of urban street scenes has attracted much attention in the field of autonomous driving, which not only helps vehicles perceive the environment in real time, but also significantly improves the decision-making ability of autonomous driving systems. However, most of the current methods based on Convolutional Neural Network (CNN) mainly use coding the input image to a low resolution and then try to recover the high resolution, which leads to problems such as loss of spatial information, accumulation of errors, and difficulty in dealing with large-scale changes. To address these problems, in this paper, we propose a new semantic segmentation network (HRDLNet) for urban street scene images with high-resolution representation, which improves the accuracy of segmentation by always maintaining a high-resolution representation of the image. Specifically, we propose a feature extraction module (FHR) with high-resolution representation, which efficiently handles multi-scale targets and high-resolution image information by efficiently fusing high-resolution information and multi-scale features. Secondly, we design a multi-scale feature extraction enhancement (MFE) module, which significantly expands the sensory field of the network, thus enhancing the ability to capture correlations between image details and global contextual information. In addition, we introduce a dual-attention mechanism module (CSD), which dynamically adjusts the network to more accurately capture subtle features and rich semantic information in images. We trained and evaluated HRDLNet on the Cityscapes Dataset and the PASCAL VOC 2012 Augmented Dataset, and verified the model's excellent performance in the field of urban streetscape image segmentation. The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L L -based variational retinex (AD- L L VR) for non-uniform illumination endoscopic images. Firstly, a haze-free endoscopic image is obtained by an anti-degraded model named dark channel prior (DCP). For getting a more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to the naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between the detail component of the haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using the proposed L L -based variational retinex ( L L VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods. [ABSTRACT FROM AUTHOR]

Correlation filter-based trackers have recently shown excellent performance in terms of motion blur and illumination changes, but they are notoriously sensitive to deformation. It has been demonstrated that the combination of the correlation filter-based tracker and the color histogram-based tracker can alleviate the deformation and keep advantages of the correlation filter-based tracker. However, the most existing complementary tracking algorithms, which use fixed complementary weights, limit the performance of every sub tracker. This paper introduces an adaptive complementary tracker by online learning dynamic complementary weights. The strategy enables inappropriate sub tracker to be down-weighted while increasing the impact of suitable one. We jointly learn the sub trackers and their reliability weights by regression analysis of the corresponding historical tracking results. The robustness of the model also can be improved by training each sub tracker with the result of historical tracking. Finally, both qualitative and quantitative evaluations demonstrate that our tracker achieves the state-of-the-art results in a wide range of tracking scenarios. [ABSTRACT FROM AUTHOR]