Semantic segmentation is crucial in autonomous driving, offering exceptional scene understanding to tackle challenges like small object edges and blurred textures in complex traffic environments. By performing pixel-level classification, it provides vehicles with comprehensive environmental information, ensuring safe navigation. However, when dealing with fine, fuzzy-bordered objects in complex scenes, the existing techniques face the issue of low segmentation accuracy due to their insufficient feature extraction capability. To address this problem, this study proposes a semantic segmentation network that combines Gaussian perception with iterative multi-scale attention. The method integrates Gaussian perception with local–global channel attention, accurately models pixel associations, and dynamically focuses on features to address the issue of edge blurring in complex scene segmentation. At the same time, the method employs the difference module to enhance the low-frequency features, and integrates the iterative multi-scale attention mechanism to achieve deep integration of low-frequency and high-frequency information. This enhances the fine capture of features and mitigates the edge discontinuity issue in segmentation caused by the masking of boundary information. In addition, the method combines channel and spatial attention to optimize feature extraction, enhance the sensory field, and improve detail, context, and boundary recognition abilities. This significantly improves the feature expression ability and reduces the probability of background mis-segmentation. The experimental results show that the proposed method achieves 79.34% mIoU on the Cityscapes validation set (a 1.32% improvement over PIDNet-S) and 81.48% mIoU on the CamVid test set (a 1.05% improvement over PIDNet-S). These results demonstrate significant improvements over existing state-of-the-art methods, confirming the effectiveness of this approach in semantic segmentation of complex urban scenes. The source code has been made publicly available on GitHub: https://github.com/wgsheng897/GMSANet.git

Exposure problems often affect image quality more significantly than noise and blurring, seriously affecting their effectiveness in computer vision applications. To address this core problem, this paper proposes an innovative deep-learning method. The method uses pyramidal multi-scale decomposition to repair the image layer by layer, handling both overexposure and underexposure problems. Firstly, we introduce the Strengthen-Operate-Subtract (SOS) feature enhancement module, which refines and enhances the image by building previously estimated images to make the exposure correction effect more pronounced. Additionally, we propose using the Multi-scale Aggregation Back-projection (MAB) module to improve the U-Net model. Through the error feedback mechanism, the features of non-adjacent layers are compensated and integrated effectively, and the loss of detailed information is reduced. We introduce a dense block of matte residuals at the maximum scale to expand the network's receptive field and effectively capture local information. These innovations significantly enhance image exposure correction, reducing detail loss, regional contrast saturation, and color imbalance. Extensive evaluations confirm the effectiveness of our model, which demonstrates superior performance across all datasets, particularly in PSNR, achieving an average value of 26.271.

With the development of artificial intelligence, computer vision technology has been widely used in the fields of security monitoring, automatic driving and wisdom city. However, there has not been a research on the detection of the meridians in human bodies by using the computer vision technology. In order to promote the use of the computer vision technology in human meridian detection, this paper first releases a dataset based on human meridians, which makes up for the gap in the field of human meridian detection using image processing technology. Moreover, the human meridian detection dataset is manually annotated and proofread by experienced Traditional Chinese Medicine (TCM) practitioners according to the position and direction of the human meridians, so that the annotated human meridians are as accurate as possible. The released human meridian dataset label’s 12 meridians, including spleen meridian, pericardium meridian, stomach meridian, lung meridian, heart meridian, kidney meridian, gallbladder meridian, liver meridian, triple energizer meridian, bladder meridian, large intestine meridian and small intestine meridian. A total of 296 acupoints were labeled. At last, this paper proposes a method for data augmentation, especially for datasets with a small amount of data, wherein the data amount can be augmented by enhancing the underlying edge visual features of the data. Experimental results show that human meridians can be detected by using image processing technology, and the proposed method for data augmentation can effectively improve the detection accuracy of human meridians. The dataset can be downloaded from https://www.zksylf.com/col.jsp?id=127 .

Automated object measurement is becoming increasingly important due to its ability to reduce manual costs, increase production efficiency, and minimize errors in various fields. In this paper, we present a novel approach to three-dimensional (3D) object measurement based on point cloud modeling. Our method introduces a fast point cloud modeling computation framework consisting of five stages: coordinate centralization, rotation and translation, noise filtering, plane projection, and geometric computation. Furthermore, we propose a fast convex hull optimization algorithm to reduce the high complexity problem of traditional convex hull calculation. Our extensive experiments demonstrate that our approach outperforms existing methods in terms of measurement error rate and time savings, with a maximum time saving of 31.03% under certain error conditions.

With the exponential growth of images, the accuracy of image retrieval for improving the performance of rank layer and feature layer in content-based image retrieval (CBIR) becomes more and more attractive. Better single feature search results will further enhance the effect. Better sorting results will robust the performance. Therefore, this paper focuses on rank reordering to improve the performance of image retrieval. We propose a rank-level framework for feature reordering based on hierarchical undirected graphs. First, we calculate the K Nearest Neighbors for each image in the database. Then, we propose a method based on the combination of reciprocal nearest neighbor and K nearest neighbor to construct an undirected graph, and a method for calculating the image distance based on the shared nearest neighbor. An undirected graph is used to represent the similarity relationship of an image, where the vertices are composed of pictures and the edges are weighted according to the distance of the pictures. Finally, based on the constructed undirected graph in this paper, we perform sorting optimization. Experiments on four public data sets demonstrate the effectiveness of the method and are challenging.

The aim of this paper is to improve the optical flow accuracy for the preservation of flow discontinuities in total variation method. The proposed method is based on the total variation of the local-global method, including the following steps: firstly, the initial optical flow is obtained from the local-global method; secondly, the data item and the smoothing item are designed based on initial optical flow by using the total variation, with the data item involving the brightness constancy and the gradient constancy, and the smoothing item involving exponential function; and finally, the optical flow results from the second step are optimized by using median filtering. The experimental results show that our proposed method could enhance the accuracy and robustness of the optical flow on the Middlebury Dataset and the MPI Sintel Dataset.