Barrier coverage is an appropriate coverage model for intrusion detection by constructing sensor barriers in wireless sensor networks. In this paper, we focus on the problem how to relocate mobile sensors to construct k sensor barriers with minimum energy consumption. We first analyze this problem, give its Integer Linear Programming(ILP) model and prove it to be NP-hard. Then we devise an approximation algorithm AHGB to construct one sensor barrier energy-efficiently, simulations show that the solution of AHGB is close to the optimal solution. Based on AHGB, a Divide-and-Conquer algorithm is proposed to achieve k-barrier coverage for large sensor networks. Simulations demonstrate the effectiveness of the Divide-and-Conquer algorithm.

In computer vision field, image classification as a basic image processing task has been widely concerned. This paper puts forward an efficient attention network with dilated convolution named Dilated-CBAM for image classification. The dilated convolution is applied in the backbone of the residual network to extract the residual edge path features and integrate the global information of the processed image. The amount of network parameters is greatly reduced while the receptive field is expanded, and the network parameters are learnable. By embedding our spatial attention mechanism and channel attention mechanism, the model uses FCN to strengthen the effective information in the image, weaken the invalid information, and summarize the local features of the processed image. Combining the global information and local information, the time and space for network training are saved, while the effective image features can be extracted better. In the design of attention module, this work innovatively applies residual path in attention module for combining context information inside attention mechanism without adding parameters, and helps attention module extract features of interest in image classification task more accurately. In image classification, experiment, we verify the classification effect of the Dilated-CBAM model on Cifar-10 dataset, which is 2.5% higher than ResNet-18, and reaches the classification accuracy of 93.5%. In terms of the efficiency of network training, the Dilated-CBAM reduces the number of training epochs to about 10 on the basis of CBAM model, shortens the training time to about half of the original, which greatly testifies the training efficiency.

Gesture recognition is an important way of human-computer interaction. With time going on, people are no longer satisfied with gesture recognition based on wearable devices, but hope to perform gesture recognition in a more natural way. Computer vision-based gesture recognition can transfer human feelings and instructions to computers conveniently and efficiently, and improve the efficiency of human-computer interaction significantly. The gesture recognition based on computer vision is mainly based on hidden Markov, dynamic time rounding algorithm and neural network algorithm. The process is roughly divided into three steps: image collection, hand segmentation, gesture recognition and classification. This paper reviews the computer vision-based gesture recognition methods in the past 20 years, analyses the research status at home and abroad, summarizes its current development, the advantages and disadvantages of different gesture recognition methods, and looks forward to the development trend of gesture recognition technology in the next stage.

Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitching methods based on hand-crafted features are effective for constructing a cylindrical panorama from a sequence of images in the case when there are sufficient reliable features in the scene. However, these methods are unable to handle low-texture environments where no reliable feature correspondence can be established. This paper proposes a novel two-step image alignment method based on deep learning and iterative optimization to address the above issue. In particular, a light-weight end-to-end trainable convolutional neural network (CNN) architecture called ShiftNet is proposed to estimate the initial shifts between images, which is further optimized in a sub-pixel refinement procedure based on a specified camera motion model. Extensive experiments on a synthetic dataset, rendered photo-realistic images, and real images were carried out to evaluate the performance of our proposed method. Both qualitative and quantitative experimental results demonstrate that cylindrical panorama stitching based on our proposed image alignment method leads to significant improvements over traditional feature based methods and recent deep learning based methods for challenging low-texture environments.

Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inputs in most methods, often results in a certain loss of global structure information. Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details. To overcome these drawbacks, we introduce PointCFormer, a transformer framework optimized for robust global retention and precise local detail capture in point cloud completion. This framework embraces several key advantages. First, we propose a relation-based local feature extraction method to perceive local delicate geometry characteristics. This approach establishes a fine-grained relationship metric between the target point and its k-nearest neighbors, quantifying each neighboring point's contribution to the target point's local features. Secondly, we introduce a progressive feature extractor that integrates our local feature perception method with self-attention. Starting with a denser sampling of points as input, it iteratively queries long-distance global dependencies and local neighborhood relationships. This extractor maintains enhanced global structure and refined local details, without generating substantial computational overhead. Additionally, we develop a correction module after generating point proxies in the latent space to reintroduce denser information from the input points, enhancing the representation capability of the point proxies. PointCFormer demonstrates state-of-the-art performance on several widely used benchmarks.