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Abstract Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future.

Background: Monocular depth estimation aims to predict the dense depth map from a single RGB image, which has important applications in 3D reconstruction, automatic driving, and augmented reality. However, existing methods directly feed the original RGB image into the model to extract depth features without avoiding the interference of depth-irrelevant information on depth estimation accuracy, which leads to inferior performance. To remove the influence of depth-irrelevant information and improve depth prediction accuracy, we propose RADepthNet, a novel reflectance-guided network fusing boundary features. Specifically, our method predicts depth maps using three steps: 1) Intrinsic Image Decomposition. We propose a Reflectance extraction module consisting of an encoder-decoder structure to extract depth-related reflectance. We demonstrate that the module can reduce the influence of illumination on depth estimation through an ablation study. 2) Boundary Detection. Boundary extraction module, consisting of an encoder, a refinement block, and an upsample block, is proposed to better predict depth at object boundaries utilizing gradient constraints. 3) Depth Prediction Module. Use a different encoder from 2) to obtain depth features from the reflectance map and fuse boundary features to predict depth. Besides, we proposed FIFADataset, a depth estimation dataset applied in soccer scenarios. Extensive experiments on the public dataset and our proposed FIFADataset show that our method achieves state-of-the-art performance.

Abstract A significant performance boost has been achieved in point cloud semantic segmentation by utilization of the encoder-decoder architecture and novel convolution operations for point clouds. However, co-occurrence relationships within a local region which can directly influence segmentation results are usually ignored by current works. In this paper, we propose a neighborhood co-occurrence matrix (NCM) to model local co-occurrence relationships in a point cloud. We generate target NCM and prediction NCM from semantic labels and a prediction map respectively. Then, Kullback-Leibler (KL) divergence is used to maximize the similarity between the target and prediction NCMs to learn the co-occurrence relationship. Moreover, for large scenes where the NCMs for a sampled point cloud and the whole scene differ greatly, we introduce a reverse form of KL divergence which can better handle the difference to supervise the prediction NCMs. We integrate our method into an existing backbone and conduct comprehensive experiments on three datasets: Semantic3D for outdoor space segmentation, and S3DIS and ScanNet v2 for indoor scene segmentation. Results indicate that our method can significantly improve upon the backbone and outperform many leading competitors.

Face alignment has made great progress in recent years and the cascade regression framework is one of the main contributors. However, the performance of this framework is unsatisfactory on heavily occluded faces or those far from the frontal pose. This is because regression is sensitive to hidden landmarks and unified initialisation can often lead to the method falling into local minima. The authors propose a new pipeline of salient‐to‐inner‐to‐all to progressively compute the locations of landmarks. Additionally, a feedback process is utilised to improve the robustness of regression. They bring out a pose‐invariant shape retrieval method to generate the discriminative initialisation. Experiments are performed on two benchmarks, and the experimental results demonstrate that the proposed method has a considerable improvement on the cascade regression model, and achieves favourable results compared with the state‐of‐the‐art deep learning‐based methods.

Patch-based texture synthesis techniques from a small sample texture generate a new texture image efficiently, but one significant problem is that these existing texture synthesis algorithms suffer from structure mismatching across patch boundaries due to inaccurate similarity measure. In this paper, we propose a desired distance metric to measure structural similarity when especially handling textures with salient features, which has the ability to find best matches during the patch-based sampling. To do this, we introduce the concept of “layers” to describe the correlation of pixels through computing each pixel weight, and construct the orientation fields to capture the inherent direction and variation of texture features through computing the dominant local orientation angle and the degree of anisotropy of each pixel. Our experimental results demonstrate that the proposed method can significantly alleviate structure discontinuities in synthesized textures.

Different from existing techniques3-5 that often focus on high level data integration, this paper contributes a novel solution at device level, implementing a fully scalable and configurable middleware, aiming at releasing the power of parallel processing with distributed data. It is designed as a soft-gateway to link upper-level PCs with lower-level data transfer units, and implemented by extracting the key part of data communication out of its original holder such that the UI only cares about how to display data.