Abstract MRI and CT images have been routinely used in clinical practice for treatment planning of the head‐and‐neck (HAN) radiotherapy. Delineating organs‐at‐risk (OAR) is an essential step in radiotherapy, however, it is time‐consuming and prone to inter‐observer variation. The existing automatic segmentation approaches are either limited by image registration or lack of global spatial awareness, thus under‐performed when dealing with segmentation of complex anatomies. Herein, we propose a full‐scale attention network (FSANet) that integrates bi‐side skip connections, full‐scale feature fusion modules (FFM), a feature pyramid fusion and supervision module (FPFSM) to accurately and efficiently delineate OARs in HAN region on CT and MRI scans. Specifically, bi‐side skip connections were adopted to keep small targets in the deep network and to capture semantic features at different scales. The FFM with cascaded attention mechanisms were used to recalibrate the significant channels and salient regions in the feature maps. The FPFSM was used to guide the network to learn the hierarchical representation so as to improve the segmentation robustness. The proposed algorithm was validated on the public benchmark HAN CT dataset and an in‐house MR dataset. Both results show significant improvement compared to state‐of‐the‐art OAR single‐stage segmentation methods for the HAN region.

Predicting gaze point on mobile devices without calibration in unconstrained environments has great significance on human computer interaction. Appearance-based gaze estimation methods have been improved due to the recent advance in convolutional neural network (CNN) models and the availability of large-scale datasets. CNN models have limitations on extracting the global information of features and ignore the important information of local features. In this paper, we propose a novel structure named GazeAttentionNet. To improve the accuracy of gaze estimation, we use the global and local attention modules to utilize both global and local features. Firstly, we use MobileNetV2 and the self-attention layers as the global attention module to extract global features. Secondly, we add the local attention module containing the spatial attention to extract local features. With GazeAttentionNet, we achieve an excellent result on the GazeCapture dataset. The average errors of mobile phones and tablets are 1.67 cm and 2.37 cm.

In this paper, we propose a gaze-based interactive system for metro map named GazeMetro, which helps explore and interact with the metro map only by eye movements, providing a new experience of interaction. The objective of GazeMetro is to provide metro map viewers with gaze-based interactions to search the metro map without other manual operations. We implement GazeMetro with 4 gaze-based interaction techniques which are Gaze Fisheye, Gaze Scaling and Panning, Gaze Selection and Gaze Hint. We conducted an experiment to evaluate GazeMetro and the results showed a positive evaluation in pragmatic quality and especially in hedonic quality.

Polar Fourier analysis can extract orthogonal and rotation invariant features and demonstrate superior performances in many image processing tasks. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this research presents GPU accelerated polar Fourier analysis. Proposed parallel algorithm is based on mathematical properties of polar Fourier analysis and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result proposed method is over 170 times faster. Wide range of applications that using polar Fourier analysis will inspired from this work.

Ambient Occlusion (AO) is a technique to approximate the effect of environment lighting and add realism to a scene by accentuating surface details and adding soft shadows, which is widely used in multimedia applications. Neural Network Ambient Occlusion (NNAO) is a pioneer in introducing deep learning to accurate and real-time AO, but it has two limitations: 1) performance bottleneck under excessive amount of samples ; 2) low contrast and blurred edges leading to unreal effect. To overcome these two limitations, we propose Accelerated Detail-enhanced Ambient Occlusion (ADAO) method based on NNAO by adopting three image processing methods: 1) spiral sampling in screen space; 2) contrast enhancement of AO map; 3) normal-depth edge preserving bilateral filtering. Experimental results show that the proposed method is over 2 times faster than NNAO and produces shadows with more realistic details.

Due to the limitation of existing filters in detection and removal of fixed value impulse noise, the authors propose an iterative grouping median filter (IGMF) according to the characteristics of noise intensity and distribution. It sorts the noise‐free pixels in neighbourhood by intensity, divides the sorted pixels into groups depending on the intensity differences of adjacent pixels, and finally takes the median of the maximum group as the intensity of noisy pixel. This noise removal strategy is performed iteratively and takes full advantage of the previous denoising results. Experiments show that IGMF outperforms the existing state‐of‐the‐art filters in terms of visual perception, peak signal to noise ratio and structural similarity index at various noise densities.

Higher resolution, wider FOV and increasing frame rate of HMD are demanding more VR computing resources. Foveated rendering is a key solution to these challenges. This paper introduces a perceptual model optimized foveated rendering. Tessellation levels and culling areas are adaptively adjusted based on visual sensitivity. We improve rendering performance while satisfying visual perception.

To overcome the drawbacks of existing filters for salt and pepper noises, an adaptive probability filter is proposed. For an image, it detects salt and pepper noises based on the characteristic of minimum and maximum intensity values of the images, as well as the distribution of noise. If the noise‐free intensities in neighbourhood repeat with a certain probability, the noise‐free intensity with highest repeated frequency is used to remove noise based on the statistical significance; otherwise, the median of noise‐free pixels in neighbourhood is used to remove noise. Experiments show that the proposed method is capable of detecting noise more accurately and perform much better than the existing distinguished filters in terms of peak‐signal‐to‐noise ratio, image enhancement factor, and visual representation at all the noise densities.

The recently proposed robust principle component analysis (RPCA) has been successfully applied in background subtraction. However, low-rank decomposition makes sense on the condition that the foreground pixels (sparsity patterns) are uniformly located at the scene, which is not realistic in real-world applications. To overcome this limitation, we reconstruct the input video frames and aim to make the foreground pixels not only sparse in space but also sparse in time. Therefore, we propose a joint video frame set division and RPCA-based method for background subtraction. In addition, we use the motion as a priori knowledge which has not been considered in the current subspace-based methods. The proposed method consists of two phases. In the first phase, we propose a lower bound-based within-class maximum division method to divide the video frame set into several subsets. In this way, the successive frames are assigned to different subsets in which the foregrounds are located at the scene randomly. In the second phase, we augment each subset using the frames with a small quantity of motion. To evaluate the proposed method, the experiments are conducted on real-world and public datasets. The comparisons with the state-of-the-art background subtraction methods validate the superiority of our method.