Conventional image retrieval techniques for Structure-from-Motion (SfM) are limited in their ability to effectively distinguish symmetric or repetitive textured patterns and cannot guarantee an accurate generation of pairwise matches without costly redundancy. In this paper, we formulate the image retrieval task as a node binary classification problem with graph data: if a candidate node is marked as positive, it is believed to share the same scene with the query image. The key idea of our approach is that the local context in the feature space around a query image contains abundant information about the matchable relation between the image and its neighbours. By constructing a subgraph surrounding the query image as input data, we adopt a learnable Graph Convolutional Network (GCN) to determine whether nodes in the subgraph have overlapping regions with the query photograph. Experiments demonstrate that our method performs remarkably well on a challenging dataset of highly ambiguous and duplicated scenes. Furthermore, compared with state-of-the-art matchable retrieval methods , the proposed approach significantly reduces unnecessary attempted matches without sacrificing the accuracy and completeness of reconstruction.

The goal of image denoising is to recover a clean image from noisy input(s). For single image denoising, utilising similarities (or priors) within and across an image dataset helps recover clean images. As the noise level increases, using multiple frames become feasible, which is defined as burst denoising. In this study, the authors propose a deep residual model with squeeze‐and‐excitation (SE) modules for the burst denoising. Unlike previous methods, the authors' model does not need an explicit aligning procedure, which is light‐weighted and fast. The network contains a noise estimation convolutional neural network, which makes it capable of blind denoising. Besides, by inverting the image processing pipeline and simulating real noise in bursts, their model can suppress real noise blindly. Since denoising performance is closely related to the noise level, frame displacement, and the number of frames (burst length), intensive experiments including ablation study are performed. Quantitative results show that the proposed method performs significantly better than previous state‐of‐the‐art methods V‐BM4D and KPN in removing Gaussian noise. Qualitative results show that the proposed method is also effective in removing real noise using bursts and the SE module is key to reduce blur in results.

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we propose a parallax-tolerant image stitching method based on robust elastic warping, which could achieve accurate alignment and efficient processing simultaneously. Given a group of point matches between images, an analytical warping function is constructed to eliminate the parallax errors. Then, the input images are warped according to the computed deformations over the meshed image plane. The seamless panorama is composed by directly reprojecting the warped images. As an important complement to the proposed method, a Bayesian model of feature refinement is proposed to adaptively remove the incorrect local matches. This ensures a more robust alignment than existing approaches. Moreover, our warp is highly compatible with different transformation types. A flexible strategy of combining it with the global similarity transformation is provided as an example. The performance of the proposed approach is demonstrated using several challenging cases.

Image demosaicing and denoising are import steps of image signal processing. Sequential executions of demosaicing and denoising have essential drawbacks that they degrade the results of each other. Joint demosaicing and denoising overcomes the difficulties by solving the two problems in one model. This paper introduces a unified object function with hidden priors and a variant of ADMM to recover a full-resolution color image with a noisy Bayer input. Experimental results demonstrate that our method performs better than state-of-the-art methods in both PSNR comparison and human vision. In addition, our method is much more robust to variations of noise level.

Compared with other large-scale warfare simulation systems, wargame, as a traditional type of warfare simulations, has the advantages of low cost, convenience, practicability, etc. In order to realize Human-Computer Confrontation, Computer Generated Forces (CGF) technology has been added to wargame, which would enhance wargame's training and decision support ability. Based on computerization of “Future: Korea War” which is a strategic-level wargame, this paper presents a framework of wargame CGF system based on multi-agent and defines its Atomic Action Library. The framework and the Atomic Action Library provide the basic platform for farther research on the intelligent behaviors of each agent in wargame CGF system.

This paper presents a video summarization method to visualize the video object trajectories across multiple cameras in a static image for monitoring the movements of suspicious people in a building. First, we have designed an object association algorithm across multiple stationary cameras, which can be used to build the object trajectories in the building with the assistants of the predefined locations of the cameras. The object associations are based on the features of human body, such as the clothing colour, stature, shoulder breadth and so on. These features are calculated from the anthropometric dimensions of the human body from calibrated monocular video sequences. Finally, the object trajectories are illustrated in a perspective picture that overviews all floors of the building, wihich is called storyboard. Some diagrammatic elements such as arrows and texts are used to annotate the object movements. Thicker arrows represent the more rapid movements and narrower arrows represent the slower movements. The visualization results can assist the monitors to real-time observe the people's movements and easily understand the people's activities in a building.