Digital refocusing has a tradeoff between complexity and quality when using sparsely sampled light fields for low-storage applications. In this paper, we propose a fast physically correct refocusing algorithm to address this issue in a twofold way. First, view interpolation is adopted to provide photorealistic quality at infocus-defocus hybrid boundaries. Regarding its conventional high complexity, we devised a fast line-scan method specifically for refocusing, and its 1D kernel can be 30× faster than the benchmark View Synthesis Reference Software (VSRS)-1D-Fast. Second, we propose a block-based multi-rate processing flow for accelerating purely infocused or defocused regions, and a further 3- 34× speedup can be achieved for high-resolution images. All candidate blocks of variable sizes can interpolate different numbers of rendered views and perform refocusing in different subsampled layers. To avoid visible aliasing and block artifacts, we determine these parameters and the simulated aperture filter through a localized filter response analysis using defocus blur statistics. The final quadtree block partitions are then optimized in terms of computation time. Extensive experimental results are provided to show superior refocusing quality and fast computation speed. In particular, the run time is comparable with the conventional single-image blurring, which causes serious boundary artifacts.

View-interpolation-based refocusing achieves realistic quality for sparse light fields but requires lots of computation. In this paper, we aim to reduce the computation load while maintaining the superior refocusing quality. The idea is to interpolate only few views for infocused regions and to perform refocusing on downsampled pixels for defocused area. This is achieved b y a proposed block-based refocusing algorithm which consists of multi-level refocusing mode decision and timing-based bloc k merging. For each variable-size block, the former chooses the fastest downsampling mode given that the distortion is negligible based on a localized filter response analysis. Then, the latter determines the fastest quadtree block partition by minimizing the timing cost which is accurately estimated for each block. Experimental results show that a 10x speedup on average is achieved for realistic refocusing. Also, the computation time becomes comparable to the conventional fast depth-dependent blurring which has serious boundary artifacts.

With the popularity of location-based social networks (LBSNs), users would like to share their check-ins with their friends for more social interactions. These check-in records reflect not only when and where they are, but also what they are doing. If we can capture the relations of the location, time, and activity factors in LBSNs, the location-based social platforms can provide more personalized location-based services to users. In this paper, we aim to infer individual activity and mobility based on their check-in records in LBSNs. For these two inference problems, we analyze check-in records, and utilize Bayesian network to represent the relations among location, time, and activity factors of check-in records. Based on the proposed network model, the two inference problems can be simplified to two modules, the activity-time and the location-activity relation. For the activity-time relation, we propose Order-1 Activity Transition Model to capture the activity-time relations of check-in records. Moreover, for the location-activity relation, we exploit the Gaussian mixture model to capture individual mobility features in different activities. To evaluate the proposed network model for the two inference problems, we conduct extensive experiments on two real datasets, and the experimental results show that our proposed Bayesian-based approach has higher performance than the state-of-the-art approaches for activity and mobility inference in LBSNs.

Digital refocusing for sparsely sampled light fields results in aliasing effect. For realistic quality, previous works performed anti-aliasing by applying time-consuming view interpolation for a heuristic number of novel views. In this paper, we study this problem by first performing a spectral analysis to give an analytical rule for the novel view number, which saves 34% of views compared to the intuitional choice. Then we propose a fast refocusing algorithm using a view interpolation method which is about 30x faster than VSRS-1D-Fast. Experimental results show the effectiveness of our approach in terms of speed and quality. For showing realistic refocusing, a light-field wafer-level-optics array is implemented, and its refocused images are compared to pictures captured by a real camera.

Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of cameras and optical devices has made it economically feasible to deploy front‐mounted intelligent systems for visual‐based event detection. Prior to vehicle event detection, detecting vehicles robustly in real time is challenging, especially conducting detection process in images captured by a dynamic camera. Therefore in this study, a robust vehicle detector is developed. The proposed contribution is three‐fold. Road modelling is first proposed to confine detection area for maintaining low computation complexity and reducing false alarms as well. Haar‐like features and eigencolours are then employed for the vehicle detector. To tackle the occlusion problem, chamfer distance is used to estimate the probability of each individual vehicle. Consequently, to find the bounding box of vehicle candidate, the authors take the configuration of a normalised chamfer distance map that corresponds to the maximum score as the target using exponential entropy. Experiments on an extensive dataset show that the proposed system can effectively detect vehicles under different lighting and traffic conditions, and thus demonstrates its feasibility in real‐world environments.