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We present a new post processing method of simulating depth of field based on accurate calculations of circles of confusion. Compared to previous work, our method derives actual scene depth information directly from the existing depth buffer, requires no specialized rendering passes, and allows easy integration into existing rendering applications. Our implementation uses an adaptive, two-pass filter, producing a high quality depth of field effect that can be executed entirely on the GPU, taking advantage of the parallelism of modern graphics cards and permitting real time performance when applied to large numbers of pixels.

Abstract

Although particle swarm optimisation (PSO) algorithm is an effective tool to solve the real‐number optimisation problem, it cannot be directly adopted to the discrete optimisation. Many discrete PSO versions focused on the different discrete strategies on particle position and speed update equation, but these attempts decreased the performance of the PSO on discrete problems (compared with other intelligent optimisation algorithms). In this study, binary inheritance learning PSO (BILPSO) is proposed and is used to solve thinned antenna array synthesis problems, such as the pattern synthesis of 100‐element symmetrical thinned linear array and 20 × 10 symmetrical thinned planar array. The optimisation results show the BILPSO is superior to other discrete PSO versions and other discrete intelligent optimisation algorithms.

Simulation of physically realistic complex dust behavior is very useful in training, education, art, advertising, and entertainment. There are no published models for real-time simulation of dust behavior generated by a traveling vehicle. In this paper, we use particle systems, computational fluid dynamics, and behavioral simulation techniques to simulate dust behavior in real time. First, we analyze the forces and factors that affect dust generation and the behavior after dust particles are generated. Then, we construct physically-based empirical models to generate dust particles and control the behavior accordingly. We further simplify the numerical calculations by dividing dust behavior into three stages, and establishing simplified particle system models for each stage. We employ motion blur, particle blending, texture mapping, and other computer graphics techniques to achieve the final results. Our contributions include constructing physically-based empirical models to generate dust behavior and achieving simulation of the behavior in real time.

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In this paper, we propose a low-rank representation method that incorporates graph regularization for robust subspace clustering. We make the assumption that high-dimensional data can be approximated as the union of low-dimensional subspaces of unknown dimension. The proposed method extends the low-rank representation algorithm by incorporating graph regularization with a discriminative dictionary. Existing low-rank representation methods for subspace clustering use noisy data as the dictionary. The proposed technique, however, takes advantage of the discriminative dictionary to seek the lowest-rank representation by virtue of matrix recovery and completion techniques. Moreover, the discriminative dictionary is further used to construct a graph Laplacian to separate the low-rank representation of high-dimensional data. The proposed algorithm can recover the low-dimensional subspace structure from high-dimensional observations (which are often corrupted by gross errors). Simultaneously, the samples are clustered into their corresponding underlying subspaces. Extensive experimental results on benchmark databases demonstrate Communicated by V. Loia. B Wu He the efficiency and effectiveness of the proposed algorithm for subspace clustering.