Leiting Chen

Image-based fusion for video enhancement of night-time surveillance

In this paper, a novel image-based fusion video enhancement algorithm is proposed for night-time video surveillance applications by a combination of illumination fusion and based on moving objects fusion. The proposed algorithm fuses video frames from high quality day-time and night-time background with low quality night-time videos. For improving the perception quality of the moving objects, based on moving objects of region fusion method is proposed. Experimental results show the effectiveness of the proposed algorithm.

Illumination-based nighttime video contrast enhancement using genetic algorithm

Contrast enhancement is crucial to the domain of security and surveillance where limitations in dynamic range and lack of lighting sources prevent fine details of the scene from being captured. Here, we propose a method of nighttime video contrast enhancement based on genetic algorithms. Conversion from RGB to HSI and illumination component extraction were done firstly. Illumination-based enhancement which combines chromosome, corresponding operators and genetic algorithm was then applied to enhance the contrast and details of the video according to an objective fitness criterion. Image reconstruction followed previous procedures finally. Comparison of our proposed method with other automatic enhancement techniques such as histogram equalization shows that our method produces natural looking images/videos, especially when the dynamic range of the input image is high. Results obtained, both in terms of subjective and objective evaluation, show the superiority of the proposed method.

The M -relative reduct problem

Since there may exist many relative reducts for a decision table, some attributes that are very important from the viewpoint of human experts may fail to be included in relative reduct(s) computed by certain reduction algorithms. In this paper we present the concepts of M-relative reduct and core where M is a user specified attribute set to deal with this problem. M-relative reducts and cores can be obtained using M-discernibility matrices and functions. Their relationships with traditional definitions of relative reduct and core are closely investigated

Knowledge reduction in inconsistent decision tables

In this paper, we introduce a new type of reducts called the A-Fuzzy-Reduct, where the fuzzy similarity relation is constructed by means of cosine-distances of decision vectors and the parameter A is used to tune the similarity precision level. The A-Fuzzy-Reduct can eliminate harsh requirements of the distribution reduct, and it is more flexible than the maximum distribution reduct, the traditional reduct, and the generalized decision reduct. Furthermore, we prove that the distribution reduct, the maximum distribution reduct, and the generalized decision reduct can be converted into the traditional reduct. Thus in practice the implementations of knowledge reductions for the three types of reducts can be unified into efficient heuristic algorithms for the traditional reduct. We illustrate concepts and methods proposed in this paper by an example.

Object-Aware Dense Semantic Correspondence

This work aims to build pixel-to-pixel correspondences between images from the same visual class but with different geometries and visual similarities. This task is particularly challenging because (i) their visual content is similar only on the high-level structure, and (ii) background clutters keep bringing in noises. To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classi?er for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint. Specifically, an Object-aware Hierarchical Graph (OHG) model is constructed to regulate matching consistency from one coarse grid cell containing whole object(s), to fine grid cells covering smaller semantic elements, and finally to every pixel. A guidance layer is introduced as the semantic constraint on local structure matching. In addition, we propose to learn the important high-level structure for each grid cell in an objectness-driven way as an alternative to handcrafted descriptors in de?ning a better visual similarity. The proposed method has been extensively evaluated on various challenging benchmarks and real-world images. The results show that our method signi?cantly outperforms the state-of-the-arts in terms of semantic flow accuracy.

Real-Time incompressible fluid simulation on the GPU

We present a parallel framework for simulating incompressible fluids with predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH) on the GPU in real time. To this end, we propose an efficient GPU streaming pipeline to map the entire computational task onto the GPU, fully exploiting the massive computational power of state-of-the-art GPUs. In PCISPH-based simulations, neighbor search is the major performance obstacle because this process is performed several times at each time step. To eliminate this bottleneck, an efficient parallel sorting method for this time-consuming step is introduced. Moreover, we discuss several optimization techniques including using fast on-chip shared memory to avoid global memory bandwidth limitations and thus further improve performance on modern GPU hardware. With our framework, the realism of real-time fluid simulation is significantly improved since our method enforces incompressibility constraint which is typically ignored due to efficiency reason in previous GPU-based SPH methods. The performance results illustrate that our approach can efficiently simulate realistic incompressible fluid in real time and results in a speed-up factor of up to 23 on a high-end NVIDIA GPU in comparison to single-threaded CPU-based implementation.

Real-time control of individual agents for crowd simulation

This paper presents a novel approach for individual agent’s motion simulation in real-time virtual environments. In our model, we focus on addressing two problems: 1) the control model for local motions. We propose to represent a combination of psychological and geometrical rules with a social and physical forces model so that it can avoid individual agent’s local collision. 2) Global path planning algorithm with moving obstacle. We propose a more efficient algorithm by extending the indicative route method. Experimental results show that the proposed approach can be tuned to simulate different types of crowd behaviors under a variety of conditions, and can naturally exhibit emergent phenomena that have been observed in real crowds.

Global motion estimation–based method for nighttime video enhancement

In order to efficiently enhance the dark nighttime videos, the high-quality daytime information of the same scene is often introduced to help the enhancement. However, due to camera motion, the introduced daytime may not have exactly the same scene of the nighttime videos. Thus, the final fused moving objects may not produce reasonable results. In this paper, we make the following two contributions: 1. we propose a global motion estimation-based scheme to address the problem of scene differences between daytime and nighttime videos. 2. Based on this, we further propose an improved framework for nighttime video enhancement which can efficiently recover the unreasonable enhancement results due to scene differences. Experimental results show the effectiveness of the proposed algorithm.

Predicting the evolution of complex networks via similarity dynamics

Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial–temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links’ likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.

Diffusion Kurtosis Imaging Based on Adaptive Spherical Integral

Diffusion kurtosis imaging (DKI) is a recent approach in medical engineering that has potential value for both neurological diseases and basic neuroscience research. In this letter, we develop a robust method based on adaptive spherical integral that can compute kurtosis based quantities more precisely and efficiently. Our method integrates spherical trigonometry with a recursive computational scheme to make numerical estimations in kurtosis imaging convergent. Our algorithm improves the efficiency of computing integral invariants based on reconstructed diffusion kurtosis tensors and makes DKI better prepared for further clinical applications.