Yunbo Rao

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.

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.

Sparse codes fusion for context enhancement of night video surveillance

Fusion-based method for video enhancement has been playing a basic but significant role, which is also proved high-efficiency. Still, there are some open questions, such as lamp-off problem, over-enhanced moving objects and night shadow. To resolve the problems, a novel method—sparse codes fusion (SCF) is proposed. With plenty of samples from daytime videos and nighttime videos of the same scene, we learn and obtain a daytime dictionary and a nighttime dictionary using the proposed mutual coherence learning (MCL) algorithm. These two dictionaries are utilized for fusion and extracting context enhanced background. Moreover, we reconstruct the nighttime dictionary to get nighttime background that would be applied in motion extraction. Then the moving objects are added into the enhanced background. Extensive experimental results show a highly comprehensive description of video frames that leads to improvements over the state of the art on many usual public video datasets.

Anterior cruciate ligament reconstruction model based on anatomical position locating

In the reconstruction surgery of Anterior Cruciate Ligament (ACL), how to locate the anatomical position is a very hard point to clinician occupational therapists. In this paper, we propose an Anatomical Position Locating (APL) approach based on Expectation Maximization (EM) algorithm. Firstly, the proposed intersection set operation algorithm is proposed to compute the attachment region between the injured ACL and femur or tibia. Then, the anatomical position is located by the 3D points cloud with the Gaussian spatial distribution. The last, the attachment spatial distribution and the barycenter, which are also viewed as the candidates of the anatomical position by a proposed EM algorithm, is partition. Experimental results verify our assumption and demonstrate that the located anatomical position has great serviceability.

An Efficient ACL Segmentation Method

Soft-tissue segmentation has always been difficult point in the medical research of diagnosis of soft-tissue defects. Especially for Anterior Cruciate Ligament (ACL) rebuilding surgery, ACL segmentation from all soft-tissue inside knee joint, including Posterior Cruciate Ligament (PCL) and meniscus, is a very important task. In this paper, we propose a novel ACL segmentation method: Space Model Contrast Clustering-based (SMC-based) ACL Segmentation. Unlike the widely used processing method, such as segmentation by MRI gray values and Mimics segmentation drawing, the proposed method relies 3D model of knee joint to segment soft tissue by self-adaptive K-means clustering. Extensional experiments demonstrate that the proposed method can be capable of solving the problem of soft-tissue segmentation well and has achieved higher ACL segmentation efficiency.

Optimization algorithm based on texture feature and frame correlation in HEVC

Newly proposed video standard High Efficiency Video Coding (HEVC) achieves higher compression performance than previous ones. In this paper, we propose a novel algorithm for intra prediction, which scales the complexity of pictures’ texture to perform different levels of simplification on Most Probable Mode(MPM) selection. And the proposed algorithm for inter prediction initializes current Coding Unit(CU) depth information with that information of temporally adjacent frame’s co-located CU. These two proposed algorithms, utilizing the texture feature and correlation of adjacent frames, reduce the computational complexity to improve the efficiency of encoder. The proposed algorithms decrease more than 30 % of encoding time with nearly negligible increment in bit-rate, especially work well when encoding sequences with high definition.

Detail-enhanced and brightness-adjusted exposure image fusion

Abstract. We present a multiexposure image fusion method that can enhance details, yet effectively improve brightness in the final result. In addition, a valid weight measurement is developed to remove motion objects in dynamic scenes. During the fusion process, each source image is first decomposed into one low-pass sub-band and a series of high-pass directional sub-bands using the nonsubsampled contourlet transform. Then the blended sub-bands are constructed by weight maps of the source images. To preserve the details of source images and adjust brightness of final image, gain control maps are used for each fused sub-band. Experimental results demonstrate that the proposed method significantly outperforms the traditional methods in terms of both visual inspection and objective evaluation, especially in cases which the regions of interest are in dark areas.

Fractal-based 3d tree modeling

In this paper, we propose an approach for generating three dimension (3D) models of trees base on fractal idea that has the benefit of offering automatically and interactively modeling. The approach can generate 3D geometry tree using botanical knowledge and the fractals, can automatically generate photorealistic tree in real-time, also provides the capability for the user to control shape of the tree in a parameterized way as compared with rule-based tree modeling systems. The approach is very useful in practices and generates visually convincing results.