Pei Jihong

Key frame extraction based on multi-scale phase-based local features

Key-frames are representative frames in a shot. Key frame extraction is one of the basic procedures relating to video retrieval and indexing. In view of the surveillance video characteristic and the user attention focus, this paper proposed a key frame extraction method based on multi-scale phase-based local features. Prior to key frame extraction, the video should be segmented into shots. Then, find the interest points in the head of the moving target extracted by adaptive background mixture Gaussian models, mark the candidate key frame which has a certain number of interest points matching with the given target model. Lastly, for each shot, extract key frame which has the best similar match. Experimental results demonstrate that the proposed method is feasible and effective.

Elastic image registration using attractive and repulsive particle swarm optimization

Elastic image registration plays an important role in medical image registration. For elastic image registration based on landmarks of sub-images, optimization algorithm is applied to extract landmarks. But local maxima of similarity measure make optimization difficult to convergence to global maximum. The registration error will lead to location error of landmarks and lead to unexpected elastic transformation results. In this paper, an elastic image registration method using attractive and repulsive particle swarm optimization (ARPSO) is proposed. For each subimage, rigid registration is done using ARPSO. In attractive phase, particles converge to promise regions in the search space. In repulsive phase, particles are repelled each other along opposition directions and new particles are created, which might avoid premature greatly. Next, thin plate spline transformation is used for the elastic interpolation between landmarks. Experiments show that our method does well in the elastic image registration experiments.

Signal processing in the context of big data

The rapid developments of wideband signal, high-dimensional signal, high resolution image, and multi-sensor network have made the growth rate of data acquisition amount higher than the growth rate of data storage amount and the growth rate of signal processing speed. Signal processing steps into the big data era. This paper points out the key issues of signal processing with big data. Considering the diversity and complexity of the big data produced by multi-sensor networks, it is necessary to conduct information fusion. The main information fusion models are described, and the trends of information fusion technology are analyzed. Intelligent sensor network technology can reduce the capacity of signal processing and communication, and efficiently extract valuable information from big data. The basic architecture of a smart sensor is given, and calculation methods for intelligent sensor networks are described. From viewpoint of high-speed and real-time signal processing requirements of signals with big data, we also introduce developments of high-speed digital signal processing chips and high-performance hardware platforms. The vision of core technology in high-speed signal processing is presented.

Head location based on fuzzy weighted projection histogram in infrared thermal sequences

Human detection is important in visual surveillance systems. Especially, counting the number of moving objects and locating the head of an object is a difficult problem in human detection. In this paper, a novel method is used to count and locate moving objects in thermal images, which uses thermal and position information to compute the fuzzy weighted projection histogram. The peaks of the fuzzy weighted projection histogram could be sharper and more separated. In the meantime, a potential function clustering method is used to count the number and locate the position of peaks in the histogram, which is the number and the head position of moving objects. Experiments show that our method is effective and feasible to count and locate moving objects in thermal images.