Improved Region Energy Based Image Fusion Rule for Multi-focus Image Fusion

In this paper,we propose a novel improved region energy based image fusion rule.The original images are firstly decomposed by using the lifting scheme of wavelet transform into four sub-bands:LL,LH,HL,HH,by studying principles and characteristics of the wavelet subbands,and we put emphasis on the high frequency subbands.Thus HH,HL,LH sub-bands,which represent three direction of high frequency details,are weighted by different size of three direction Gaussian kernel,then the energy based image fusion rule is applied with a optional size of window,thus the activity level of high frequency subbands are obtained,followed by a local region matching degree in the corresponding direction and resolution,an activity level of low frequency subband is calculated,then perform consistency verification on the selected wavelet coefficients,by doing the inverse wavelet transform the fused image is obtained.The performance of the proposed novel image fusion scheme is conducted and compared with a few existing image fusion algorithm,the experimental results show that the proposed method is an effective multi-focus image fusion algorithm.

The automatic height extraction of buildings from shadow region

Height extraction for buildings is a fundamental step of 3D scene reconstruction in many virtual reality applications. In this paper, we propose an automatic method to extract the height of buildings in high resolution satellite imagery based on the length of shadow. Taking into account the limitation of traditional algorithms, we make use of the boundary information of a building to facilitate detecting and matching the shadow regions with higher accuracy. Then, we introduce a shadow-cast model to correct the shadow location in our system. The experimental result shows that when extracting the height of buildings from complex urban regions, our method has better accuracy.

Insect recognition based on integrated region matching and dual tree complex wavelet transform

To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.

Improving Retrieval Performance by Region Constraints and Relevance Feedback

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.

Video Structure Analysis

Video structure analysis is a basic requirement for most content-based video editing and processing systems. This paper presents a fast video structure analysis method based on image segmentation in each frame, with region matching between frames. The structure analysis decomposes the video into several moving objects, including information about their colors, positions, shapes, movements, and lifetimes. The method also supports user interactions to improve the results. The result shows that this method is fast and stable and can complete video analyzinq interactivelv.

A Novel Interpretation of Structural Dot Plots of Genomes Derived from the Analysis of Two Strains of Neisseria meningitidis

Neisseria meningitidis is the agent of invasive meningococcal disease, including cerebral meningitis and septicemia. Because the diseases caused by different clonal groups （sequence types） have their own epidemiological characteristics, it is important to understand the differences among the genomes of the N. meningitidis clonal groups. To this end, a novel interpretation of a structural dot plot of genomes was devised and applied; exact nu- cleotide matches between the genomes ofN. meningitidis serogroup A strain Z2491 and serogroup B strain MC58 were identified, leading to the specification of various structural regions. Known and putative virulence genes for each N. meningitidis strain were then classified into these regions. We found that virulence genes of MC58 tend more to the translocated regions （chromosomal segments in new sequence contexts） than do those of Z2491, notably tending towards the interface between one of the translocated regions and the collinear region. Within the col- linear region, virulence genes tend to occur within 16 kb of gaps in the exact matches. Verification of these tendencies using genes clustered in the cps locus was sufficiently supportive to suggest that these tendencies can be used to focus the search for and understanding of virulence genes and mechanisms of pathogenicity in these two organisms.