Adaptive video coding using mixed-domain filter banks having optimal-shaped subbands
This contribution presents a mixed-domain filter bank. This filter bank can realize arbitrary (and thereby optimal) spectral decomposition and therefore is very suitable for the efficient subband coding of video signals. A coding scheme using the proposed filter bank is considered, which takes advantage of the spectral distribution of the signal and is especially selective to motion.<<ETX>>

title: The design of nonuniform-band maximally decimated filter banks
abstract: A design method for nonuniform-band maximally decimated filter banks is presented. It is based on the quadrature mirror filter (QMF) design method and allows the direct frequency domain design of two-band filter banks having arbitrary rational decimation ratios. The numerical design of nonuniform-band filter banks is achieved using a simple structure in which elementary modulators are used in the highpass channel to obtain almost-perfect reconstruction.<<ETX>>

title: Intelligent Route Finding: Combining Knowledge and Cases and an Efficient Search Algorithm
abstract: This paper presents an integrated approach of using knowledge about a road network, past cases, and an efficient search algorithm for the purpose of route finding. The technique reduces the time and space required in computation and also produces human-oriented and personalized solutions. Using this approach, a system was implemented for route finding in Singapore.

title: Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images
abstract: Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: ''ROI generation step'' and ''ROI classification step''. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the ''classification checkpoints'' which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.

title: An Efficient and Accurate Method for 3D-Point Reconstruction from Multiple Views
abstract: In this paper we consider the problem of finding the position of a point in space given its projections in multiple images taken by cameras with known calibration and pose. Ideally the 3D point can be obtained as the intersection of multiple known rays in space. However, with noise the rays do not meet at a single point generally. Therefore, it is necessary to find a best point of intersection. In this paper we propose a modification of the method (Ma et al., 2001. Journal of Communications in Information and Systems, (1):51–73) based on the multiple-view epipolar constraints. The solution is simple in concept and straightforward to implement. It includes generally two steps: first, image points are corrected through approximating the error model to the first order, and then the 3D point can be reconstructed from the corrected image points using any generic triangulation method. Experiments are conducted both on simulated data and on real data to test the proposed method against previous methods. It is shown that results obtained with the proposed method are consistently more accurate than those of other linear methods. When the measurement error of image points is relatively small, its results are comparable to those of maximum likelihood estimation using Newton-type optimizers; and when processing image-point correspondences cross a small number of views, the proposed method is by far more efficient than the Newton-type optimizers.

title: Intelligent air travel and tourist information systems

title: Joint estimation of image and coil sensitivities in parallel MRI
abstract: Parallel magnetic resonance imaging (MRI) using multichannel receiver coils has emerged as an effective tool to reduce imaging time in various dynamic imaging applications. However, there are still a number of image reconstruction issues that have not been fully addressed, thereby limiting the level of speed enhancement achievable with the technology. This paper considers the inaccuracy of coil sensitivities in conventional reconstruction methods such as SENSE, and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative algorithm. Experimental results demonstrate the effectiveness of the proposed method especially when large acceleration factors are used