作者:
Lucey, SimonLucey, PatrickAdvanced Multimedia Processing Laboratory
Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Speech
Audio Image and Video Research Laboratory Queensland University of Technology GPO Box 2424 Brisbane4001 Australia
Motivated by the success of free-parts based representations in face recognition [1] we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent auto...
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In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in X-ray image sequences. Different with traditional DSA image registration processes, in our ...
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While feature selection is very difficult for high dimensional, unstructured data such as face image, it may be much easier to do if the data can be faithfully transformed into lower dimensional space. In this paper, ...
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Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual ...
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Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual speech recognition. A major problem with canonical area-based approaches in automatic visual speech recognition is the dependence these approaches have on locating and tracking the speaker’s region of interest (ROI) correctly. By employing a free-parts representation,we assume that the position/structure of patches within the mouth image can be relaxed so they can "freely" move to varying extents, hence reducing the influence of the front-end effect. In this paper, we show that by using a free-parts representation we gain some robustness against the problem of ROI localisation and tracking compared to current area-based feature extraction techniques such as the discrete cosine transform (DCT). Also in this paper, we expose the importance of representation for the task of visual speech recognition highlighted by the poor results current representations yield.
As a new unsupervised learning technique, manifold learning has captured the attention of many researchers in the field of machine learning and cognitive sciences. The major algorithms include Isometric mapping (ISOMA...
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As a new unsupervised learning technique, manifold learning has captured the attention of many researchers in the field of machine learning and cognitive sciences. The major algorithms include Isometric mapping (ISOMAP) and Locally Linear Embedding (LLE). The approaches can be used for discovering the intrinsic dimensions of nonlinear high-dimensional data effectively and aim researchers to analyze the data better. How to quantitatively analyze the relationship between the intrinsic dimensions and the observation space, however, has fewer reports. And thus further works in manifold learning may have suffered some difficulties. The paper focuses on two kinds of manifold learning algorithms (ISOMAP, LLE), and discusses magnification factors and principal spread directions from the observation space to the intrinsic low-dimensional space. Also the corresponding algorithm is proposed. Experiments show the effectiveness and advantages of the research.
In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in X-ray image sequences. Different with traditional DSA image registration processes, in our ...
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In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in X-ray image sequences. Different with traditional DSA image registration processes, in our proposed image registration method, the control points are selected from the vessel centerlines using multiscale Gabor filters, and mutual information (MI) is then taken as the similarity criterion to find the correspondences. Experimental results demonstrate our algorithm efficiently yields satisfying registration result for DSA images.
This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses....
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This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.
Panchromatic data of pixel resolution 5.8 m obtained from IRS-IC and IRS-ID satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a com...
Panchromatic data of pixel resolution 5.8 m obtained from IRS-IC and IRS-ID satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.
The key procedure of exploratory projection pursuit is to optimize a criterion function, which is called the projection pursuit index. The cook family index estimated by the wavelet kernel function is given in this pa...
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The residue number system (RNS) has computational advantages in addition and multiplication compared with weighted number systems, such as the binary number system (BNS), since operations on residue digits are perform...
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The residue number system (RNS) has computational advantages in addition and multiplication compared with weighted number systems, such as the binary number system (BNS), since operations on residue digits are performed independently and these processes can be performed in parallel. Thus they are widely used in digital signal processing etc. Since residue to binary conversion is critical and difficult for the practicality of RNS, in this paper, a novel residue to binary (R/B) conversion algorithm for the restricted moduli set (2/sup n/ -1, 2/sup n/, 2n+1), based on exploring the periodicity of modulo (2/sup n/ /spl plusmn/ 1) operations is presented. A new 2n-bit adder based R/B converter is also proposed. The performance comparison results demonstrate that the new converter is faster and requires less area compared with the others reported in the previous literature.
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