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International Symposium on Intelligent multimedia, Video and Speech processing

作者： Xiuying Wang D.D. Feng School of Computer Science and Technology Heilongjiang University Harbin China Biomedical and Multimedia Information Technology (BMIT) Group School of Information Technologies University of Sydney Australia Center for Multimedia Signal Processing (CMSP) Department of Electronic & Information Engineering Hong Kong Polytechnic University Hong Kong China

Wavelets have been studied and applied in the multimedia and video processing community, including some areas in medical imaging, however, wavelet-based medical image registration has not been explored properly. In this paper, we propose an hierarchical registration method based on wavelet decomposition. Firstly, based on the wavelets, the images are decomposed into subbands which compose the registration pyramid; then, in each hierarchy, the affine registration based on mutual information is performed and the results of the current registration are used as the initial guess for the next hierarchy; finally, to further improve the registration performance, the local elastic registration is carried out. The proposed algorithm has been validated by experiments on clinical tomographic images and our experimental results demonstrate the the proposed method is of efficiency and good accuracy.

关键词： Humans Brain Biomedical imaging Image registration Magnetic resonance imaging X-ray imaging Computed tomography Ultrasonic imaging Positron emission tomography Medical services

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Texture segmentation using local morphological multifractal exponents

Texture segmentation using local morphological multifractal ...

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International Symposium on Intelligent multimedia, Video and Speech processing

作者： Xia Yong D. Feng Zhao Rongchun School of Computer Northeastern Polytechnical University Xi'an China School of Information Technology University of Sydney Sydney Australia Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

This paper deals with the problem of segmenting various textures. For this purpose, we have applied mathematical morphology for the multifractal analysis of images. The digital gray level image is treated as a 3D surface whose multifractal measures are calculated by performing dilations on this surface. Plotting the acquired measures against the size of the structuring element, the local morphological multifractal exponents can be estimated, based on which the unsupervised fuzzy C-means clustering method is used to segment a texture image into the desired number of classes. Randomly choosing 12 natural textures from the Brodatz album, 66 mosaics of 2 textures and 495 mosaics of 4 textures are used to test the new segmentation approach and other two techniques, where the multifractal features are extracted by the box-counting based methods. The comparison results demonstrate that the proposed approach can differentiate texture images more effectively and provide more robust segmentation results.

关键词： Fractals Image segmentation Surface morphology Surface treatment Image analysis Performance evaluation Size measurement Clustering methods Testing Feature extraction

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Machine learning techniques for ontology-based leaf classification

Machine learning techniques for ontology-based leaf classifi...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Hong Fu Zheru Chi Dagan Feng Jiatao Song Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong China School of Information Technologies University of Sydney NSW Australia Department of Information and Communication Engineering University of Zhejiang Hangzhou China

ISBN: (纸本)0780386531

Leaf classification, indexing as well as retrieval is an important part of a computerized plant identification system. In this paper, an integrated approach for an ontology-based leaf classification system is proposed, wherein machine learning techniques play a crucial role for the automatization of the system. For the leaf contour classification, a scaled CCD code system is proposed to categorize the basic shape and margin type of a leaf by using the similar taxonomy principle adopted by the botanists. Then a trained neural network is employed to recognize the detailed tooth patterns. The measurement on an unlobed leaf is also conducted automatically according to the method used in botany. For the leaf vein recognition, the vein texture is extracted by employing an efficient combined thresholding and neural network approach so as to obtain more vein details of a leaf. Compared with the past studies, the proposed method integrates low-level features of an image and the specific knowledge in the domain (ontology) of botany, and therefore provides a more practical system for users to comprehend and handle. Primary experiments have shown promising results and proven the feasibility of the proposed system.

关键词： Machine learning Ontologies Veins Neural networks Indexing Charge coupled devices Shape Taxonomy Pattern recognition Teeth

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Editorial

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International Journal of Image and Graphics 2003年第1期3卷 1-2页

作者： Feng, David Dagan Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong Group School of Information Technologies Assoc. Dean Faculty of Science University of Sydney NSW2006 Australia

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Content-based image retrieval with relevance feedback using adaptive processing of tree-structure image representation

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International Journal of Image and Graphics 2003年第1期3卷 119-143页

作者： Wang, Zhiyong Chi, Zheru Feng, Dagan Tsoi, Ah H. Chung Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong School of Information Technologies University of Sydney NSW2006 Australia University of Wollongong NSW2522 Australia

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested. © 2003 World Scientific Publishing Company.

关键词： Image representation

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A fast two-stage OMP algorithm for coding stereo image residuals

A fast two-stage OMP algorithm for coding stereo image resid...

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IEEE International Conference on Image processing

作者： Wan-Fung Cheung Yuk-Hee Chan Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780377508

Over-complete signal decomposition has been shown to be an efficient technique for coding disparity compensated residual images in stereo image pairs. This paper presents a residual image coding scheme based on a recently proposed MP-based coding scheme called SOSU. The proposed scheme decomposes an orthogonal matching pursuit process into two stages. In different stages, dictionaries of different nature are used. Simulation results show that the proposed scheme can significantly reduce the computation effort while maintaining a similar rate-distortion performance as compared with SOSU.

关键词： Image coding Matching pursuit algorithms Dictionaries signal processing algorithms Computational modeling Discrete cosine transforms signal resolution High-resolution imaging Image resolution Hardware

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Automatic segmentation of Chinese chunks using a neural network

Automatic segmentation of Chinese chunks using a neural netw...

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International Conference on Neural Networks and signal processing

作者： Rongbo Wang Zheru Chi Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780377028

Chunk analysis method is a new idea proposed in recent years in developing new machine translation methods. Chinese chunk analysis plays a very important role in machine translation from Chinese to foreign languages. The automatic segmentation of Chinese chunks is the basis of Chinese chunk analysis. It is important for processing real text and corpus construction. This paper presents a method for automatic segmentation of Chinese chunks based on 3-layers neural networks. The corpus we used has been processed with Chinese word segmentation and phrase identification and tagging. In the neural networks model, the input data is the segmentation situation of every character and its combinations with neighbor characters in a Chinese sentence. The output is the segmentation results of every character in a Chinese sentence. The preliminary results show that the method is feasible and effective.

关键词： Neural networks Biological neural networks Natural languages Information analysis signal processing signal analysis Tagging Springs Hardware Statistical analysis

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An effieient algorithm for fractal image coding using kick-out and zero contrast conditions

An effieient algorithm for fractal image coding using kick-o...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Cheung-Ming Lai Kin-Man Lam Wan-Chi Siu Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780377613

In this paper, we propose a fast algorithm for fractal image coding based on a single kick-out condition and zero contrast prediction. The single kick-out condition can eliminate lots of unmatched domain blocks in the early encoding phase. An efficient method based on zero contrast prediction is also proposed, which can determine whether the contrast factor for a domain block is zero or not and compute the corresponding difference between the range block and the transformed domain block efficiently and exactly. The proposed algorithm can achieve the same reconstructed image quality as the exhaustive search, and can greatly reduce the required computational complexity. In addition, this algorithm does not need any preprocessing steps and additional memory for its implementation, and can combine with other fast fractal algorithms to further improve the speed. Experimental results show that the runtime is reduced by about 50% when compared to the exhaustive search method. The runtime can be reduced by about 75% when our algorithm is combined with the DCT inner product algorithm.

关键词： Fractals Image coding signal processing algorithms Image reconstruction Discrete cosine transforms Image quality Partitioning algorithms Runtime Computational complexity Search methods

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A two-stage approach for leaf vein extraction

A two-stage approach for leaf vein extraction

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International Conference on Neural Networks and signal processing

作者： Hong Fu Zheni Chi Center for Multimedia Signal Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780377028

Living plant recognition is a promising but challenging task in the fields of pattern recognition and computer vision. As an inherent trait, the leaf vein definitely contains the important information for plant species recognition despite of its complex modality. In this paper, an efficient two-stage approach is presented for leaf vein extraction. At the first stage, a preliminary segmentation based on the intensity histogram of the leaf image is carried out to estimate the rough regions of vein pixels. This is followed at the second stage by a fine checking using a trained artificial neural network (ANN) classifier. Ten features distilled from a window centered at the pixel are used as the input to train the ANN classifier. Compared with conventional edge detection methods, experimental results show that the proposed method is capable of extracting more precise venation modality of the leaf for the subsequent leaf recognition.

关键词： Veins Image edge detection Image segmentation Pixel Artificial neural networks Histograms Data mining Neural networks Laplace equations Hydrogen

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Computer-aided lesion detection for brain pet images

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IFAC Proceedings Volumes 2003年第15期36卷 163-167页

作者： Zhe Chen David Dagan Feng Weidong Cai Biomedical & Multimedia Information Technology (BMIT) Group School of Information technologies The University of Sydney Center for Multimedia Signal Processing (CMSP) Department of Electronic & Information Engineering Hong Kong Polytechnic University

We proposed a new method for the automatic detection of lesions in brain PET images based on the asymmetry across segments of the segmented PET data. Per-pixel asymmetry feature detection is experimentally compared with our per-segment approach. The proposed per-segment method is shown to produce fewer false positives in the brain PET data examples presented. Also, the algorithm is shown to provide accurate demarcation of the tumour in each plane of PET data. Therefore. this method offers potential advantages in region-of-interest (ROI) extraction for kinetic modelling and quantitative analysis.

关键词： Positron emission tomography Symmetry Image alignment Segmentation

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