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Using active contours and Fourier descriptors for motion tracking with applications in MRI

Using active contours and Fourier descriptors for motion tra...

引用

IEEE International Conference on image processing

作者： D.L. Guidry A.A. Farag Computer Vision and Image Processing Lab Electrical Engineering Department University of Louisville Louisville KY USA

To estimate motion, the edges of a moving object can be detected using active contours. After detecting the edges throughout the duration of the motion, the contours are registered together in order to obtain the motion model parameters. This idea can be applied to motion artifacts in Magnetic Resonance images (MRI) which are caused by patient motion during the acquisition of an MRI. Thus, if the patient motion can be determined, it will be possible to correct for the motion in the images. The proposed method eliminates motion artifacts by dividing the acquired MRI data into several subbands. These subbands create invariant tissue feature maps called subband images. Using active contours, the edges of each subband image are detected and the relative motion is analyzed across the different subband images, thus determining the motion parameters. From these motion parameters it is possible to correct the subband images, thus correcting the k-space yielding clear, noise-free MR images.

关键词： Active contours Tracking image edge detection Motion detection Magnetic resonance imaging Motion analysis Motion estimation Object detection Magnetic resonance image analysis

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On the robustness of vector set partitioning image coders to bit errors

On the robustness of vector set partitioning image coders to...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： D. Mukherjee S.K. Mitra Image Processing Laboratory Department of Electrical & Computer Engineering University of California Santa Barbara CA USA

A vector enhancement of Said and Pearlman's Set Partitioning in Hierarchical Trees (SPIHT) methodology, named VSPIHT has recently been proposed for embedded wavelet image compression. A major advantage of vector based embedded coding with fixed length VQs over scalar embedded coding, is its superior robustness to noise. In this work we show that vector set partitioning can effectively alter the balance of bits in the bit stream so that significantly fewer critical bits carrying significant information is transmitted, thereby improving inherent noise resilience. Additionally, the degradation in reconstruction quality caused by errors in non-critical quantization information, can be reduced by appropriate VQ indexing, or designing channel optimized VQs for the successive refinement systems. For very noisy channels unequal error protection to the critical and non critical bits with either block codes or convolution codes are used. Extensive simulation results are presented.

关键词： image coding Noise robustness Streaming media Resilience Degradation image reconstruction Quantization Indexing Design optimization Error correction codes

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Encoding of predictive error frames in rate scalable video codecs using wavelet shrinkage

Encoding of predictive error frames in rate scalable video c...

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IEEE International Conference on image processing

作者： E. Asbun P. Salama E.J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Rate scalable video compression is appealing for low bit rate applications, such as video telephony and wireless communication, where bandwidth available to an application cannot be guaranteed. In this paper, we investigate a set of strategies to increase the performance of SAMCoW, a rate scalable encoder. These techniques are based on based on wavelet decomposition, spatial orientation trees, and motion compensation.

关键词： Encoding Video codecs image coding Video compression Bit rate Application software Wavelet coefficients Scalability computer errors image processing

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Object recognition using neural networks and surface signatures

Object recognition using neural networks and surface signatu...

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International Joint Conference on Neural Networks (IJCNN)

作者： S.M. Yamany A.M. El-Bialy A.A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Systems and Biomedical Engineering Department Cairo University Egypt

We present a concept for 3D free-form object recognition using a surface representation scheme. This representation scheme captures the 3D curvature information of any free-form surface and encodes it into a 2D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image the surface point signature (SPS). Using specially designed neural networks; the SPS images are used in the matching and recognition of 3D objects in a 3D scanned scene.

关键词： Object recognition Neural networks Layout image recognition Shape image processing Laboratories computer vision Robot vision systems Application software

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Free-form object recognition and registration using surface signatures

Free-form object recognition and registration using surface ...

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IEEE International Conference on image processing

作者： S.M. Yamany A.A. Farag A. El-Bialy Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Systems and Biomedical Engineering Department Cairo University Cairo Egypt

This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-form surface and encodes it into a 2-D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image “Surface Point Signature” (SPS). This scheme can be used as a global representation of the surface as well as a local one and also in a scale independent surface matching. It performs faster registration than existing registration approaches. We applied this technique in object registration, multimodal medical image registration and the recognition of multiple objects in a 3-D scene.

关键词： Object recognition Biomedical imaging image recognition Layout Shape image processing Laboratories image registration Robot vision systems Contracts

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Fully constrained least-squares based linear unmixing [hyperspectral image classification]

Fully constrained least-squares based linear unmixing [hyper...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： D. Heinz C.-I. Chang M.L.G. Althouse Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

A fully constrained least-squares linear unmixing approach to hyperspectral image classification is presented. It is derived from an unconstrained least-squares based orthogonal subspace projection. It is similar to a method developed by Shimabukuro and Smith (1991) in the least-squares sense, but significantly different from their method in the way of implementing the constraints. Since there is no closed form solution available, an efficient algorithm is developed for finding a fully constrained solution, which can be viewed as a generalization of Shimabukuro and Smith's method. The effectiveness of this algorithm is demonstrated through computer simulations and real data experiments.

关键词： Vectors Subspace constraints Pixel image processing Equations Hyperspectral imaging Remote sensing Signal processing Laboratories computer science

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Linearly constrained minimum variance beamforming approach to target detection and classification for hyperspectral imagery

Linearly constrained minimum variance beamforming approach t...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chein-I Chang Hsuan Ren Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Subspace projection and maximum likelihood have been used for hyperspectral image classification. One of their disadvantages is the requirement of a priori knowledge about material signatures resident in images which may be difficult to obtain in many practical applications. In order to resolve this problem, an approach, called Constrained Energy Minimization (CEM) was developed for target detection where the only required knowledge is the target signature of interest. It was derived based on the Minimum Variance Distortionless Response (MVDR) in array processing. However, CEM has been also shown to be very sensitive to the knowledge being used to describe the target signature. This paper develops a more general approach, called Linearly Constrained Minimum Variance (LCMV) beamforming which includes CEM as a special case. Unlike CEM which considers a single constraint, LCMV uses multiple signatures to extend the capability of CEM to various target classification. More specifically, LCMV can be implemented as an estimator for target classification while CEM can be used as a detector for target detection.

关键词： Array signal processing Object detection Finite impulse response filter Hyperspectral sensors Hyperspectral imaging Nonlinear filters image classification Vectors Power generation Layout

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Spectral information divergence for hyperspectral image analysis

Spectral information divergence for hyperspectral image anal...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

The authors propose an information theoretic criterion, called spectral information divergence (SID) for spectral similarity and discriminability. It is derived from the concept of divergence arising in information theory and can be used to describe the statistics of a spectrum. Unlike spectral angle mapper (SAM) which extracts geometric features between two spectra, SID views each pixel spectrum as a random variable and then measures the discrepancy of probabilistic behaviors between two spectra. In order to evaluate SID, SAM is used for comparison via hyperspectral data. Experimental results show that SID can characterise spectral similarity and variability more effectively than SAM.

关键词： Hyperspectral imaging image analysis Hyperspectral sensors Random variables Remote sensing Information theory Pixel Data mining Interference Pattern classification

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An interference rejection-based radial basis function neural network for hyperspectral image classification

An interference rejection-based radial basis function neural...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qian Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA

A new application for RBF neural networks in nonlinear mixed pixel classification for hyperspectral imaging is presented. It is a three-layer neural network with the input layer specified by spectral signatures of a mixed pixel vector, the hidden layer used for nonlinear mixing functions and the output layer used to produce classification results of the mixed pixel vector. A noise estimation method in conjunction with noise subspace projection is developed to reliably estimate the member of mixing materials plus interference signatures that can be used as the number of hidden nodes as well as the member of input nodes. The least-mean-square learning algorithm is applied to adjust parameters used in the hidden layer and weights of the output layers adaptively and simultaneously so as to achieve best possible performance. The performance is evaluated through a series of experiments via AVIRIS data. A comparative analysis is also conducted among various methods.

关键词： Interference Radial basis function networks Hyperspectral imaging Hyperspectral sensors Pixel image classification Remote sensing Laboratories image sensors Spectroscopy

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Novel surface registration using the grid closest point (GCP) transform

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IEEE International Conference on image processing

作者： S.M. Yamany M.N. Ahmed E.E. Hemayed A.A. Farag Computer Vision and Image Processing Laboratory Electrical Engineering Department University of Louisville Louisville KY USA

ISBN: (纸本)0818688211

A novel approach has been developed for fast registration of two sets of 3-D curves or surfaces. The technique is an extension of Besl and Mackay's (1992) iterative closest point (ICP) algorithm. This technique solves the computational complexity associated with the ICP algorithm by applying a novel grid closest point (GCP) transform and a genetic algorithm to minimize the cost function. A detailed description of the algorithm is presented along with a comparison of its performance versus several registration techniques. Two applications are presented in this paper. In the first, the algorithm is used to register 2-D head contours extracted from CT/MRI data to correct for possible mis-alignment caused by motion artifacts during scanning. In the second, the algorithm is used to register 3-D segments of the human jaw obtained using the shape from shading technique. Registration using the GCP/GA technique is found to be significantly faster and of comparable accuracy than two popular techniques in the computer vision and medical imaging literature.

关键词： Iterative algorithms Iterative closest point algorithm Computational complexity Genetic algorithms Cost function Head Data mining Computed tomography Magnetic resonance imaging Registers

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