An optical diagnostic technique has been developed to measure the gas-liquid interfacial film thickness in microcapillary two-phase flows. The spatial frequencies from the multiscattering measured with a CCD camera ar...
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An optical diagnostic technique has been developed to measure the gas-liquid interfacial film thickness in microcapillary two-phase flows. The spatial frequencies from the multiscattering measured with a CCD camera are used to determine the slug diameter and film thickness. It is found that, with an optimized optical orientation angle, the spatial frequency method shows great accuracy in the measurements. To demonstrate the capability of the newly developed method, a validation experiment was conducted in water-air and water-honey mixture-air two-phase flows. We measured the spatial frequency variations when the microbubble and slug were pulsating by utilizing a highly accurate signalprocessing technique and a five-point interpolation method. This newly developed optical method is easy to implement, and it will be a useful technique for two-phase flow measurements. (c) 2005 Optical Society of America.
A Support Vector Machine (SVM) with the auto-correlation of compactly supported wavelet as kernel is proposed in this paper. It is proved that this kernel is an admissible support vector kernel. The main advantage of ...
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ISBN:
(纸本)0769523196
A Support Vector Machine (SVM) with the auto-correlation of compactly supported wavelet as kernel is proposed in this paper. It is proved that this kernel is an admissible support vector kernel. The main advantage of the auto-correlation of a compactly supported wavelet is that it satisfies the translation invariant property, which is very important for signalprocessing. Also, we can choose a better wavelet from different choices of wavelet families for our auto- correlation wavelet kernel. Experiments on signal regression show that this method is better than the existing SVM function regression with the scalar wavelet kernel, the Gaussian kernel, and the exponential radial basis function kernel. It can be easily extended to other applications such as pattern recognition by using this newly developed auto- correlation wavelet SVM.
This paper presents a flexible hardware architecture for performing the Discrete Wavelet Transform (DWT) on a digital image. The proposed architecture uses a variation of the lifting scheme technique and provides adva...
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ISBN:
(纸本)0819456446
This paper presents a flexible hardware architecture for performing the Discrete Wavelet Transform (DWT) on a digital image. The proposed architecture uses a variation of the lifting scheme technique and provides advantages that include small memory requirements, fixed-point arithmetic implementation, and a small number of arithmetic computations. The DWT core may be used for imageprocessing operations, such as denoising and image compression. For example, the JPEG2000 still image compression standard uses the Cohen-Daubechies-Favreau (CDF) 5/3 and CDF 9/7 DWT for lossless and lossy image compression respectively. Simple wavelet image denoising techniques resulted in improved images up to 27 dB PSNR. The DWT core is modeled using MATLAB and VHDL. The VHDL model is synthesized to a xilinx FPGA to demonstrate hardware functionality. The CDF 5/3 and CDF 9/7 versions of the DWT are both modeled and used as comparisons. The execution time for performing both DWTs is nearly identical at approximately 14 clock cycles per image pixel for one level of DWT decomposition. The hardware area generated for the CDF 5/3 is around 15,000 gates using only 5% of the xilinx FPGA hardware area, at 2.185 MHz max clock speed and 24 mW power consumption.
In this paper, we discuss some of the leading issues in through the wall radar imaging (TWRI) problems. We focus on the primary system challenges and deliverables, dealing only with the applications of statistical sig...
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ISBN:
(纸本)081945804X
In this paper, we discuss some of the leading issues in through the wall radar imaging (TWRI) problems. We focus on the primary system challenges and deliverables, dealing only with the applications of statistical signal and array processing. applications of antenna design and electromagnetic propagation are equally important, but they are both outside the scope of this paper. The material presented considers key desirable TWRI system properties and features and provides candidate solutions to achieve them. We focus on research performed at Villanova University and demonstrate some of our recent approaches to address system functionalities and requirements using analyses, computer simulations, and real-data. The paper does not attempt to cover all progress made in the field to date nor does it intend to compare the proposed techniques with alternative and competitive methods. It is written with the primary purpose of bringing to the reader many leading challenges and diverse issues worthy of considerations.
The ability to transform facial images between groups (e.g. from young to old, or from male to female) has applications in psychological research, police investigations, medicine and entertainment. Current techniques ...
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The ability to transform facial images between groups (e.g. from young to old, or from male to female) has applications in psychological research, police investigations, medicine and entertainment. Current techniques suffer either from a lack of realism due to unrealistic or inappropriate textures in the output images, or a lack of statistical validity, e.g. by using only a single example image for training. This paper describes a new method for improving the realism and effectiveness of facial transformations (e.g. ageing, feminising etc.) of individuals. The method aims to transform low resolution image data using the mean differences between the two groups, but converges on more specific texture features at the finer resolutions. We separate high and low resolution information by transforming the image into a wavelet domain. At each point we calculate a mapping from the original set to the target set based on the probability distributions of the input and output wavelet values. These distributions are estimated from the example images, using the assumption that the distribution depends on the values in a local neighbourhood of the point (the Markov Random Field (MRF) assumption). We use a causal neighbourhood that spans multiple coarser scales of the wavelet pyramid.. The distributions are estimated by smoothing the histogram of example values. By increasing the smoothing of the histograms at coarser resolutions we are able to maintain perceived identity across the transforms while producing realistic fine-scale textures. We use perceptual testing to validate the new method, and the results show that it can produce more accurate shifts in perceived age and an increase in realism.
The proceedings contain 20 papers. The topics discussed include: analysis of multiband astronomical images using multiscale tools;wavelet-based segmentation of multi-component images;evaluation of multisensor image fu...
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The proceedings contain 20 papers. The topics discussed include: analysis of multiband astronomical images using multiscale tools;wavelet-based segmentation of multi-component images;evaluation of multisensor image fusion using different wavelet transform;wavelet-based enhancement of remote sensing and biomedical image series using an auxiliary image;Gabor filters in industrial inspection: a review. Application to semiconductor industry;a new wavelet sub-band characterization for texture recognition;classification of FTIR cancer data using wavelets and fuzzy C-means clustering;a general approach to defect detection in textured materials using a wavelet domain model and level sets;and normal offsets for digital image compression.
This paper introduces a simple and efficient representation for natural images. We partition an image into blocks and treat the blocks as vectors in a high-dimensional space. We then fit a piece-wise linear model (i.e...
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ISBN:
(纸本)076952334X
This paper introduces a simple and efficient representation for natural images. We partition an image into blocks and treat the blocks as vectors in a high-dimensional space. We then fit a piece-wise linear model (i.e. a union of affine subspaces) to the vectors at each down-sampling scale. We call this a multi-scale hybrid linear model of the image. The hybrid and hierarchical structure of this model allows us effectively to extract and exploit multi-modal correlations among the imagery data at different scales. It conceptually and computationally remedies limitations of many existing image representation methods that are based on either a fixed linear transformation (e.g. DCT wavelets), an adaptive uni-modal linear transformation (e.g. PCA), or a multi-modal model at a single scale. We will justify both analytically and experimentally why and how such a simple multi-scale hybrid model is able to reduce simultaneously the model complexity and computational cost. Despite a small overhead for the model, our results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal-to-noise ratio than many existing methods, including wavelets.
In this paper, we propose a new method for estimation of the number of embedding changes for non-adaptive +/- k embedding in images. By modeling the cover image and the stego noise as additive mixture of random proces...
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ISBN:
(纸本)0819456543
In this paper, we propose a new method for estimation of the number of embedding changes for non-adaptive +/- k embedding in images. By modeling the cover image and the stego noise as additive mixture of random processes, the stego message is estimated from the stego image using a denoising filter in the wavelet domain. The stego message estimate is further analyzed using ML/MAP estimators to identify the pixels that were modified during embedding. For non-adaptive +/- k embedding, the density of embedding changes is estimated from selected segments of the stego image. It is shown that for images with a low level of noise (e.g., for decompressed JPEG images) this approach can detect and estimate the number of embedding changes even for small values of k, such as k=2, and in some cases even for k=1.
Estimation of difference between curves (curve matching) is a useful and often necessary technique in many applications, including: pattern recognition, image object recognition, robotic applications, computational ge...
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ISBN:
(纸本)953184089X
Estimation of difference between curves (curve matching) is a useful and often necessary technique in many applications, including: pattern recognition, image object recognition, robotic applications, computational geometry, etc. In this paper, three methods for curve matching using turning functions are presented. While the first two, called plain and polygonal method, are based on a simple adaptation of the existing approaches, the third one, called penalty method, is a new one and tries to overcome some important problems from the first two. The advantages and essential problems of the proposed methods are also discussed. A number of examples are presented to show major differences among the methods and their potential usefulness.
The wavelet transform has been widely used for defect detection and classification in fabric images. The detection and classification performance of the wavelet transform approach is closely related to the selection o...
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The wavelet transform has been widely used for defect detection and classification in fabric images. The detection and classification performance of the wavelet transform approach is closely related to the selection of the wavelet. Instead of predetermining a wavelet, a method of designing a wavelet adapting to the detection or classification of the fabric defects has been developed. For further improvement of the performance, this paper extends the adaptive wavelet-based methodology from the use of a single adaptive wavelet to multiple adaptive wavelets. For each class of fabric defect, a defect-specific adaptive wavelet was designed to enhance the defect region at one channel of the wavelet transform, where the defect region can be detected by using a simple threshold classifier. Corresponding to the multiple defect-specific adaptive wavelets, the multiscale edge responses to defect regions have been shown to be more efficient in characterising the defects, which leads to a new approach to the classification of defects. In comparison with the single adaptive wavelet approach, the use of multiple adaptive wavelets yields better performance on defect detection and classification, especially for defects that are poorly detected by the single adaptive wavelet approach. The proposed method using multiple adaptive wavelets has been evaluated on the inspection of 56 images containing eight classes of fabric defects, and 64 images without defects, where 98.2% detection rate and 1.5% false alarm rate were achieved in defect detection, and 97.5% classification accuracy was achieved in defect classification.
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