9269626
A Wavelet-Based Preprocessing for Moving Object Segmentation in Video Sequences
A simple preprocessing method for extracting boundary regions of moving objects in a video sequence is presented. We use Chui's overssampled shift-invariant wavelet transform and the multiresolution motion estimation and compensation in the wavelet domain. Dominant prediction errors often appear along the boundary of a moving object. Our algorithm is developed to detect boundary regions at a coarse scale by utilizing the prediction error information provided in all subband images at the coarse resolution. This is taken as our first step toward the video object segmentation for use in the wavelet-based MPEG-4.
39573011
The adaptive modulated wavelet transform image representation
In this paper, a new, called the adaptive modulated wavelet transform (AMWT) image representation is presented. One of the attractive features is that the informative instantaneous frequencies of images can be taken into account to improve the representation performance via adaptation of the modulating frequencies involved. The transform coefficients in both wavelet and modulated wavelet domains are uniformly quantized with several quantization levels. The computed peak signal-to-noise ratio values and entropies are used as rate distortion curves for performance comparison. Experimental results show that AMWT out performs wavelet transform for representing images containing textures with rapid variation in grays.
9155774
Image Registration Using Wavelet-Based Motion Model
An image registration algorithm is developed to estimate dense motion vectors between two images using the coarse-to-fine wavelet-based motion model. This motion model is described by a linear combination of hierarchical basis functions proposed by Cai and Wang (SIAM Numer. Anal., 33(3):937–970, 1996). The coarser-scale basis function has larger support while the finer-scale basis function has smaller support. With these variable supports in full resolution, the basis functions serve as large-to-small windows so that the global and local information can be incorporated concurrently for image matching, especially for recovering motion vectors containing large displacements. To evaluate the accuracy of the wavelet-based method, two sets of test images were experimented using both the wavelet-based method and a leading pyramid spline-based method by Szeliski et al. (International Journal of Computer Vision, 22(3):199–218, 1996). One set of test images, taken from Barron et al. (International Journal of Computer Vision, 12:43–77, 1994), contains small displacements. The other set exhibits low texture or spatial aliasing after image blurring and contains large displacements. The experimental results showed that our wavelet-based method produced better motion estimates with error distributions having a smaller mean and smaller standard deviation.
8674625
Image coding with modulated wavelets
In this paper, a subband image-coding scheme with adapted modulated wavelets is presented. Due to the fact that Gabor functions mimic well the simple cells of mammalian visual receptive field with optimum localization in the joint space-spatial frequency domain, we divide the image into subbands using discrete Gabor transform; decompose each subband into modulated wavelet (MW) coefficients with adapted modulating frequencies on a subband basis; and encode the complex-valued MW coefficients using the modified SPIHT in an embedded manner. Experimental results show that the proposed MW transform based subband coding is preferable to the wavelet transform based coding for images composed of textures with rapid variation in grays.
342925
Signaling P Systems and Verification Problems
We introduce a new model of membrane computing system (or P system), called signaling P system. It turns out that signaling systems are a form of P systems with promoters that have been studied earlier in the literature. However, unlike non-cooperative P systems with promoters, which are known to be universal, non-cooperative signaling systems have decidable reachability properties. Our focus in this paper is on verification problems of signaling systems; i.e., algorithmic solutions to a verification query on whether a given signaling system satisfies some desired behavioral property. Such solutions not only help us understand the power of “maximal parallelism” in P systems but also would provide a way to validate a (signaling) P system in vitro through digital computers when the P system is intended to simulate living cells. We present decidable and undecidable properties of the model of non-cooperative signaling systems using proof techniques that we believe are new in the P system area. For the positive results, we use a form of “upper-closed sets” to serve as a symbolic representation for configuration sets of the system, and prove decidable symbolic model-checking properties about them using backward reachability analysis. For the negative results, we use a reduction via the undecidability of Hilbert’s Tenth Problem. This is in contrast to previous proofs of universality in P systems where almost always the reduction is via matrix grammar with appearance checking or through Minsky’s two-counter machines. Here, we employ a new tool using Diophantine equations, which facilitates elegant proofs of the undecidable results. With multiplication being easily implemented under maximal parallelism, we feel that our new technique is of interest in its own right and might find additional applications in P systems.
15246731
Constructions of Self-complementary Circulants with No Multiplicative Isomorphisms
The main topic of the paper is the question of the existence of self-complementary Cayley graphs Cay(G, S) with the property S??=G#\S for all ??Aut(G). We answer this question in the positive by constructing an infinite family of self-complementary circulants with this property. Moreover, we obtain a complete classification of primes p for which there exist self-complementary circulants of order p2with this property.
222927
Regularized color clustering in medical image database
A regularized color clustering algorithm is proposed to solve the color clustering problem in medical image database. By incorporating both measures of cluster separability and cluster compactness, regularized color clustering allows the automatic extraction of significant color groups with varying populations. Experimental results in different color spaces show that the regularized color clustering gives superior results in extracting significant distinct/abnormal color clusters without significant increases in cluster compactness. Furthermore, results of color clustering in different color spaces show that the LUV color space is more suitable for color clustering. Methods for selecting the regularization constants have also been suggested.
15590248
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.
35008704
Image segmentation based on a multiresolution Bayesian framework
A multiresolution Markov random field (MMRF) approach to texture segmentation which makes use of both regional and boundary information is proposed in this work. The algorithm is designed to improve the efficiency and solve two problems: over-segmentation and under-segmentation. At each resolution level, regional information and boundary information between textured regions is extracted to describe each block using the multiresolution Fourier transform (MFT). The image is then modelled as a sequence of Markov random fields (MRFs) of varying resolution and a Gibbs sampling scheme is applied to label the constituent sites at each scale by seeking the configuration with minimal interaction energy. The interaction energy is defined as a function of the regional and boundary information. The constraints of region context, and boundary smoothness and connectivity are also encoded in the interaction energy. Once the algorithm converges at a given scale, the segmentation result is propagated down to the next resolution for further refinement.