6531229
Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm
This work approaches the texture segmentation problem by incorporating Gibbs sampler (i.e., the combination of Markov random fields and simulated annealing) and a region-merging process within a multiresolution structure with "high class resolution and low boundary resolution" at high levels and "low class resolution and high boundary resolution" at lower ones. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class-boundary uncertainty and to improve the segmentation accuracy. The computational complexity and frequent occurrences of over-segmentation of Gibbs sampler are addressed and the computationally and functionally effective region-merging process is included to allow Gibbs sampler to start its annealing schedule at relatively low pseudo-temperature and to guide the search trajectory away from local minima associated with over-segmented configurations.
24590909
Fragile watermarking scheme exploiting non-deterministic block-wise dependency
Block-wise dependency is recognized as a key requirement in fragile watermarking schemes to thwart the vector quantization (VQ) attack. It has also been observed that deterministic dependency is still susceptible to transplantation attack or even simple cover-up attack. In this work, a new fragile watermarking scheme exploiting non-deterministic dependency is proposed. To watermark the original image, each pixel is adjusted according to the consistency between a key-dependent binary watermark bit, the information from the pixel itself and the non-deterministic secret dependency information calculated from a neighborhood centered at that pixel. Localization resolution is enhanced with a post-processing operation on the output difference map.
23059132
Image authentication and integrity verification via content-based watermarks and a public key cryptosystem
A technique using the inherent feature map of the underlying image as the watermark is proposed in this work. First, the binary feature map is extracted as watermark and partitioned into blocks. Secondly, neighboring feature map blocks are blended and encrypted for insertion. On the receiver side, the feature map from the received image is extracted again and compared against the recovered watermark to verify the integrity and authenticity. In addition the capability of detecting geometric transformation, removal of original objects and addition of foreign objects, the proposed scheme is also capable of localizing tampering and detecting cropping without a priori knowledge about the image.
14325657
An approach to reducing the labeling cost of Markov random fields within an infinite label space
This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random fields (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually infinite label space is confined in a small finite space. Globally, the number of labels allowed is as many as the number of image sites while locally the optimal label is sampled from a space consisting of the labels assigned to the four-neighbor plus a random one. Neither the prior knowledge about the number of classes nor the estimation phase of the class number is required in this work. The proposed method is applied to the problem of texture segmentation and the result is compared with those obtained from conventional methods.
43675712
Unsupervised Image Segmentation using Gibbs Sampler within a Multiresolution Framework
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.
29832620
A Family of Quasiprimitive 2-arc Transitive Graphs which Have Non-quasiprimitive Full Automorphism Groups
For each primep? 1 or ?1(mod24), we construct a (PSL(2,p), 2)-arc transitive graph ? of valency 4 such that Aut ?=PSL(2,p) ×Z2. Thus there exists an infinite family of quasiprimitive 2-arc transitive graphs which have non-quasiprimitive full automorphism groups.
12615212
Finite locally-quasiprimitive graphs
A finite graph is said to be locally-quasiprimitive relative to a subgroup G of automorphisms if, for all vertices α, the stabiliser in G of α is quasiprimitive on the set of vertices adjacent to α. (A permutation group is said to be quasiprimitive if all of its non-trivial normal subgroups are transitive.) The graph theoretic condition of local quasiprimitivity is strictly weaker than the conditions of local primitivity and 2-arc transitivity which have been studied previously. It is shown that the family of locally-quasiprimitive graphs is closed under the formation of a certain kind of quotient graph, called a normal quotient, induced by a normal subgroup. Moreover, a locally-quasiprimitive graph is proved to be a multicover of each of its normal quotients. Thus finite locally-quasiprimitive graphs which are minimal in the sense that they have no non-trivial proper normal quotients form an important sub-family, since each finite locally-quasiprimitive graph has at least one such graph as a normal quotient. These minimal graphs in the family are called "basic" locally-quasiprimitive graphs, and their structure is analysed. The process of constructing locally-quasiprimitive graphs with a given locally-quasiprimitive graph Σ as a normal quotient is then considered. It turns out that this can be viewed as a problem of constructing covering graphs of certain multigraphs associated with Σ. Further, it is shown that, under certain conditions, a locally-quasiprimitive graph can be reconstructed from knowledge of two of its normal quotients. Finally a series of open problems is presented.
34090759
Segmentation of external force field for automatic initialization and splitting of snakes
Active contours or snakes have been extensively utilized in handling image segmentation and classification problems. In traditional active contour models, snake initialization is performed manually by users, and topological changes, such as splitting of the snake, cannot be automatically handled. In this paper, we introduce a new method to solve the snake initialization and splitting problem, based on an area segmentation approach: the external force field is segmented first, and then the snake initialization and splitting can be automatically performed by using the segmented external force field. Such initialization and splitting produces multiple snakes, each of which is within the capture range associated to an object and will be evolved to the object boundary. The external force used in this paper is a gradient vector flow with an edge-preserving property (EPGVF), which can prevent the snakes from passing over weak boundaries. To segment the external force field, we represent it with a graph, and a graph-theory approach can be taken to determine the membership of each pixel. Experimental results establish the effectiveness of the proposed approach.
2028079
Strong uniqueness of the restricted Chebyshev center with respect to an RS-set in a Banach space
Let G be a strict RS-set (resp. an RS-set) in X and let F be a bounded (resp. totally bounded) subset of X satisfying rG (F) < rx (F), where rG (F) is the restricted Chebyshev radius of F with respect to G. It is shown that the restricted Chebyshev center of F with respect to G is strongly unique in the case when X is a real Banach space, and that, under some additional convexity assumptions, the restricted Chebyshev center of F with respect to G is strongly unique of order α ≥ 2 in the case when X is a complex Banach space.
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.