14070336
Image segmentation via brittle fracture mechanics
Brittle material like ceramic and rock has the nature that it is hard to crack, but if it is to crack it will crack into not dangling pieces, but separate pieces whose boundaries are contours that link up positions of strong stress in a continuous and often smooth fashion. Image segmentation desires similar results: it is to partition the image so that each partition corresponds to a separate surface or object in the imaged scene, whose boundaries are continuous and often smooth contours with a dense concentration of large intensity gradients. This work explores the adaptation of brittle fracture mechanics model toward image segmentation. An image is regarded as a plate experiencing brittle fracture when external forces arc exerted on it according to intensity gradient distribution in the image. Stress concentrations due to the external forces and its change over time due to changing crack distribution are what determine the development of cracks. Real image results indicate that the model is promising.
9910247
Image Segmentation That Optimizes Global Homogeneity in a Variational Framework
A two-phase segmentation mechanism is described that allows a global homogeneity-related measure to be optimized in a level-set formulation. The mechanism has uniform treatment toward texture, gray level, and color boundaries. Intensities or colors of the image are first coarsely quantized into a number of classes. Then a class map is formed by having each pixel labeled with the class identity its gray or color level is associated with. With this class map, for any segmented region, it can be determined which pixels inside the region belong to which classes, and it can even be calculated how spread-out each of such classes is inside the region. The average spread-size of the classes in the region, in comparison with the size of the region, then constitutes a good measure in evaluating how homogeneous the region is. With the measure, the segmentation problem can be formulated as the optimization of the average homogeneity of the segmented regions. This work contributes chiefly by expressing the above optimization functional in such a way that allows it to be encoded in a variational formulation and that the solution can be reached by the deformation of an active contour. In addition, to solve the problem of multiple optima, this work incorporates an additional geodesic term into the functional of the optimization to maintain the active contour's mobility at even adverse condition of the deformation process. Experimental results on synthetic and real images are presented to demonstrate the performance of the mechanism.
32534487
Image Segmentation That Merges Together Boundary and Region Information
Segmentation is a classical yet important problem in vision. Most of the previous works are either region-based or boundary-based. The two approaches own complementary merits - while the region-based one always produces closed boundaries, the boundary-based one involves primarily local operations and avoids the complexity of deciding how homogeneous a region and how inhomogeneous neighboring regions should be. In this paper, we propose a new solution mechanism that makes use of both cues. We use the boundary processing and a particular field model to come up with a number of coarse, initial closed boundaries about the image first. Such coarse boundaries will then, through an adaptation of the Four Color Theorem, serve as the initialization to a level-set method-based minimization that acts on the intensity distribution of the image, and allows the final crispy segmentation result to emerge. Compared with the existing solutions, our method requires no initialization from the user, and the automatically extracted closed contours do provide guidance to derive more optimal and smoother segmentation result. Experimental results with some benchmarking image-sets show that the proposed solution could deliver accurate segmentation boundary.
12605077
The Multiplicative Path Toward Prior-Shape Guided Active Contour for Object Detection
In detecting the boundary of an object in an image, if certain prior shape knowledge of the object is available, an effective approach is to have the intensity gradient information in the image and the prior shape knowledge be combined together to drive an active contour for the purpose. While in the classical methods the two terms are almost always summed with a certain weight between them to form the optimization functional, in the method we propose, they are multiplied together so as to avoid the need and thus design of the weight parameter. We show that the object detection result in the traditional formulation could indeed be very much affected by the weight value, and the proposed method, being without its presence, is therefore free from the influence of the important parameter. Experimental results on cells in real biological images, whose boundaries are blurred to very different degrees across the image by the inevitably uneven illumination, are shown to demonstrate the improvement in performance.
2629783
Optimal Rate-Based Image Transmissions via Multiple Paths in Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a multi-level rate-oriented routing (MLRR) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate multiple rate-oriented routing paths. The simulation results by using GA algorithm show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.
16734183
An Optimal Approach for Image Transmission in Multi-Rate Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in Wireless sensor network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a multi-rate least cost path (MRLCP) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate rate- based routing paths. A specific genetic algorithm is designed to solve such discrete optimization problem. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.
9921122
Probabilistic Skyline Operator over Sliding Windows
Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficient processing of continuous skyline queries over sliding windows on uncertain data elements regarding given probability thresholds. We first characterize what kind of elements we need to keep in our query computation. Then we show the size of dynamically maintained candidate set and the size of skyline. We develop novel, efficient techniques to process a continuous, probabilistic skyline query. Finally, we extend our techniques to the applications where multiple probability thresholds are given or we want to retrieve "top-k" skyline data objects. Our extensive experiments demonstrate that the proposed techniques are very efficient and handle a high-speed data stream in real time.
29715580
A high-speed residue-to-binary converter and a scheme for its VLSI implementation
In this paper, a high-speed residue-to-binary converter for the recently introduced moduli set (2/sup k/, 2/sup k/-1, 2/sup k-1/-1) is proposed. Compared to the previous converter based on this moduli set, the proposed one is 40% faster; also, the time-complexity product is improved by 20%. Following the VLSI design flow established by the Canadian Microelectronics Corporation, the proposed converter is implemented in 0.5-micron CMOS technology. Based on this moduli set, layouts of the 8-bit, 16-bit, 32-bit and 64-bit residue-to-binary converters which can be used in further RNS system designs, are generated and simulation results obtained.
14611333
TAR: temporal association rules on evolving numerical attributes
Data mining has been an area of increasing interest. The association rule discovery problem in particular has been widely studied. However, there are still some unresolved problems. For example, research on mining patterns in the evolution of numerical attributes is still lacking. This is both a challenging problem and one with significant practical applications in business, science, and medicine. In this paper we present a temporal association rule model for evolving numerical attributes. Metrics for qualifying a temporal association rule include the familiar measures of support and strength used in traditional association rule mining and a new metric called density. The density metric not only gives us a way to extract the rules that best represent the data, but also provides an effective mechanism to prune the search space. An efficient algorithm is devised for mining temporal association rules, which utilizes all three thresholds (especially the strength) to prune the search space drastically. Moreover, the resulting rules are represented in a concise manner via rule sets to reduce the output size. Experimental results on real and synthetic data sets demonstrate the efficiency of our algorithm.