12782129
Computational measures corresponding to perceptual textural features
Texture is a very important image feature extremely used in various image processing problems. It has been shown that humans use some perceptual textural features to distinguish between textured images or regions. Some of the most important features are coarseness, contrast, direction and busyness. In this paper a new method based on the autocovariance function to estimate quantitatively these features is shown and the correspondence between these computational measures and the psychological ones made by human subjects is shown using some psychometric method.
2622680
A direct approach to graph clustering
1160887
An objective approach to cluster validation
Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perform well when clusters overlap or there is significant variation in their covariance structure. The contribution of this paper is twofold. First, we propose a new validity index for fuzzy clustering. Second, we present a new approach for the objective evaluation of validity indices and clustering algorithms. Our validity index makes use of the covariance structure of clusters, while the evaluation approach utilizes a new concept of overlap rate that gives a formal measure of the difficulty of distinguishing between overlapping clusters. We have carried out experimental studies using data sets containing clusters of different shapes and densities and various overlap rates, in order to show how validity indices behave when clusters become less and less separable. Finally, the effectiveness of the new validity index is also demonstrated on a number of real-life data sets.
14423228
A New Median Graph Algorithm
Median graph is an important new concept introduced to represent a set of graphs by a representative graph. Computing the median graph is an NP-Complete problem. In this paper, we propose an approximate algorithm for computing the median graph. Our algorithm performs in two steps. It first carries out a node reduction process using a clustering method to extract a subset of most representative node labels. It then searches for the median graph candidates from the reduced subset of node labels according to a deterministic strategy to explore the candidate space. Comparison with the genetic search based algorithm will be reported. This algorithm can be used to build a graph clustering algorithm.
18505600
FCM-Based Model Selection Algorithms for Determining the Number of Clusters
Clustering is an important research topic that has practical applications in many fields. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initializing parameters. The main goal of this paper is to develop an effective fuzzy algorithm for automatically determining the number of clusters. After a brief review of the relevant literature, we present a new algorithm for determining the number of clusters in a given data set and a new validity index for measuring the "goodness" of clustering. Experimental results and comparisons are given to illustrate the performance of the new algorithm.
9573239
A new algorithm for inexact graph matching
The graph is an essential data structure for representing relational information. When graphs are used to represent objects, comparing objects amounts to graph matching. Inexact graph matching is the process of finding the best possible matching between two graphs when exact matching is impossible. We propose a new algorithm for the inexact matching problem. The new algorithm decomposes the matching process into K phases, where the value of K ranges from 1 to the minimum of the numbers of nodes in the two graphs to be matched. The efficiency of the new algorithm results from the use of small values of K, significantly reducing the search space while still producing very good matchings (most of them optimal) between graphs. The algorithm is compared with the error-correcting subgraph isomorphism algorithm based on A*.
166912773
FAIRY: Personalized Information Dissemination System Based on How-Net
10877540
SMIS - a real-time stereoscopic medical imaging system
In this paper, we present an X-ray radiography system which can provide intra-operative stereo imaging of the patient's operational area during a spinal surgery. We use two horizontally separated X-ray tubes and a flat-panel X-ray detector mounted on two ends of a C-arm microcontroller synchronizes the image acquisition, and the left and right images are grabbed into the system alternately. Surgeons can have a 3D perception of the spine's X-ray image in real-time. We further developed a whole image processing language called PPL. PPL takes images as operands instead of dealing with complex pixel operation. To speed up the rendering speed in a network environment, we proposed a level of detail (LOD) with reverse prediction method that can fast transmit images over Internet in real-time.
6620052
ScatterType: a legible but hard-to-segment CAPTCHA
The ScatterType CAPTCHA (completely automated public Turing tests to tell computers and humans apart), designed to resist character-segmentation attacks and shown to be highly legible to human readers, is analyzed for vulnerabilities and is offered for experiments in automatic attack. As introduced in Baird and Riopka (2005), 'ScatterType' challenges are images of machine-print text whose characters are cut into pieces which then drift apart, in an attempt to frustrate segment-then-recognize computer vision attacks. Analysis of experimental human legibility data has shown that better than 95% correct legibility can be achieved through judicious choice of the pseudorandom generating parameters (Baird et al., 2005). That analysis is summarized and discussed here as motivation for a discussion of potential vulnerabilities. An invitation to attack ScatterType is offered.
16031876
Shape deformation: SVM regression and application to medical image segmentation
This paper presents a novel landmark-based shape deformation method. This method effectively solves two problems inherent in landmark-based shape deformation: (a) identification of landmark points from a given input image, and (b) regularized deformation the shape of an an object defined in a template. The second problem is solved using a new constrained support vector machine (SVM) regression technique, in which a thin-plate kernel is utilized to provide non-rigid shape deformations. This method offers several advantages over existing landmark-based methods. First, it has a unique capability to detect and use multiple candidate landmark points in an input image to improve landmark detection. Second, it can handle the case of missing landmarks, which often arises in dealing with occluded images. We have applied the proposed method to extract the scalp contours from brain cryosection images with very encouraging results.
14614060
A feature-based scalable codec for image compression
This paper presents a feature-based scheme for image compression. In the proposed method, the features in an image are recorded by three-dimensional verge points, which are defined as high curvature points on the image surface. The verge point representation can be further condensed using B-spline control point approximation. By encoding and transmitting these control points, the features in an image could be revealed at the decoder side. Furthermore, by selecting different coding modes, the proposed method can offer diversified scalabilities, including SNR scalability, spatial scalability, and shape scalability. In addition, this coding technique is suitable for region of interest selection and transmission. Experimental results are provided to present the scalability and interactivity of the proposed method.