27423282
Visualization of DNA/RNA Structure using Temporal CGRs
We present a new DNA/RNA sequence visualization technique, Temporal Chaos Game Representation (TCGR), which is an extension of Frequency Chaos Game Representation (FCGR). Unlike earlier CGR variants, TCGR illustrates the distribution of nucleotide sub-patterns at different positions within a sequence, and it can be applied to sets of sequences as well as individual sequences. TCGRs are suitable for short or long sequences. Preliminary experiments examining patterns of nucleotide occurrences in micro-RNA data illustrate not only the informative nature of the images, but clearly indicate the variability of distributions based on position, and the similarity and differences of distributions across various species
12928468
Distributed goal recognition algorithms for modular robots
Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.
10936975
Quantifying Evolving Processes in Multimodal 3D Medical Images
Quantitative measurements of changes in evolving brain pathology, such as multiple sclerosis lesions and brain tumors, are important for clinicians to perform pertinent diagnoses and to help in patient follow-up. Lesions or tumors can vary over time in size, shape, location and composition because of natural pathological processes or the effect of a drug treatment or therapy. In the past, people have used as a quantitative measurement the change in total or regional lesion/tumor volume. In this paper we propose a new model to quantify changes in evolving processes in multimodal 3D medical images. We believe this model reflects changes in pathology more accurately because it simultaneously takes into account information in multiple imaging modalities and the location of lesion/tumor voxels. We demonstrate the effectiveness of this model with experiments on synthetic lesion data.
53224637
Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram
15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
15617422
R*-Histograms: efficient representation of spatial relations between objects of arbitrary topology
Representation of relative spatial relations between objects is often required in many multimedia database applications because spatial relations between objects in an image convey important information about the image. Quantitative representation of spatial relations taking into account shape, size, orientation and distance is often required. The R-Histogram is such a quantitative representation of spatial relations between two objects. However, this method only considers pixels on the object boundary, assuming that the objects are homeomorphic to a 2-ball. For objects with more complicated topology, we propose in this paper the R*-Histogram, a new extension to the R-Histogram. The R*-Histogram generalizes the R-Histogram by taking into account all the pixels in the objects. We also introduce an efficient O(kN log N) time algorithm to compute the R*-Histogram, which is asymptotically faster than the original O(N<sup>2</sup>) time algorithm for the R-Histogram even when k=O(n). Here, N=n<sup>2</sup> denotes the number of pixels in the processed n x n image and k is the number of different directions considered. The effectiveness of the R*-Histogram is evaluated empirically with a Query By Example (QBE) system on a database of 2000 synthetic images containing objects with complicated shape and topology. Experiments have shown that the similarly search results match human intuition very well.
793367
3D Brain surface matching based on geodesics and local geometry
The non-rigid registration of surfaces is a complex and difficult task for which there are many important applications, such as comparing shape between deformable objects and comparing associated function. This paper presents a new approach for brain surface matching by determining the correspondence of 3D point sets between pairs of surfaces. The algorithm is based on shape using a combination of geodesic distance and surface curvature. There are two major procedures involved. An initial sparse set of corresponding points is first generated by matching local geometrical features. Geodesic distance interpolation is then employed hierarchically in order to capture the complex surface. By this scheme, surface correspondence and triangulation are computed simultaneously. Experiments applied to human cerebral cortical surfaces are shown to evaluate the approach. It is shown that the proposed method performs well for both surface matching and surface shape recovery.
2560769
Invariance Properties of Random Sequences
We present invariance characterizations of different types of random sequences. We correct Schnorr’s original, incorrect characterization of Martin-Lof random sequences, compare it with Schnorr’s corresponding characterization of his own randomness concept, and give a similar, new chararacterization of Kurtz random sequences. That is, we show that an infinite sequence ξ is Kurtz random if and only if for every partial, computable, measure-invariant function Φ : Σ → Σ the sequence Φ(ξ) is not recursive.
17202693
Multiobjective programming using uniform design and genetic algorithm
The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. We design a genetic algorithm for this purpose. We compose multiple fitness functions to guide the search, where each fitness function is equal to a weighted sum of the normalized objective functions and we apply an experimental design method called uniform design to select the weights. As a result, the search directions guided by these fitness functions are scattered uniformly toward the Pareto frontier in the objective space. With multiple fitness functions, we design a selection scheme to maintain a good and diverse population. In addition, we apply the uniform design to generate a good initial population and design a new crossover operator for searching the Pareto-optimal solutions. The numerical results demonstrate that the proposed algorithm can find the Pareto-optimal solutions scattered uniformly over the Pareto frontier.
14047084
Efficient Algorithm for Embedding Hypergraphs in a Cycle
The problem of Minimum Congestion Hypergraph Embedding in a Cycle (MCHEC) is to embed the hyperedges of a hypergraph as paths in a cycle such that the maximum congestion (the maximum number of paths that use any single link in the cycle) is minimized. This problem has many applications, including minimizing communication congestions in computer networks and parallel computations. The MCHEC problem is NP-hard. We give a 1.8-approximation algorithm for the problem. This improves the previous 2-approximation results. The algorithm has the optimal O(mn) time for the hypergraph with m hyperedges and n nodes.
6932570
3D Surface Matching and Recognition Using Conformal Geometry
3D surface matching is a fundamental issue in computer vision with many applications such as shape registration, 3D object recognition and classification. However, surface matching with noise, occlusion and clutter is a challenging problem. In this paper, we analyze a family of conformal geometric maps including harmonic maps, conformal maps and least squares conformal maps with regards to 3D surface matching. As a result, we propose a novel and computationally efficient surface matching framework that uses least squares conformal maps. According to conformal geometry theory, each 3D surface with disk topology can be mapped to a 2D domain through a global optimization and the resulting map is a diffeomorphism, i.e., one-to-one and onto. This allows us to simplify the 3D surface-matching problem to a 2D image-matching problem, by comparing the resulting 2D conformal geometric maps, which are stable, insensitive to resolution changes and robust to occlusion and noise. Therefore, highly accurate and efficient 3D surface matching algorithms can be achieved by using conformal geometric maps. Finally, the performance of conformal geometric maps is evaluated and analyzed comprehensively in 3D surface matching with occlusion, noise and resolution variation. We also provide a series of experiments on real 3D face data that achieve high recognition rates.