14931870
SVM decision boundary based discriminative subspace induction
We study the problem of linear dimension reduction for classification, with a focus on sufficient dimension reduction, i.e., finding subspaces without loss of discrimination power. First, we formulate the concept of sufficient subspace for classification in parallel terms as for regression. Then we present a new method to estimate the smallest sufficient subspace based on an improvement of decision boundary analysis (DBA). The main idea is to combine DBA with support vector machines (SVM) to overcome the inherent difficulty of DBA in small sample size situations while keeping DBA's estimation simplicity. The compact representation of SVM boundary results in a significant gain in both speed and accuracy over previous DBA implementations. Alternatively, this technique can be viewed as a way to reduce the run-time complexity of SVM itself. Comparative experiments on one simulated and four real-world benchmark datasets highlight the superior performance of the proposed approach.
1236601
Skewed symmetry groups
We introduce the term skewed symmetry groups and provide a complete theoretical treatment for 2D wallpaper groups under affine transformations. For the first time, a given periodic pattern can be classified not simply by its Euclidean symmetry group but,by its highest "potential" symmetry group under affine deformation. A concise wallpaper group migration map is constructed that separates the 17 affinely deformed wallpaper groups into small, distinct orbits. The practical value of this result includes a novel indexing and retrieval scheme for regular patterns, and a maximal-symmetry-based method for estimating shape and orientation from texture under unknown views.
122978488
Representation and matching of articulated shapes
We consider the problem of localizing the articulated and deformable shape of a walking person in a single view. We represent the non-rigid 2D body contour by a Bayesian graphical model whose nodes correspond to point positions along the contour. The deformability of the model is constrained by learned priors corresponding to two basic mechanisms: local non-rigid deformation, and rotation motion of the joints. Four types of image cues are combined to relate the model configuration to the observed image, including edge gradient map, foreground/background mask, skin color mask, and appearance consistency constraints. The constructed Bayes network is sparse and chain-like, enabling efficient spatial inference through sequential Monte Carlo sampling methods. We evaluate the performance of the model on images taken in cluttered, outdoor scenes. The utility of each image cue is also empirically explored.
5262963
Texture replacement in real images
Texture replacement in real images has many applications, such as interior design, digital movie making and computer graphics. The goal is to replace some specified texture patterns in an image while preserving lighting effects, shadows and occlusions. To achieve convincing replacement results we have to detect texture patterns and estimate the lighting map from a given image. Near regular planar texture patterns are considered in this paper. Given a sample texture patch, a standard tile is computed. Candidate texture regions are determined by mutual information between the standard tile and each image patch. Regions with high mutual information scores are used to estimate the admissible lighting distributions, which is represented by cached statistics. Spatial lighting change constraints are represented by a Markov random field model. Maximum a posteriori estimation of the texture segmentation and lighting map is solved in a stochastic annealing fashion, namely, the Markov chain Monte Carlo method. Visually satisfactory result is achieved using this statistical sampling model.
1966938
Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images
This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.
14419032
Content-based 3D neuroradiologic image retrieval: preliminary results
A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval. A testbed has been implemented for using detected salient visual features for indexing and retrieving 3D images.
7269331
Quadric Surface Extraction by Variational Shape Approximation
Based on Lloyd iteration, we present a variational method for extracting general quadric surfaces from a 3D mesh surface. This work extends the previous variational methods that extract only planes or special types of quadrics, i.e., spheres and circular cylinders. Instead of using the exact L2 error metric, we use a new approximate L2 error metric to make our method more efficient for computing with general quadrics. Furthermore, a method based on graph cut is proposed to smooth irregular boundary curves between segmented regions, which greatly improves the final results.
23025365
A structural approach to recognizing incomplete graphic objects
We propose a structural approach to recognizing incomplete graphic objects, which aims to recognize a graphic object before the user finishes drawing all its strokes. In our approach, we first create a spatial division tree (SDT) for the input object and then search for similar objects in the database of graphic object models based on this representation. Along with the SDT, an intersection-based relationship model is also proposed in our approach to describe the relationships among strokes. The experiment result has shown that our approach can significantly save user's input time for complex graphic objects. Moreover, it also proves that our approach can response to user's input in real-time and is independent of the user's drawing style.
16444844
Scaling Up Evolutionary Programming Algorithms
Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four different EP algorithms for large-scale problems, i.e., problems whose dimension ranges from 100 to 300. The four are classical EP (CEP) [1, 2], fast EP (FEP).
15077137
Modeling nonlinearity in dilution design microarray data
MOTIVATION
Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC.


RESULTS
We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent.


AVAILABILITY
The R codes which implements all described methods, and some Supplementary material, are freely available from http://www.utdallas.edu/~ying.liu/BetaRatio.htm
2600635
A systematic incrementalization technique and its application to hardware design

Abstract.A systematic transformation method based on incrementalization and value caching generalizes a broad family of program optimizations. It yields significant performance improvements in many program classes, including iterative schemes that characterize hardware specifications. CACHET is an interactive incrementalization tool. Although incrementalization is highly structured and automatable, better results are obtained through interaction, where the main task is to guide term rewriting based on data-specific identities. Incrementalization specialized to iteration corresponds to strength reduction, a familiar program improvement technique. This correspondence is illustrated by the derivation of a hardware-efficient nonrestoring square-root algorithm, which has also served as an example of theorem prover-based implementation verification.