9568443
A computational model for repeated pattern perception using frieze and wallpaper groups
Humans have an innate ability to perceive symmetry, but it is not obvious how to automate this powerful insight. In this paper the mathematical theory of frieze and wallpaper groups is used to extract visually meaningful building blocks (motifs) from a repeated pattern. A novel peak detection algorithm based on "regions of dominance" is used to automatically detect the underlying translational lattice of a repeated pattern. Following automatic classification of the pattern's symmetry group, knowledge of the interplay between rotation, reflection, glide-reflection and translation in that group leads to a small set of candidate motifs that exhibit local symmetry consistent with the global symmetry of the entire pattern. Although other work has addressed detection of the translational lattice of a repeated pattern, ours is the first to seek a principled method for determining a representative motif. Experiments show that the-resulting pattern motifs conform well with human perception.
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.
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.
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.
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.
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.
9255277
A New Pairwise Ensemble Approach for Text Classification
Text classification, whether by topic or genre, is an important task that contributes to text extraction, retrieval, summarization and question answering. In this paper we present a new pairwise ensemble approach, which uses pairwise Support Vector Machine (SVM) classifiers as base classifiers and "input-dependent latent variable" method for model combination. This new approach better captures the characteristics of genre classification, including its heterogeneous nature. Our experiments on two multi-genre collections and one topic-based classification datasets show that the pairwise ensemble method outperforms both boosting, which has been demonstrated as a powerful ensemble approach, and Error-Correcting Output Codes (ECOC), which applies pairwise-like classifiers for multiclass classification problems.
2633029
Principled strength reduction
This paper presents a principled approach for optimizing iterative (or recursive) programs. The approach formulates a loop body as a function f and a change operation ⊕, incrementalizes f with respect to ⊕, and adopts an incrementalized loop body to form a new loop that is more efficient. Three general optimizations are performed as part of the adoption; they systematically handle initializations, termination conditions, and final return values on exits of loops. These optimizations are either omitted, or done in implicit, limited, or ad hoc ways in previous methods.
13208870
An Evolutionary Approach to Adaptive Model-Building
Learning an input-output mapping from a set of examples can be regarded as a problem of model-building. From this point of view, by a hierarchical representation scheme for models, an evolutionary computational approach to model-building problems is proposed in this paper, which is based on the ideas from the evolution programs and the genetic programmings. The computer experiments indicate that it is surprisingly effective in searching the optimal model for model-building problems.
30505871
User Interest Detection on Web Pages for Building Personalized Information Agent
In this paper we propose a vision-based approach to detecting user’s interests on web pages. This approach first splits a web page into several blocks of topics according to visual cues and then analyzes the layout structure of the page based on these blocks. The user’s interests are detected according to his browsing behaviors on the blocks and the layout structure of the web page. A personalized information agent is also developed to automatically extract new content from those web pages according to the user’s interests. Our experiments show that our proposed approach to detecting user’s interests on web pages achieves satisfactory accuracy and the personalized information agent significantly saves user’s browsing time and improves user’s browsing experience.
39241507
Feature selection using a proximity-index optimization model
Abstract One of the recurring issues in pattern recognition problems has been feature extraction and selection. This paper addresses this issue from a different pespective. Without assuming any particular classificaiton algorithm, it first suggests that one extract as much information as conveniently possible in several pattern-information domains. This paper later suggests applying the proposed Proximity-Index method, to select a significantly smaller, yet optimal feature subset. This method is formally described and is successfully applied to a waveform classification problem. The features selected by the algorithm are used to classify ten signal classes and produce a very encouraging recognition performance of 87.00% on 200 samples. This method is computationally inexpensive and particularly useful for large data set problems.