Dmitriy Bespalov

REEB GRAPH BASED SHAPE RETRIEVAL FOR CAD

Our recent work has described a framework for matching solid of mechanical artifacts models based on scale-space feature decomposition. In this work we adopt a method of comparing solid models based on Multiresolutional Reeb Graphs (MRG) similarity computations. This method was originally proposed by Hilaga et al. in [1]. Reeb Graph technique applies MRG structure to comparisons of approximate models found in the graphics community, such as polygonal meshes, faceted representation and Virtual Reality Modeling Language (VRML) models. First, we provide a brief review of shape matching using Multiresolutional Reeb Graphs and present an approach to matching solid models. Second, we show the performance of the Reeb Graph technique when handling primitive CAD models, such as cubes and spheres; then we perform experiments with more complex models, such as LEGO models and mechanical parts, and we discuss Reeb Graph technique’s performance on complex CAD models. Third, we emphasize several problems with the existing technique. Finally, we conclude with discussion of future work.

Sentiment classification based on supervised latent n-gram analysis

In this paper, we propose an efficient embedding for modeling higher-order (n-gram) phrases that projects the n-grams to low-dimensional latent semantic space, where a classification function can be defined. We utilize a deep neural network to build a unified discriminative framework that allows for estimating the parameters of the latent space as well as the classification function with a bias for the target classification task at hand. We apply the framework to large-scale sentimental classification task. We present comparative evaluation of the proposed method on two (large) benchmark data sets for online product reviews. The proposed method achieves superior performance in comparison to the state of the art.

Local feature extraction and matching partial objects

Abstract A primary shortcoming of existing techniques for hree-dimensional (3D) model matching is the reliance on global information of the model’s structure. Models are matched in their entirety, depending on overall topology and geometry information. A currently open challenge is how to perform partial matching. Partial matching is important for finding similarities across part models with different global shape properties and for the segmentation and matching of data acquired from 3D scanners. This paper presents a Scale–Space feature extraction technique based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results presented in this paper suggest that this technique can potentially be used to perform matching based on local model structure. In our previous work, Scale–Space decomposition has been used successfully to extract features from mechanical artifacts. Scale–Space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these technique is to support matching and content-based retrieval of solid models. This paper shows how a Scale–Space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning process introduce. In order to accomplish this, we introduce a new distance function defined on triangles instead of points. We believe this technique offers a new way to control the feature decomposition process, which results in the extraction of features that are more meaningful from an engineering viewpoint. The new technique is computationally practical for use in indexing large models. Examples are provided that demonstrate effective feature extraction on 3D laser scanned models. In addition, a simple sub-graph isomorphism algorithm was used to show that the feature adjacency graphs, obtained through feature extraction, are meaningful descriptors of 3D CAD objects. All of the data used in the experiments for this work is freely available at: http://www.designrepository.org/datasets/ .

Scale-space representation of 3D models and topological matching

Reeb graphs have been shown to be effective for topology matching of 3D objects. Their effectiveness breaks down, however, when the individual models become very geometrically and topologically detailed---as is the case for complex machined parts. The result is that Reeb graph techniques, as developed for matching general shape and computer graphics models, produce poor results when directly applied to create engineering databases.This paper presents a framework for shape matching through scale-space decomposition of 3D models. The algorithm is based on recent developments in efficient hierarchical decomposition of metric data using its spectral properties. Through spectral decomposition, we reduce the problem of matching to that of computing a mapping and distance measure between vertex-labeled rooted trees. We use a dynamic programming scheme to compute distances between trees corresponding to solid models. Empirical evaluation of the algorithm on an extensive set of 3D matching trials demonstrates both robustness and efficiency of the overall approach.

Benchmarking CAD search techniques

While benchmark datasets have been proposed for testing computer vision and 3D shape retrieval algorithms, no such datasets have yet been put forward to assess the relevance of these techniques for engineering problems. This paper presents several distinctive benchmark datasets for evaluating techniques for automated classification and retrieval of CAD objects. These datasets include (1) a dataset of CAD primitives (such as those common in constructive solid geometry modeling); (2) two datasets consisting of classes generated by minor topological variation; (3) two datasets of industrial CAD models classified based on object function and manufacturing process, respectively; (4) and a dataset of LEGO© models from the Mindstorms© robotics kits. Each model in the datasets is available in three formats - ACIS SAT, ISO STEP, and as a VRML mesh (some models are available under several different fidelity settings). These are all available through the National Design Repository.Using these datasets, we present comprehensive empirical results for nińe (9) different shape and solid model matching and retrieval techniques. These experiments show, as expected, that the quality of precision-recall performance can significantly vary on different datasets. These experiments reveal that for certain object classes and classifications, such as those based on manufacturing processes, all existing techniques perform poorly. This study reveals the strengths and weaknesses of existing research in these areas, introduces open challenge problems, and provides meaningful datasets and metrics against which the success of current and future work can be measured.

Scale-Space Representation and Classification of 3D Models

This paper presents a framework for shape matching and classification through scalespace decomposition of 3D models. The algorithm is based on recent developments in efficient hierarchical decomposition of a point distribution in metric space ~p,d! using its spectral properties. Through spectral decomposition, we reduce the problem of matching to that of computing a mapping and distance measure between vertex-labeled rooted trees. We use a dynamic programming scheme to compute distances between trees corresponding to solid models. Empirical evaluation of the algorithm on an extensive set of 3D matching trials demonstrates both robustness and efficiency of the overall approach. Lastly, a technique for comparing shape matchers and classifiers is introduced and the scale-space method is compared with six other known shape matching algorithms. @DOI: 10.1115/1.1633576 #

Classification of archaeological ceramic fragments using texture and color descriptors

The application of digital technologies to culture history preservation and interpretation is a rapidly growing field that has captured the imagination of many. In this work, we explore the application of image classification systems for use in the reconstruction of archaeologically excavated 18th and 19th-century ceramic fragments. In specific, we investigate the classification of thin-shell ceramics based on color and texture descriptors in order to aid in vessel reconstructions. In addition to using well known SIFT features, we formulate a new feature descriptor based on total variation geometry. The experimental results demonstrate that the proposed descriptor accurately represents texture and that, when categorizing fragments, it is best to utilize both the color and texture information available.

Sentiment classification with supervised sequence embedding

In this paper, we introduce a novel approach for modeling n-grams in a latent space learned from supervised signals. The proposed procedure uses only unigram features to model short phrases (n-grams) in the latent space. The phrases are then combined to form document-level latent representation for a given text, where position of an n-gram in the document is used to compute corresponding combining weight. The resulting two-stage supervised embedding is then coupled with a classifier to form an end-to-end system that we apply to the large-scale sentiment classification task. The proposed model does not require feature selection to retain effective features during pre-processing, and its parameter space grows linearly with size of n-gram. We present comparative evaluations of this method using two large-scale datasets for sentiment classification in online reviews (Amazon and TripAdvisor). The proposed method outperforms standard baselines that rely on bag-of-words representation populated with n-gram features.

Local Feature Extraction Using Scale-Space Decomposition

In our recent work we have introduced a framework for extracting features from solid of mechanical artifacts in polyhedral representation based on scale-space feature decomposition [1]. Our approach used recent developments in efficient hierarchical decomposition of metric data using its spectral properties. In that work, through spectral decomposition, we were able to reduce the problem of matching to that of computing a mapping and distance measure between vertex-labeled rooted trees. This work discusses how Scale-Space decomposition frame-work could be extended to extract features from CAD models in polyhedral representation in terms of surface triangulation. First, we give an overview of the Scale-Space decomposition approach that is used to extract these features. Second, we discuss the performance of the technique used to extract features from CAD data in polyhedral representation. Third, we show the feature extraction process on noisy data — CAD models that were constructed using a 3D scanner. Finally, we conclude with discussion of future work.Copyright

On evaluating the efficiency of software feature development using algebraic manifolds

Managers are often unable to explain objectively why or when effort was misplaced during the development process. In this paper, we present a formal technique to depict the expended effort during the life-cycle of a software feature using feature development manifolds (FDMs). Using the FDMs we can compute the preferred development path for a given feature. This development path includes the versions of a software feature that contributed to the final version of the feature in a positive way. The preferred development path excludes versions of the software feature that should have been skipped. Once the preferred development path is computed the amount of wasted effort can be quantified using the metric that we have developed. We demonstrate the effectiveness of our approach to compute wasted software feature development by applying our technique to two large open source software systems, Gaim and Firefox.