Salient Local 3D Features for 3D Shape Retrieval

In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized model and calculate invariant 3D local feature descriptors at these keypoints. We then use the bag of words approach on the 3D local features to represent the 3D models for shape retrieval. The advantages of the method are that it can be applied to rigid as well as to articulated and deformable 3D models. Finally, this approach is applied for 3D Shape Retrieval on the McGill articulated shape benchmark and then the retrieval results are presented and compared to other methods.Comment: Three-Dimensional Imaging, Interaction, and Measurement. Edited by Beraldin, J. Angelo; Cheok, Geraldine S.; McCarthy, Michael B.; Neuschaefer-Rube, Ulrich; Baskurt, Atilla M.; McDowall, Ian E.; Dolinsky, Margaret. Proceedings of the SPIE, Volume 7864, pp. 78640S-78640S-8 (2011). Conference Location: San Francisco Airport, California, USA ISBN: 9780819484017 Date: 10 March 201

An Analysis of Sindhi Annotated Corpus using Supervised Machine Learning Methods

The linguistic corpus of Sindhi language is significant for computational linguistics process, machine learning process, language features identification and analysis, semantic and sentiment analysis, information retrieval and so on. There is little computational linguistics work done on Sindhi text whereas, English, Arabic, Urdu and some other languages are fully resourced computationally. The grammar and morphemes of these languages are analyzed properly using dissimilar machine learning methods. The development and research work regarding computational linguistics are in progress on Sindhi language at this time. This study is planned to develop the Sindhi annotated corpus using universal POS (Part of Speech) tag set and Sindhi POS tag set for the purpose of language features and variation analysis. The features are extracted using TF-IDF (Term Frequency and Inverse Document Frequency) technique. The supervised machine learning model is developed to assess the annotated corpus to know the grammatical annotation of Sindhi language. The model is trained with 80% of annotated corpus and tested with 20% of test set. The cross-validation technique with 10-folds is utilized to evaluate and validate the model. The results of model show the better performance of model as well as confirm the proper annotation to Sindhi corpus. This study described a number of research gaps to work more on topic modeling, language variation, sentiment and semantic analysis of Sindhi language

Orientation intégrale multi-échelle

This thesis examines and proposes a new set of image region descriptors based on the multiscale localized orientation histograms. The orientation histograms have been proven useful in many image analysis tasks and they have been quite extensively studied in the literature. In this these we have proposed multiscale integral orientations which are a fast multiscale version of the localized orientation histograms. We have also tested the MSIO descriptor on many different image analysis problems and shown the accuracy of this descriptor. In the case of the document image analysis we have tackled with problem of word spotting and writer classification as the test problems for the MSIO descriptor. The results have shown that the feature provide good results and warrant an excellent study on other problems related in the domain of the document image analysis. In the domain of natural image analysis we have tackled the problems of face detection and recognition using the MSIO descriptor. The results there also show the effectiveness of this descriptor. lastly, we have tested the MSIO descriptor to match 3D models based on their shape similarity with an accuracy of 95%. The results have shown the promising aspect of the MSIO descriptor and we hope to use these descriptors for more work in the image analysis domain.Cette thèse porte sur l’analyse d’un nouveau descripteur d’images basé sur la caractérisation de régions à partir d’histogrammes d’orientation multiéchelles. Les histogrammes d’orientation se sont révélés très utiles pour la caractérisation des images dans de nombreuses tâches d’analyse et sont largement étudiés dans la littérature. Nous proposons, dans cette étude, de revisiter cette caractérisation par histogrammes en proposant une version multiéchelle calculée par l’intermédiaire d’images intégrales, afin d’optimiser le coût de calcul sur différentes régions de tailles variables. Ce descripteur basé sur les orientations intégrales multiéchelles, ou MultiScale Integral Orientations (MSIO) a été testé sur différents types de problèmes d’analyse d’images pour mettre en évidence ses caractéristiques et monter son intérêt. Nous avons donc appliqué les MSIO dans le domaine de l’analyse des documents manuscrits, et nous nous sommes plus particulièrement intéressés à la caractérisation de scripteurs et a la recherche de mots (ou Word Spotting) à des fins d’indexation et de recherche par le contenu. Les résultats obtenus ont montré une très bonne capacité de caractérisation de ces formes particulières engendrées par les groupements de caractères et laissent penser que de bons résultats pourraient être obtenus pour d’autres problèmes connexes dans le domaine de l’analyse d’images de documents. Dans le domaine de l’analyse d’images naturelles que nous avons abordé celui de la détection et la reconnaissance de visages en utilisant le descripteur MSIO. Les résultats montrent l à aussi l’efficacité de ce descripteur. Enfin, nous avons utilisé le descripteur MSIO pour la mise en correspondance de modèles 3D à partir de leurs similitudes de forme. Les résultats obtenus sur nos bases de tests ont montré l à encore l’aspect prometteur du descripteur MSIO. Nous continuons à diversifier l’utilisation de ce descripteur à d’autres domaines de l’analyse d’image

Unicode-8 based linguistics data set of annotated Sindhi text

Sindhi Unicode-8 based linguistics data set is multi-class and multi-featured data set. It is developed to solve the natural languages processing (NLP) and linguistics problems of Sindhi language. The data set presents information on grammatical and morphological structure of Sindhi language text as well as sentiment polarity of Sindhi lexicons. Therefore, data set may be used for information retrieving, machine translation, lexicon analysis, language modeling analysis, grammatical and morphological analysis, Semantic and sentiment analysis. Keywords: Sindhi, NLP, Computational linguistics, Morphology, Lexicon, Datase

Syntactic parsing and supervised analysis of Sindhi text

This research study addresses the morphological and syntactic problems of Sindhi language text by proposing an Algorithm for tokenization and syntactic parsing. A Sindhi parser is developed on basis of proposed algorithm to perform syntactic parsing on Sindhi text using Sindhi WordNet (SWN) and corpus. Results of Sindhi syntactic parsing are accumulated to develop multi-class and multi-feature based Sindhi dataset in CSV format. Three attributes of Sindhi dataset are labelled as class. All three classes are comprised with different number of categories. SVM, Random forest and K-NN supervised machine learning methods are used and trained to analyze and evaluate the Sindhi dataset. 80% of dataset is used as training set and 20% of dataset is used as test set. In this research study, 10-fold cross validation technique is applied to evaluate and validate the supervised machine learning process. The SVM classifier gives better results on class phrase and UPOS whereas Random forest gives better result on class TagStatus. Precision, recall, f-measure and confusion matrix approve the performance of all supervised classifiers. The better performance of supervised machine learning methods, support the Sindhi dataset and Sindhi online parser for future research. This study opens new doors for research on right hand written languages especially Sindhi language to solve its computational linguistics problems. Keywords: Sindhi parser, Sindhi WordNet, NLP, Tokenization, Machine learning, Supervised mode

Visual based 3D CAD Retrieval using Fourier Mellin Transform

International audienceOne of most widely used method for image analysis, reconstruction and retrieval is Fourier Mellin Transform (FMT) which is also invariant to similarity transformation. In this paper, we have introduced FMT to the field of 3D shape retrieval. The whole procedure includes three steps: 1) generate silhouettes along the six principle directions for each 3D model; 2) compute a collection of FMT coefficients for all the silhouettes, which are translation, scale, and rotation invariant; and 3) compute a match measure between the query coefficients collection and those in the 3D shape repositories. Our experimental results validate the effectiveness of our approach

3D Retrieval and Classification for structural Bio-Informatics with Implications to Biological Image databases

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Map Quality Assessment

International audienceThe maps generated by robots in real environment are usually incomplete, distorted, and noisy. The map quality is a quantitative performance measure of a robot's understanding of its environment. Map quality also helps researcher study the effects of different mapping algorithms and hardware components used. In this paper we present an algorithm to assess the quality of the map generated by the robot in terms of a ground truth map. To do that, First, localized features are calculated on the pre-evaluated map. Second, nearest neighbor of each valid local feature is searched between the map and the ground truth map. The quality of the map is defined according to the number of the features having the correspondence in the ground truth map. Three feature detectors are tested in terms of their effectiveness, these are the Harris corner detector, Hough Transform and Scale Invariant Feature Transform

3D shape searching using object partitioning

International audienceIn this paper we propose a novel algorithm for 3D shape searching based on the visual similarity by cutting the object into sections. This method rectify some of the shortcomings of the visual similarity based methods, so that it can better account for concave areas ofan object and parts of the object not visible because of occlusion. As the first step, silhouettes of the 3D object are generated by partitioning the object into number of parts with cutting planes perpendicular to the view direction. Then Zernike moments are applied on the silhouettes to generate shape descriptors. The distance measure is based on minimizing the distance among all the combinations of shape descriptors and then these distances are used for similarity based searching. We have performed experiments on the Princeton shapebenchmark and the Purdue CAD/CAM database, and have achieved results comparable to some of the best algorithms in the 3D shape searching literature