Le sujet de la thèse est l'extraction et la segmentation des vêtements à partir d'images en utilisant des techniques de la vision par ordinateur, de l'apprentissage par ordinateur et de la description d'image, pour la recommandation de manière non intrusive aux utilisateurs des produits similaires provenant d'une base de données de vente. Nous proposons tout d'abord un extracteur d'objets dédié à la segmentation de la robe en combinant les informations locales avec un apprentissage préalable. Un détecteur de personne localises des sites dans l'image qui est probable de contenir l'objet. Ensuite, un processus d'apprentissage intra-image en deux étapes est est développé pour séparer les pixels de l'objet de fond. L'objet est finalement segmenté en utilisant un algorithme de contour actif qui prend en compte la segmentation précédente et injecte des connaissances spécifiques sur la courbure locale dans la fonction énergie. Nous proposons ensuite un nouveau framework pour l'extraction des vêtements généraux en utilisant une procédure d'ajustement globale et locale à trois étapes. Un ensemble de modèles initialises un processus d'extraction d'objet par un alignement global du modèle, suivi d'une recherche locale en minimisant une mesure de l'inadéquation par rapport aux limites potentielles dans le voisinage. Les résultats fournis par chaque modèle sont agrégés, mesuré par un critère d'ajustement globale, pour choisir la segmentation finale. Dans notre dernier travail, nous étendons la sortie d'un réseau de neurones Fully Convolutional Network pour inférer le contexte à partir d'unités locales (superpixels). Pour ce faire, nous optimisons une fonction énergie, qui combine la structure à grande échelle de l'image avec le local structure superpixels, en recherchant dans l'espace de toutes les possibilité d'étiquetage. De plus, nous introduisons une nouvelle base de données RichPicture, constituée de 1000 images pour l'extraction de vêtements à partir d'images de mode. Les méthodes sont validées sur la base de données publiques et se comparent favorablement aux autres méthodes selon toutes les mesures de performance considérées. / The topic of the thesis is the extraction and segmentation of clothing items from still images using techniques from computer vision, machine learning and image description, in view of suggesting non intrusively to the users similar items from a database of retail products. We firstly propose a dedicated object extractor for dress segmentation by combining local information with a prior learning. A person detector is applied to localize sites in the image that are likely to contain the object. Then, an intra-image two-stage learning process is developed to roughly separate foreground pixels from the background. Finally, the object is finely segmented by employing an active contour algorithm that takes into account the previous segmentation and injects specific knowledge about local curvature in the energy function.We then propose a new framework for extracting general deformable clothing items by using a three stage global-local fitting procedure. A set of template initiates an object extraction process by a global alignment of the model, followed by a local search minimizing a measure of the misfit with respect to the potential boundaries in the neighborhood. The results provided by each template are aggregated, with a global fitting criterion, to obtain the final segmentation.In our latest work, we extend the output of a Fully Convolution Neural Network to infer context from local units(superpixels). To achieve this we optimize an energy function,that combines the large scale structure of the image with the locallow-level visual descriptions of superpixels, over the space of all possiblepixel labellings. In addition, we introduce a novel dataset called RichPicture, consisting of 1000 images for clothing extraction from fashion images.The methods are validated on the public database and compares favorably to the other methods according to all the performance measures considered.

This work is trying to propose Diarization System based on Bayesian Information Criterion (BIC). In this paper is possible to find description of background theory and short description of previously used systems. Idea of this work is to try to use methods proposed earlier in a faster and more reliable way. Proposed system was tested on some records to prove its error rate. Results of tests are not very good but some possible improvements are proposed.

No description available.

No description available.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

For many years, the Virginia Department of Game and Inland Fisheries (VDGIF) has managed freshwater fisheries without fully understanding their stakeholders. To increase its knowledge and improve management, the VDGIF commissioned a market segmentation study to collect baseline information about its constituents and serve as a model for future studies. I developed a 16-page mail questionnaire that was sent to a stratified random sample of 5,378 Virginia resident freshwater fishing license holders. The questionnaire was use to collect information on characteristics, motivations, attitudes, and preferences of Virginia resident freshwater anglers. The response rate was 52%. I examined the descriptive characteristics of resident freshwater anglers and anglers who purchased different types of licenses and anglers from different management regions. Differences in fishing behaviors, motivations for fishing, attitudes, and preferences for management existed among anglers based on license type and regions. Although satisfaction with freshwater fishing was high, in most cases, many anglers believed that fishing quality had declined. By adopting a marketing approach and providing the desired experiences to each segment of anglers, the Fisheries Division may improve its relationship with anglers, as well as increase participation and satisfaction. I also segmented the Virginia anglers by species preference, specialization, and a multi-level approach that involved a combination of species preference and specialization. Anglers are not a homogenous group and they seek different experiences. Multi-level segmentation was the most useful method of segmentation because it identified within-species preference group differences. Within each species preference group I found several segments of anglers. Segments differed in their orientations (trophy or consumptive), preferred methods of fishing and information sources, and support for regulations. Specialist anglers from each species preference group were trophy oriented and some were consumptive oriented as well. Specialists also were the most supportive of restrictive regulations. Less specialized anglers in each species preference group generally were less trophy oriented, more consumptive, and less supportive of regulations than specialist anglers. My results provide better understanding of the different segments of anglers within each species preference group, which will allow managers to provide a more satisfying experience for their stakeholders. / Master of Science

Many tasks of visual computing and communications such as object recognition, matting, compression, etc., need to extract and encode the outer boundary of the object in a digital image or video. In this thesis, we focus on a particular video segmentation task and propose an efficient method for head-and-shoulder of humans through video frames. The key innovations for our work are as follows: (1) a novel head descriptor in polar coordinate is proposed, which can characterize intrinsic head object well and make it easy for computer to process, classify and recognize. (2) a learning-based method is proposed to provide highly precise and robust head-and-shoulder segmentation results in applications where the head-and-shoulder object in the question is a known prior and the background is too complex. The efficacy of our method is demonstrated on a number of challenging experiments. / Thesis / Master of Applied Science (MASc)

Due to the rapid progress and advancements in deep learning and neural networks, manyapproaches and state-of-the-art researches have been conducted in these fields which cause developing various learning-based attacks leading to vulnerability of websites and portals. This kind of attacks decrease the security of the websites which results in releasing the sensitive and important personal information. These days, preserving the security of the websites is one of the most challenging tasks. CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) is kind of test which are developed by designers and are available in various websites to distinguish and differentiate humans from robots in order to protect the websites from possible attacks. In this dissertation, we proposed a CNN based approach to attack and break text-based CAPTCHAs. The proposed method has been compared with several state-of-the-art approaches in terms of recognition accuracy (RA). Based on the results, the developed method can break and recognize CAPTCHAs at high accuracy. Additionally, we wanted to check how to make these CAPTCHAs hard to be broken, so we employed five types of distortions in these CAPTCHAs. The recognition accuracy in presence of these noises has been calculated. The results indicate that adversarial noise can make CAPTCHAs much difficult to be broken. The results have been compared with some state-of-the-art approaches. This analysis can be helpful for CAPTCHA developers to consider these noises in their developed CAPTCHAs. This dissertation also presents a hybrid model based on CNN-SVM to solve text-based CAPTCHAs. The developed method contains four main steps, namely: segmentation, feature extraction, feature selection, and recognition. For segmentation, we suggested using histogram and k-means clustering. For feature extraction, we developed a new CNN structure. The extracted features are passed through the mRMR algorithm to select the most efficient features. These selected features are fed into SVM for further classification and recognition. The results have been compared with several state-of-the-art methods to show the superiority of the developed approach. In general, this dissertation presented deep learning-based methods to solve text-based CAPTCHAs. The efficiency and effectiveness of the developed methods have been compared with various state-of-the-art methods. The developed techniques can break CAPTCHAs at high accuracy and also in a short time. We utilized Peak Signal to Noise Ratio (PSNR), ROC, accuracy, sensitivity, specificity, and precision to evaluate and measure the performance analysis of different methods. The results indicate the superiority of the developed methods.

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No description available.