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Thesis (M. Sc.)--University of Alberta, 2009. / Title from pdf file main screen (viewed on Sept. 10, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Mining Engineering, Department of Civil and Environmental Engineering, University of Alberta." Includes bibliographical references.

The Support Vector Machine (SVM) classification method has recently gained popularity due to the ease of implementing non-linear separating surfaces. SVM is an optimization problem with the two competing goals, minimizing misclassification on training data and maximizing a margin defined by the normal vector of a learned separating surface. We develop and implement new SVM models based on previously conceived SVM with L_1-Norm regularization with ramp loss error terms. The goal being a new SVM model that is both robust to outliers due to ramp loss, while also easy to implement in open source and off the shelf mathematical programming solvers and relatively efficient in finding solutions due to the mixed linear-integer form of the model. To show the effectiveness of the models we compare results of ramp loss SVM with L_1-Norm and L_2-Norm regularization on human organ microbial data and simulated data sets with outliers.

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Vijay K. Shanker, Dept. of Computer & Information Sciences. Includes bibliographical references.

After that, strip sum and orthogonal Haar transform are proposed. The sum of pixels in a rectangle can be computed by one addition using the strip sum. Then this thesis proposes to use the orthogonal Haar transform (OHT) for pattern matching. Applied for pattern matching, the fast OHT algorithm using strip sum requires O(log u) additions per pixel to project input data of size N1 x N2 onto u 2-D OHT bases. Experimental results show the efficiency of pattern matching using OHT. / Firstly, this thesis proposes a fast algorithm for Walsh Hadamard Transform (WHT) on sliding windows which can be used to implement pattern matching efficiently. / Support vector machine (SVM) is a widely used classification approach. Direct computation of SVM is not desirable in applications requiring computationally efficient classification. To relieve the burden of high computational time required for computing SVM, this thesis proposes a transform domain SVM (TDSVM) using pruning that computes SVM much faster. Experimental results show the efficiency in applying the proposed method for human detection. / Then this thesis analyzes and compares state-of-the-art algorithms for full search equivalent pattern matching. Inspired by the analysis, this thesis develops a new family of transforms called the Kronecker-Hadamard Transform (KHT) of which the GCK family is a subset and WHT is a member. Thus, KHT provides more choices of transforms for representing images. Then this thesis proposes a new fast algorithm that is more efficient than the GCK algorithm. All KHTs can be computed efficiently using the fast KHT algorithm. Based on the KHT, this thesis then proposes the segmented KHT (SegKHT). By segmenting input data into Ls parts, the SegKHT requires 1/Ls the computation required by the KHT algorithm in computing basis vectors. Experimental results show that the proposed algorithm can significantly accelerate the pattern matching process and outperforms state-of-the-art methods. / This thesis aims at improving the computational efficiency in pattern matching. / Ouyang, Wanli. / Adviser: Wai Kuen Cham. / Source: Dissertation Abstracts International, Volume: 73-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 143-147). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

<p>Face detection is a preprocessing step for face recognition algorithms. It is the localization of face/faces in an image or image sequence. Once the face(s) are localized, other computer vision algorithms such as face recognition, image compression, camera auto focusing etc are </p><p>applied. Because of the multiple usage areas, there are many research efforts in face processing. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. To build fully </p><p>automated systems, robust and efficient face detection algorithms are required. </p><p> </p><p>Numerous techniques have been developed to detect faces in a single image; in this project we have used a classification-based face detection method using Gabor filter features. We have designed five frequencies corresponding to eight orientations channels for extracting facial features from local images. The feature vector based on Gabor filter is used as the input of the face/non-face classifier, which is a Support Vector Machine (SVM) on a reduced feature </p><p>subspace extracted by using principal component analysis (PCA). </p><p> </p><p>Experimental results show promising performance especially on single face images where 78% accuracy is achieved with 0 false acceptances.</p>

Face detection is a preprocessing step for face recognition algorithms. It is the localization of face/faces in an image or image sequence. Once the face(s) are localized, other computer vision algorithms such as face recognition, image compression, camera auto focusing etc are applied. Because of the multiple usage areas, there are many research efforts in face processing. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. To build fully automated systems, robust and efficient face detection algorithms are required. Numerous techniques have been developed to detect faces in a single image; in this project we have used a classification-based face detection method using Gabor filter features. We have designed five frequencies corresponding to eight orientations channels for extracting facial features from local images. The feature vector based on Gabor filter is used as the input of the face/non-face classifier, which is a Support Vector Machine (SVM) on a reduced feature subspace extracted by using principal component analysis (PCA). Experimental results show promising performance especially on single face images where 78% accuracy is achieved with 0 false acceptances.

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

This thesis proposes an approach to segmenting and identifying mixed-language speech. Automatic LID can be divided into four steps, feature extraction, segmentation, segment clustering, and re-labeling. In feature extraction, we compare the group delay feature (GDF) with MFCC feature. Unlike the traditional feature from Fourier trans-form magnitude, GDF uses the phase spectrum. In segmentation, we compare delta Bayesian information criterion (delta-BIC) with support vector machines (SVMs). A delta-BIC is applied to segment the input speech utterance into a sequence of lan-guage-dependent segments using acoustic features. The segments are clustered using the K-means algorithm. Finally, re-labeling is used to determine the language of the clusters. SVMs proceed to segment and identify automatically after model training. Considering the effect of the accent issue, we use the corpus English Across Taiwan (EAT) to perform our system. The experimental results show that the system can reach 78.13% in the frame hit rate under the baseline 57.77%.

A Thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy, 2017 / The utility of lectures is directly related to the engagement of the students therein. To ensure the value of lectures, one needs to be certain that they are engaging to students. In small classes experienced lecturers develop an intuition of how engaged the class is as a whole and can then react appropriately to remedy the situation through various strategies such as breaks or changes in style, pace and content. As both the number of students and size of the venue grow, this type of contingent teaching becomes increasingly difficult and less precise. Furthermore, relying on intuition alone gives no way to recall and analyse previous classes or to objectively investigate trends over time. To address these problems this thesis presents the WITS INTELLIGENT TEACHING SYSTEM (WITS) to highlight disengaged students during class. A web-based, mobile application called Engage was developed to try elicit anonymous engagement information directly from students. The majority of students were unwilling or unable to self-report their engagement levels during class. This stems from a number of cultural and practical issues related to social display rules, unreliable internet connections, data costs, and distractions. This result highlights the need for a non-intrusive system that does not require the active participation of students. A nonintrusive, computer vision and machine learning based approach is therefore proposed. To support the development thereof, a labelled video dataset of students was built by recording a number of first year lectures. Students were labelled across a number of affects – including boredom, frustration, confusion, and fatigue – but poor inter-rater reliability meant that these labels could not be used as ground truth. Based on manual coding methods identified in the literature, a number of actions, gestures, and postures were identified as proxies of behavioural engagement. These proxies are then used in an observational checklist to mark students as engaged or not. A Support Vector Machine (SVM) was trained on Histograms of Oriented Gradients (HOG) to classify the students based on the identified behaviours. The results suggest a high temporal correlation of a single subject’s video frames. This leads to extremely high accuracies on seen subjects. However, this approach generalised poorly to unseen subjects and more careful feature engineering is required. The use of Convolutional Neural Networks (CNNs) improved the classification accuracy substantially, both over a single subject and when generalising to unseen subjects. While more computationally expensive than the SVM, the CNN approach lends itself to parallelism using Graphics Processing Units (GPUs). With GPU hardware acceleration, the system is able to run in near real-time and with further optimisations a real-time classifier is feasible. The classifier provides engagement values, which can be displayed to the lecturer live during class. This information is displayed as an Interest Map which highlights spatial areas of disengagement. The lecturer can then make informed decisions about how to progress with the class, what teaching styles to employ, and on which students to focus. An Interest Map was presented to lecturers and professors at the University of the Witwatersrand yielding 131 responses. The vast majority of respondents indicated that they would like to receive live engagement feedback during class, that they found the Interest Map an intuitive visualisation tool, and that they would be interested in using such technology. Contributions of this thesis include the development of a labelled video dataset; the development of a web based system to allow students to self-report engagement; the development of cross-platform, open-source software for spatial, action and affect labelling; the application of Histogram of Oriented Gradient based Support Vector Machines, and Deep Convolutional Neural Networks to classify this data; the development of an Interest Map to intuitively display engagement information to presenters; and finally an analysis of acceptance of such a system by educators. / XL2017