No / Word spotting in continuous speech is considered a challenging issue due to dynamic nature of speech. Literature contains a variety of novel techniques for the isolated word recognition and spotting. Most of these techniques are based on pattern recognition and similarity measures. This paper amalgamates the use of different techniques that includes wavelet transform, feature extraction and Euclidean distance. Based on the acoustic features, the proposed system is capable of identifying and localizing a target (test) word in a continuous speech of any length. Wavelet transform is used for the time-frequency representation and filtration of speech signal. Only high intensity frequency components are passed to feature extraction and matching process resulting robust performance in terms of matching as well as computational cost.

Feature detection, description and matching are crucial steps in many computer vision algorithms. These rely on feature descriptors to be able to match image features across sets of images. This paper discusses a hardware implementation and various optimizations of our lab's previous work on the SYnthetic BAsis feature descriptor (SYBA). Previous work has shown that SYBA can offer superior performance to other binary descriptors, such as BRIEF. This hardware implementation on an FPGA is a high throughput and low latency solution, which is critical for applications such as: high speed object detection and tracking, stereo vision, visual odometry, structure from motion, and optical flow. Finally, we compare our solution to other hardware methods. We believe that our implementation of SYBA as a feature descriptor in hardware offers superior image feature matching performance and uses less resources than most binary feature descriptor implementations.

This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First, a volumetric registration algorithm is developed which solves the registration as a state estimation problem. Using a static deformation model. To reduce computations, the similarity measure is calculated at some specific points called control points. These control points can be from a low resolution image grid or any irregular image grid. Our numerical analysis has shown that control points placed in the area without much information; i.e with small or no changes in image intensity, yield negligible deformation. Therefore, the selection of the control points can significantly impact the accuracy and computation complexity of the registration algorithms. An extension of the speeded up robust features (SURF) to 3D is proposed for enhanced selection of the control points in deformable image registration. The impact of this new control point selection method on the performance of the registration algorithm is analyzed by comparing it to the case where regular grid control points are used. The results show that the number of control points could be reduced by a factor of ten with new selection methodology without sacrificing performance. Second image registration method is proposed in which, based on a segmented pre-operative image, a deformation model of the breast tissue is developed and discretized in the spatial domain using the method of finite elements. The compression of the preoperative image is modeled by applying smooth forces on the surface of the breast where compression plates are placed. Image registration is accomplished by formulating and solving an optimization problem. The cost function is a similarity measure between the deformed preoperative image and intra-operative image computed at some control point and the decision variables are the tissue interaction forces. / Thesis / Master of Applied Science (MASc)

In this thesis, findings in three areas of computer vision estimation are presented. First, an improvement to the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm is presented in which gyroscope data is incorporated to compensate for camera rotation. This improved algorithm is then compared with the original algorithm and shown to be more effective at tracking features in the presence of large rotational motion. Next, a deep neural network approach to depth estimation is presented. Equations are derived relating camera and feature motion to depth. The information necessary for depth estimation is given as inputs to a deep neural network, which is trained to predict depth across an entire scene. This deep neural network approach is shown to be effective at predicting the general structure of a scene. Finally, a method of passively estimating the position and velocity of constant velocity targets using only bearing and time-to-collision measurements is presented. This method is paired with a path planner to avoid tracked targets. Results are given to show the effectiveness of the method at avoiding collision while maneuvering as little as possible.

Typically, computational fluid dynamic (CFD) solutions produce large amounts of data that can be used for analysis. The enormous amount of data produces new challenges for effective exploration. The prototype system EVITA, based on ranked access of application-specific regions of interest, provides an effective tool for this purpose. Automated feature detection techniques are needed to identify the features in the dataset. Automated techniques for detecting shocks, expansion regions, vortices, separation lines, and attachment lines have already been developed. A new approach for identifying the regions of flow separation is proposed. This technique assumes that each pair of separation and attachment lines has a vortex core associated with it. It is based on the velocity field in the plane perpendicular to the vortex core. The present work describes these methods along with the results obtained.

It is a popular practice in the remote-sensing community to apply principal component analysis (PCA) on a higher-dimensional feature space to achieve dimensionality-reduction. Several factors that have led to the popularity of PCA include its simplicity, ease of use, availability as part of popular remote-sensing packages, and optimal nature in terms of mean square error. These advantages have prompted the remote-sensing research community to overlook many limitations of PCA when used as a dimensionality-reduction tool for classification and target-detection applications. This thesis addresses the limitations of PCA when used as a dimensionality-reduction technique for extracting discriminating features from hyperspectral data. Theoretical and experimental analyses are presented to demonstrate that PCA is not necessarily an appropriate feature-extraction method for high-dimensional data when the objective is classification or target-recognition. The influence of certain data-distribution characteristics, such as within-class covariance, between-class covariance, and correlation on PCA transformation, is analyzed in this thesis. The classification accuracies obtained using PCA features are compared to accuracies obtained using other feature-extraction methods like variants of Karhunen-Loève transform and greedy search algorithms on spectral and wavelet domains. Experimental analyses are conducted for both two-class and multi-class cases. The classification accuracies obtained from higher-order PCA components are compared to the classification accuracies of features extracted from different regions of the spectrum. The comparative study done on the classification accuracies that are obtained using above feature-extraction methods, ascertain that PCA may not be an appropriate tool for dimensionality-reduction of certain hyperspectral data-distributions, when the objective is classification or target-recognition.

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