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This thesis proposes a novel multiple traffic light recognition system based on videos captured by a monocular camera. Advanced driver assistance system (ADAS) and autonomous driving system (ADS) are becoming increasingly important to help drivers maneuvering vehicles and increase the vehicle and road safety in modern life. Traffic light recognition system is a significant part of ADAS and ADS, which can detect traffic light on the road and recognize different types of traffic lights to provide useful signal information for drivers. The proposed method can be applied to real complex environment only based on a monocular camera and is tested in real-world scenarios. This system consists of three parts: multiple traffic light detection, multi-target tracking and state classification. For the first step, a supervised machine learning method, support vector machine (SVM) with two integral features - histogram of oriented gradients (HOG) and histogram of CIELAB color space (HCIELAB), are used to detect traffic lights in the captured image. Then, a new multi-target tracking algorithm is presented to improve the accuracy of detection, reduce the number of false alarm and missing targets, by means of nearest neighbor data association, motion model analysis and Lucas-Kanade optical flow tracking and the region of interest (ROI) prediction. Finally, a SVM-based and a convolution neural network (CNN) based classifiers are introduced to classify the state of traffic lights, that provides the stop, go, warning, straight and turn information. Various experiments have been conducted to demonstrate the practicability of the proposed method. Both GPU-based and CPU-based programming can run real-time on the real street environment. / Thesis / Master of Applied Science (MASc)

Three experiences investigated auditory-visual interaction and the role of a visual fixation point in tachistoscopic word recognition. The results showed that when a 60db. or 90 db. tone preceded the presentation of the word by two, four, or eight seconds, different effects on the recognition threshold were obtained only for the two-second interval. In this case, the 90 db. group had significantly higher thresholds than did the 60db. group. Other results showed that a 60db. tone facilitated recognition to the same extent as a fixation point. It was concluded that a tone of moderate intensity and occurring a brief interval before the presentation of the word facilitate word recognition, whereas a more intense tone produces a disruptive effect. / Thesis / Master of Arts (MA)

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A wearable device that accurately records a user's daily activities is of substantial value. It can be used to enhance medical monitoring by maintaining a diary that lists what a person was doing and for how long. The design of a wearable system to record context such as activity recognition is influenced by a combination of variables. A flexible yet systematic approach for building a software classification environment according to a set of variables is described. The integral part of the software design is the use of a unique robust classifier that uses principal component analysis (PCA) through singular value decomposition (SVD) to perform real-time activity recognition. The thesis describes the different facets of the SVD-based approach and how the classifier inputs can be modified to better differentiate between activities. This thesis presents the design and implementation of a classification environment used to perform activity detection for a wearable e-textile system. / Master of Science

The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.

No description available.

Face recognition involves a number of complex cognitive processes, including memory, executive functioning, and perception. A breakdown of one or more of these processes may result in false facial recognition, a memory distortion in which one mistakenly believes that novel faces are familiar. This study examined the cognitive mechanisms underlying false facial recognition in healthy older and younger adults, patients with frontotemporal dementia, and individuals with congenital prosopagnosia. Participants completed face recognition memory tests that included several different types of lures, as well as tests of face perception. Older adults demonstrated a familiarity-based response strategy, reflecting a deficit in source monitoring and impaired recollection of context, as they could not reliably discriminate between study faces and highly familiar lures. In patients with frontotemporal dementia, temporal lobe atrophy alone was associated with a reduction of true facial recognition, while concurrent frontal lobe damage was associated with increased false recognition, a liberal response bias, and an overreliance on "gist" memory when making recognition decisions. Individuals with congenital prosopagnosia demonstrated deficits in configural processing of faces and a reliance on feature-based processing, leading to false recognition of lures that had features in common from study to test. These findings may have important implications for the development of training programs that could serve to help individuals improve their ability to accurately recognize faces.

The goal of human action recognition on videos is to determine in an automatic way what is happening in a video. This work focuses on providing an answer to this question: given consecutive frames from a video where a person or persons are doing an action, is an automatic system able to recognize the action that is going on for each person? Seven approaches have been provided, most of them based on an alignment process in order to find a measure of distance or similarity for obtaining the classification. Some are based on fluents that are converted to qualitative sequences of Allen relations to make it possible to measure the distance between the pair of sequences by aligning them. The fluents are generated in various ways: representation based on feature extraction of human pose propositions in just an image or a small sequence of images, changes of time series mainly on the angle of slope, changes of the time series focus on the slope direction, and propositions based on symbolic sequences generated by SAX. Another approach based on alignment corresponds to Dynamic Time Warping on subsets of highly dependent parts of the body. An additional approach explored is based on SAX symbolic sequences and respective pair wise alignment. The last approach is based on discretization of the multivariate time series, but instead of alignment, a spectrum kernel and SVM are used as is employed to classify protein sequences in biology. Finally, a sliding window method is used to recognize the actions along the video. These approaches were tested on three datasets derived from RGB-D cameras (e.g., Microsoft Kinect) as well as ordinary video, and a selection of the approaches was compared to the results of other researchers.