Human face detection and recognition techniques play an important role in applica- tions like face recognition, video surveillance, human computer interface and face image databases. Using color information in images is one of the various possible techniques used for face detection. The novel technique used in this project was the combination of various techniques such as skin color detection, template matching, gradient face de- tection to achieve high accuracy of face detection in frontal faces. The objective in this work was to determine the best rotation angle to achieve optimal detection. Also eye and mouse template matching have been put to test for feature detection.

Feature extraction is an important step in the classification of high-dimensional data such as face images. Furthermore, linear feature extractors are more prevalent due to computational efficiency and preservation of the Gaussianity. This research proposes a simple and fast linear feature extractor approximating the sufficient statistic for Gaussian distributions. This method preserves the discriminatory information in both first and second moments of the data and yields the linear discriminant analysis as a special case. Additionally, an accurate upper bound on the error probability of a plug-in classifier can be used to approximate the number of features minimizing the error probability. Therefore, tighter error bounds are derived in this work based on the Bayes error or the classification error on the trained distributions. These bounds can also be used for performance guarantee and to determine the required number of training samples to guarantee approaching the Bayes classifier performance.

Perceptual grouping is essential to manage the complexity of real world scenes. We explore bottom-up grouping at three different levels. Starting from low-level grouping, we propose a novel method for oversegmenting an image into compact superpixels, reducing the complexity of many high-level tasks. Unlike most low-level segmentation techniques, our geometric flow formulation enables us to impose additional compactness constraints, resulting in a fast method with minimal undersegmentation. Our subsequent work utilizes compact superpixels to detect two important mid-level shape regularities, closure and symmetry. Unlike the majority of closure detection approaches, we transform the closure detection problem into one of finding a subset of superpixels whose collective boundary has strong edge support in the image. Building on superpixels, we define a closure cost which is a ratio of a novel learned boundary gap measure to area, and show how it can be globally minimized to recover a small set of promising shape hypotheses. In our final contribution, motivated by the success of shape skeletons, we recover and group symmetric parts without assuming prior figure-ground segmentation. Further exploiting superpixel compactness, superpixels are this time used as an approximation to deformable maximal discs that comprise a medial axis. A learned measure of affinity between neighboring superpixels and between symmetric parts enables the purely bottom-up recovery of a skeleton-like structure, facilitating indexing and generic object recognition in complex real images.

The productivity of language lies in the ability to generalize linguistic knowledge to new situations. To understand how children can learn to use language in novel, productive ways, we must investigate how children can find the right abstractions over their input, and how these abstractions can actually guide generalization. In this thesis, I present a series of hierarchical Bayesian models that provide an explicit computational account of how children can acquire and generalize highly abstract knowledge of the verb lexicon from the language around them. By applying the models to large, naturalistic corpora of child-directed speech, I show that these models capture key behaviours in child language development. These models offer the power to investigate developmental phenomena with a degree of breadth and realism unavailable in existing computational accounts of verb learning. By most accounts, children rely on strong regularities between form and meaning to help them acquire abstract verb knowledge. Using a token-level clustering model, I show that by attending to simple syntactic features of potential verb arguments in the input, children can acquire abstract representations of verb argument structure that can reasonably distinguish the senses of a highly polysemous verb. I develop a novel hierarchical model that acquires probabilistic representations of verb argument structure, while also acquiring classes of verbs with similar overall patterns of usage. In a simulation of verb learning within a broad, naturalistic context, I show how this abstract, probabilistic knowledge of alternations can be generalized to new verbs to support learning. I augment this verb class model to acquire associations between form and meaning in verb argument structure, and to generalize this knowledge appropriately via the syntactic and semantic aspects of verb alternations. The model captures children's ability to use the alternation pattern of a novel verb to infer aspects of the verb's meaning, and to use the meaning of a novel verb to predict the range of syntactic forms in which the verb may participate. These simulations also provide new predictions of children's linguistic development, emphasizing the value of this model as a useful framework to investigate verb learning in a complex linguistic environment.

Despite the popularity and success of neural networks in research, the number of resulting commercial or industrial applications have been limited. A primary cause of this lack of adoption is due to the fact that neural networks are usually implemented as software running on general-purpose processors. Hence, a hardware implementation that can take advantage of the inherent parallelism in neural networks is desired. This thesis investigates how the Restricted Boltzmann machine, a popular type of neural network, can be effectively mapped to a high-performance hardware architecture on FPGA platforms. The proposed, modular framework is designed to reduce the time complexity of the computations through heavily customized hardware engines. The framework is tested on a platform of four Xilinx Virtex II-Pro XC2VP70 FPGAs running at 100MHz through a variety of different configurations. The maximum performance was obtained by instantiating a Restricted Boltzmann Machine of 256x256 nodes distributed across four FPGAs, which results in a computational speed of 3.13 billion connection-updates-per-second and a speed-up of 145-fold over an optimized C program running on a 2.8GHz Intel processor.

Currently, online advertisements are created for specific dimensions and must be laboriously modified by advertisers to support different aspect ratios. In addition, publishers are constrained to design web pages to accommodate this limited set of sizes. As an alternative we present a framework for automatically generating visual banners at arbitrary sizes based on individual prototype ads. This technique can be used to create flexible visual ads that can be resized to accommodate various aspect ratios. In the proposed framework image and text data are stored separately. Resizing involves selecting a sub-region of the original image and updating text parameters (size and position). This problem is posed within an optimization framework that encourages solutions which maintain important structural properties of the original ad. The method can be applied to advertisements containing a wide variety of imagery and provides significantly more flexibility than existing solutions.

In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

The productivity of language lies in the ability to generalize linguistic knowledge to new situations. To understand how children can learn to use language in novel, productive ways, we must investigate how children can find the right abstractions over their input, and how these abstractions can actually guide generalization. In this thesis, I present a series of hierarchical Bayesian models that provide an explicit computational account of how children can acquire and generalize highly abstract knowledge of the verb lexicon from the language around them. By applying the models to large, naturalistic corpora of child-directed speech, I show that these models capture key behaviours in child language development. These models offer the power to investigate developmental phenomena with a degree of breadth and realism unavailable in existing computational accounts of verb learning. By most accounts, children rely on strong regularities between form and meaning to help them acquire abstract verb knowledge. Using a token-level clustering model, I show that by attending to simple syntactic features of potential verb arguments in the input, children can acquire abstract representations of verb argument structure that can reasonably distinguish the senses of a highly polysemous verb. I develop a novel hierarchical model that acquires probabilistic representations of verb argument structure, while also acquiring classes of verbs with similar overall patterns of usage. In a simulation of verb learning within a broad, naturalistic context, I show how this abstract, probabilistic knowledge of alternations can be generalized to new verbs to support learning. I augment this verb class model to acquire associations between form and meaning in verb argument structure, and to generalize this knowledge appropriately via the syntactic and semantic aspects of verb alternations. The model captures children's ability to use the alternation pattern of a novel verb to infer aspects of the verb's meaning, and to use the meaning of a novel verb to predict the range of syntactic forms in which the verb may participate. These simulations also provide new predictions of children's linguistic development, emphasizing the value of this model as a useful framework to investigate verb learning in a complex linguistic environment.

Currently, online advertisements are created for specific dimensions and must be laboriously modified by advertisers to support different aspect ratios. In addition, publishers are constrained to design web pages to accommodate this limited set of sizes. As an alternative we present a framework for automatically generating visual banners at arbitrary sizes based on individual prototype ads. This technique can be used to create flexible visual ads that can be resized to accommodate various aspect ratios. In the proposed framework image and text data are stored separately. Resizing involves selecting a sub-region of the original image and updating text parameters (size and position). This problem is posed within an optimization framework that encourages solutions which maintain important structural properties of the original ad. The method can be applied to advertisements containing a wide variety of imagery and provides significantly more flexibility than existing solutions.

Despite the popularity and success of neural networks in research, the number of resulting commercial or industrial applications have been limited. A primary cause of this lack of adoption is due to the fact that neural networks are usually implemented as software running on general-purpose processors. Hence, a hardware implementation that can take advantage of the inherent parallelism in neural networks is desired. This thesis investigates how the Restricted Boltzmann machine, a popular type of neural network, can be effectively mapped to a high-performance hardware architecture on FPGA platforms. The proposed, modular framework is designed to reduce the time complexity of the computations through heavily customized hardware engines. The framework is tested on a platform of four Xilinx Virtex II-Pro XC2VP70 FPGAs running at 100MHz through a variety of different configurations. The maximum performance was obtained by instantiating a Restricted Boltzmann Machine of 256x256 nodes distributed across four FPGAs, which results in a computational speed of 3.13 billion connection-updates-per-second and a speed-up of 145-fold over an optimized C program running on a 2.8GHz Intel processor.