Repeatable texture sampling with interchangeable patches

Rendering textures in real-time environments is a key task in computer graphics. This paper presents a new parallel patch-based method which allows repeatable sampling without cache, and does not create visual repetitions. Interchangeable patches of arbitrary shape are prepared in a preprocessing step, such that patches may lie over the boundary of other patches in a repeating tile. This compresses the example texture into an infinite texture map with small memory requirements, suitable for GPU and ray-tracing applications. The quality of textures rendered with this method can be tuned in the offline preprocessing step, and they can then be rendered in times comparable to Wang tiles. Experimental results demonstrate combined benefits in speed, memory requirements, and quality of randomisation when compared to previous methods

Distributed texture-based terrain synthesis

Terrain synthesis is an important field of Computer Graphics that deals with the generation of 3D landscape models for use in virtual environments. The field has evolved to a stage where large and even infinite landscapes can be generated in realtime. However, user control of the generation process is still minimal, as well as the creation of virtual landscapes that mimic real terrain. This thesis investigates the use of texture synthesis techniques on real landscapes to improve realism and the use of sketch-based interfaces to enable intuitive user control

High quality texture synthesis

Texture synthesis is a core process in Computer Graphics and design. It is used extensively in a wide range of applications, including computer games, virtual environments, manufacturing, and rendering. This thesis investigates a novel approach to texture synthesis in order to significantly improve speed, memory requirements, and quality. An analysis of texture properties is created, to enable the gathering a representative dataset, and a qualitative evaluation of texture synthesis algorithms. A new algorithm to make non-repeating texture synthesis on-the-fly possible is developed, tested, and evaluated. This parallel patch-based method allows repeatable sampling without cache, without creating visually noticeable repetitions, as confirmed by a perceptive objective study on quality. In order to quantify the quality of existing algorithms and to facilitate further development in the field, desired texture properties are classified and analysed, and a minimal set of textures is created according to these properties to allow subjective evaluation of texture synthesis algorithms. This dataset is then used in a user study which evaluates the quality of texture synthesis algorithms. For the first time in the field of texture synthesis, statistically significant findings quantify the quality of selected repeatable algorithms, and make it possible to evaluate new improved methods. Finally, in an effort to make these findings applicable in the British tile manufacturing industry, the developed texture synthesis technology is made available to Johnson Tiles

Algorithmic issues in visual object recognition

This thesis is divided into two parts covering two aspects of research in the area of visual object recognition. Part I is about human detection in still images. Human detection is a challenging computer vision task due to the wide variability in human visual appearances and body poses. In this part, we present several enhancements to human detection algorithms. First, we present an extension to the integral images framework to allow for constant time computation of non-uniformly weighted summations over rectangular regions using a bundle of integral images. Such computational element is commonly used in constructing gradient-based feature descriptors, which are the most successful in shape-based human detection. Second, we introduce deformable features as an alternative to the conventional static features used in classifiers based on boosted ensembles. Deformable features can enhance the accuracy of human detection by adapting to pose changes that can be described as translations of body features. Third, we present a comprehensive evaluation framework for cascade-based human detectors. The presented framework facilitates comparison between cascade-based detection algorithms, provides a confidence measure for result, and deploys a practical evaluation scenario. Part II explores the possibilities of enhancing the speed of core algorithms used in visual object recognition using the computing capabilities of Graphics Processing Units (GPUs). First, we present an implementation of Graph Cut on GPUs, which achieves up to 4x speedup against compared to a CPU implementation. The Graph Cut algorithm has many applications related to visual object recognition such as segmentation and 3D point matching. Second, we present an efficient sparse approximation of kernel matrices for GPUs that can significantly speed up kernel based learning algorithms, which are widely used in object detection and recognition. We present an implementation of the Affinity Propagation clustering algorithm based on this representation, which is about 6 times faster than another GPU implementation based on a conventional sparse matrix representation

A Fast and Accurate Cost Model for FPGA Design Space Exploration in HPC Applications

Heterogeneous High-Performance Computing (HPC) platforms present a significant programming challenge, especially because the key users of HPC resources are scientists, not parallel programmers. We contend that compiler technology has to evolve to automatically create the best program variant by transforming a given original program. We have developed a novel methodology based on type transformations for generating correct-by-construction design variants, and an associated light-weight cost model for evaluating these variants for implementation on FPGAs. In this paper we present a key enabler of our approach, the cost model. We discuss how we are able to quickly derive accurate estimates of performance and resource-utilization from the design’s representation in our intermediate language. We show results confirming the accuracy of our cost model by testing it on three different scientific kernels. We conclude with a case-study that compares a solution generated by our framework with one from a conventional high-level synthesis tool, showing better performance and power-efficiency using our cost model based approach

On the uncertain future of the volumetric 3D display paradigm.

Volumetric displays permit electronically processed images to be depicted within a transparent physical volume and enable a range of cues to depth to be inherently associated with image content. Further, images can be viewed directly by multiple simultaneous observers who are able to change vantage positions in a natural way. On the basis of research to date, we assume that the technologies needed to implement useful volumetric displays able to support translucent image formation are available and so primarily focus on other issues that have impeded the broad commercialization and application of this display paradigm. This is of particular relevance given the recent resurgence of interest in developing commercially viable, general purpose, volumetric systems. We particularly consider image and display characteristics, usability issues and identify several advantageous attributes that need to be exploited in order to effectively capitalize on this display modality.N/

Type-driven automated program transformations and cost modelling for optimising streaming programs on FPGAs

In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain

Visual pattern recognition using neural networks

Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks. In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance. We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations. Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results