14495
Tom Runia
In this thesis we design, implement and study a high-speed object detection framework. Our baseline detector uses integral channel features as object representation and AdaBoost as supervised learning algorithm. We suggest the implementation of two approximation techniques for speeding up the baseline detector and show their effectiveness by performing experiments on both detection quality and […]
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Patrick O. Glauner
This thesis describes the design and implementation of a smile detector based on deep convolutional neural networks. It starts with a summary of neural networks, the difficulties of training them and new training methods, such as Restricted Boltzmann Machines or autoencoders. It then provides a literature review of convolutional neural networks and recurrent neural networks. […]
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Agnieszka Lupinska
We present a simple parallel algorithm to test chordality of graphs which is based on the parallel Lexicographical Breadth-First Search algorithm. In total, the algorithm takes time O(N) on N-threads machine and it performs work O(N^2), where N is the number of vertices in a graph. Our implementation of the algorithm uses a GPU environment […]
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Andre Viebke
Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in the industry and research community recently. State-of-the-art applications in the area of computer vision and speech recognition (among others) are built using deep learning algorithms. In contrast to traditional algorithms, where the developer fully instructs the application what to do, […]
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Max Danielsson, Thomas Sievert
CONTEXT: Embedded platforms GPUs are reaching a level of performance comparable to desktop hardware. Therefore it becomes interesting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat generation can be an issue depending on load distribution on the device. OBJECTIVES: We evaluate the viability of a feature […]
Florence Monna
More and more computers use hybrid architectures combining multi-core processors (CPUs) and hardware accelerators like GPUs (Graphics Processing Units). These hybrid parallel platforms require new scheduling strategies. This work is devoted to a characterization of this new type of scheduling problems. The most studied objective in this work is the minimization of the makespan, which […]
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Mehdi Amini
Since the beginning of the 2000s, the raw performance of processors stopped its exponential increase. The modern graphic processing units (GPUs) have been designed as array of hundreds or thousands of compute units. The GPUs’ compute capacity quickly leads them to be diverted from their original target to be used as accelerators for general purpose […]
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Andre Eliasson, Pontus Franzen
CONTEXT: Simulating realistic fluid behavior in incompressible fluids for computer graphics has been pioneered with the implicit incompressible smoothed particle hydrodynamics (IISPH) solver. The algorithm converges faster than other incompressible SPH-solvers, but real-time performance (in the perspective of video games, 30 frames per second) is still an issue when the particle count increases. OBJECTIVES: This […]
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Akanksha Singh
Parallel programming has largely evolved as an efficient solution to a large number of compute intensive applications. Graphics Processing Unit (GPUs), traditionally designed to process computer graphics, are now widely applied to process large chunks of data parallely in many computationally expensive applications. While developing parallel programs to run on parallel computing platforms, such as […]
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Robert Andrew Aspin
Capturing, recreating and representing a high fidelity virtual representation of the dynamic human form has long been a target for a diverse range of applications including tele-presence, games, film and TV special effects. The complexity of the challenge, to achieve a lifelike, faithful and believable representation, is such that a wide range of techniques and […]
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Axel Angel
Current research in Computer Vision has shown that Convolutional Neural Networks (CNN) give state-of-the-art performance in many classification tasks and Computer Vision problems. The embedding of CNN, which is the internal representation produced by the last layer, can indirectly learn topological and relational properties. Moreover, by using a suitable loss function, CNN models can learn […]
Thomas Chun Pong Chau
This thesis addresses the problem of designing real-time reconfigurable systems. Our first contribution of this thesis is to propose novel data structures and memory architectures for accelerating real-time proximity queries, with potential application to robotic surgery. We optimise performance while maintaining accuracy by several techniques including mixed precision, function transformation and streaming data flow. Significant […]
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