Quasar: A Programming Framework for Rapid Prototyping

We present a new programming framework, Quasar, which facilitates GPU programming. Our high-level programming language relieves the developer of all implementation details such that he can focus on the algorithm and the required accuracy. The proposed programming framework consists of a simple high-level programming language, an advanced compiler system, a runtime system and IDE. The Quasar language is a high level scripting language with an easy to learn syntax similar to python and MATLAB. This makes Quasar well suited for fast development and prototyping. A Quasar program is first processed by a front-end compiler that automatically detects serial and parallel loops that could be accelerated by heterogeneous hardware. In a second compilation phase, a number of back-end compilers processes the output of the front-end compiler, thus generating C++, OpenCL or CUDA code. Based on the generated code the runtime system can dynamically switch between CPU and GPU. This automatic scheduling at runtime is done by analyzing the load of all devices, the memory transfer cost and the complexity of the task. This way, the programmer is relieved from complicated implementation issues that are common for programming heterogeneous hardware. We validated the use of Quasar on a number of complex image processing and computer vision algorithms. These programs range from denoising to automated image segmentation applications. Using Quasar we get speed-up factors of 4 to over 60, depending on the application. All results were achieved using an NVIDIA GeForce M750

Embodied cognition through cultural interaction

In this short paper we describe a robotic setup to study the self-organization of conceptualisation and language. What distinguishes this project from others is that we envision a robot with specic cognitive capacities, but without resorting to any pre-programmed representations or conceptualisations. The key to this all is self-organization and enculturation. We report preliminary results on learning motor behaviours through imitation, and sketch how the language plays a pivoting role in constructing world representations

Quasar: rapid prototyping for image/video processing on heterogeneous hardware.

In this Show&Tell session, we present Quasar, a new framework for heterogeneous programming on (multi-)CPU/GPU systems. GPUs are increasingly used because of the large performance gains they offer for calculations with large amounts of data (e.g., in image and video processing). However, GPU programming is challenging as it requires significant programming expertise and because the corresponding tools are not well suited for rapid prototyping. Quasar is aimed at alleviating these challenges, while still delivering significant acceleration. This is achieved by employing a high-level language, with a similar abstraction level as Matlab or Python. We will demonstrate the Quasar language and IDE by showing several well-known (computationally intensive) image processing algorithms, such as pyramidal optical flow, voxel carving, volumetric ray tracing, geometric active contours, superpixel segmentation, image restoration... We highlight debugging, visualization and profiling capabilities while running the algorithms in real-time. The audience is encouraged to change algorithmic parameters and see their algorithmic influence in real-time. For more information, see http://gepura.i

Computationally efficient locally adaptive demosaicing of color filter array images using the dual-tree complex wavelet packet transform

Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost. This is done through a (dual-tree complex) wavelet interpretation of the demosaicing problem. By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way. Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost. The wavelet approach also allows for computationally effective denoising or deblurring approaches

Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

Modern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique estimates the moments of the output distribution through sampling with different dropout masks. The output uncertainty of a neural network is then approximated as the sample variance. In this paper, we highlight the limitations of such a variance-based uncertainty metric and propose an novel approach. Our approach is based on the overlap between output distributions of different classes. We show that our technique leads to a better approximation of the inter-class output confusion. We illustrate the advantages of our method using benchmark datasets. In addition, we apply our metric to skin lesion classification-a real-world use case-and show that this yields promising results