In this work we present an object tracking algorithm running on GPU. The tracking is achieved by a particle filter using appearance-adaptive models. The main focus of our work is parallel computation of the particle weights. The tracker yields promising GPU/CPU speed-up. We demonstrate that the GPU implementation of the algorithm that runs with 256 particles is about 30 times faster than the CP...

We present a novel method for massively parallel hierarchical scene processing on the GPU, which is based on sequential decomposition of the given hierarchical algorithm into small functional blocks. The computation is fully managed by the GPU using a specialized task pool which facilitates synchronization and communication of processing units. We present two applications of the proposed approa...

We study two different techniques for the computation of a matrix inverse, the traditional approach based on Gaussian factorization and the Gauss-Jordan elimination alternative more suitable for parallel architectures. The target architecture is a current general-purpose multi-core processor (CPU) connected to a graphics processor (GPU). Parallelism is obtained from the use of libraries MKL (fo...

Scientific computation requires a great amount of computing power especially in floating-point operation but a high-end multi-cores processor is currently limited in terms of floating point operation performance and parallelization. Recent technological advancement has made parallel computing technically and financially feasible using Compute Unified Device Architecture (CUDA) developed by NVID...

CPU/GPU heterogeneous computing has become a tendency in scientific and engineering computing. The conventional computation models cannot be used to estimate the application running time under the CPU/GPU heterogeneous computing environment. In this paper, a new model named mHLogGP is presented on the basis of mPlogP, LogGP and LogP. In mHLogGP, he communication and memory access is abstracted ...

Today, the need of large data collection processing increase. Such type of data can has very large dimension and hidden relationships. Analyzing this type of data leads to many errors and noise, therefore, dimension reduction techniques are applied. Many techniques of reduction were developed, e.g. SVD, SDD, PCA, ICA and NMF. Non-negative matrix factorization (NMF) has main advantage in process...

We present a novel method for massively parallel hierarchical scene processing on the GPU, which is based on sequential decomposition of the given hierarchical algorithm into small functional blocks. The computation is organized using a specialized work pool in which different blocks of processing units solve different functional blocks. We present an application of the proposed approach to two...

Image analysis is a process of extracting useful information from input images using various image processing techniques, such as denoising, enhancement, feature detection, and segmentation. State-of-the-art techniques employ sophisticated and time-consuming nonlinear processes to capture subtle and detailed information from images that may be overlooked by classical linear methods. In addition...

Ambient occlusion techniques were introduced to improve data comprehension by bringing soft fading shadows to the visualization of 3D datasets. They consist in attenuating light by considering the occlusion resulting from the presence of neighboring structures. Nevertheless they often come with an important precomputation cost, which prevents their use in interactive applications based on trans...

beneficial management practices (bmps) are important measures for reducing agricultural non-point source (nps) pollution. however, selection of bmps for placement in a watershed requires optimizing available resources to maximize possible water quality benefits. due to its iterative nature, the optimization typically takes a long time to achieve the bmp trade-off results which is not desirable ...