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hgpu.org » Applications » Computer science » Computer vision

Accelerating Live Graph-Cut-Based Object Tracking Using CUDA

Ismael Daribo, Zachary A. Garrett, Yuki Takaya, Hideo Saito

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 280

December 14, 2011 by hgpu

A fast and intuitive visual programming language (VPL) for constructing Computer Vision and Image processing systems on GPUs

Tejas D Kulkarni

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Tags: Algorithms, Computer science, Computer vision, CUDA, Image processing, nVidia, Optical flow

December 12, 2011 by hgpu

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

Arturo Ribes, Senshan Ji, Arnau Ramisa, Ramon Lopez De Mantaras

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, SIFT

December 11, 2011 by hgpu

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Martin Peniak, Anthony Morse, Christopher Larcombe, Salomon Ramirez-Contla, Angelo Cangelosi

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470, Package, Tesla C1060

December 11, 2011 by hgpu

Fast Face Detection Using Graphics Processor

Kailash Devrari, K.Vinay Kumar

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Tags: Algorithms, Computer science, Computer vision, CUDA, Image recognition, nVidia, nVidia Quadro FX 580

December 11, 2011 by hgpu

DTAM: Dense tracking and mapping in real-time

Richard A. Newcombe, Steven J. Lovegrove, Andrew J. Davison

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Tags: Algorithms, Computer science, Computer vision, Image alignment, nVidia, nVidia GeForce GTX 480

December 6, 2011 by hgpu

Auto-optimization of a Feature Selection Algorithm

Didem Unat, Han Suk Kim, Jurgen P. Schulze, Scott B. Baden

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Optimization, Rendering, Tesla C1060, Visualization

December 2, 2011 by hgpu

Fast hough transform on GPUs: exploration of algorithm trade-offs

Gert-Jan van den Braak, Cedric Nugteren, Bart Mesman, Henk Corporaal

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470

November 25, 2011 by hgpu

Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

Kwang-Ting (Tim) Cheng, Yi-Chu Wang

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Tags: ARM, Computer science, Computer vision, FFT, Image recognition, nVidia, nVidia Tegra

November 20, 2011 by hgpu

The MOPED framework: Object recognition and pose estimation for manipulation

The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

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Tags: Algorithms, Clustering, Computer science, Computer vision, nVidia, nVidia GeForce GTX 260, Package, SIFT

November 18, 2011 by hgpu

gSLIC: a real-time implementation of SLIC superpixel segmentation

Carl Yuheng Ren, Ian Reid

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Package

November 15, 2011 by hgpu

AVSS2011 demo session: GPU enabled Smart Video Node

Sadiye Guler, Jay Silverstein, Ian Pushee, Xiang Ma, Ashutosh Morde

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 220

November 14, 2011 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

CLTestCheck: Measuring Test Effectiveness for GPU Kernels

CLTestCheck: Measuring Test Effectiveness for GPU Kernels

tensorflow-solvers

TensorFlow Doing HPC

CU2CL: A prototype CUDA-to-OpenCL source-to-source translator

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

EngineCL: Usability and Performance in Heterogeneous Computing

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

cuSten: CUDA Finite Difference Library

cuSten – CUDA Finite Difference and Stencil Library

ZMCintegral: An easy way to use multi-GPUs to calculate multi-dimensional integration

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

TensorFlow.js: A WebGL accelerated JavaScript library for training and deploying ML models

TensorFlow.js: Machine Learning for the Web and Beyond

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

SHA3-ON-GPU: SHA3 implementation on CPU & GPU

GPU Accelerated Keccak (SHA3) Algorithm

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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