Experiences on Image and Video Processing with CUDA and OpenCL

Temizel, Alptekin; Halici, Tugba; Logoglu, Berker; Temizel, Tuğba Taşkaya; Omruuzun, Fatih; Karaman, Ersin

doi:10.1016/b978-0-12-384988-5.00034-6

Cited by 11 publications

(5 citation statements)

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“…Before building the model, correlation tests, covariance tests, and global significance tests were performed, respectively (Temizel et al, 2011;Hair et al, 1995). We retain the independent variables with Person values less than 0.7, VIF values below 10 and P values greater than 0.1, so we exclude intercity public transport station density (because it has a more significant covariance with P values greater than 0.1 and VIF values of 10.5).…”

Section: Model Comparison Resultsmentioning

confidence: 99%

Uncovering the spatiotemporal impacts of built environment on traffic carbon emissions using multi-source big data

Jia

Feng

et al. 2023

Land Use Policy

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Section: Model Comparison Resultsmentioning

confidence: 99%

Uncovering the spatiotemporal impacts of built environment on traffic carbon emissions using multi-source big data

Jia

Feng

et al. 2023

Land Use Policy

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“…The authors of [13] propose the implementation of several image processing algorithms like histogram equalization, edge detection and others, based on the GPGPU using CUDA. In [14], implementation using the GPGPU is proposed for image and video processing to tackle real-time issues and optimization. Another relevant work presented in [15], tried to optimize the use of the GPU in deep learning, more specifically in the inference phase, where the power of the GPU is not fully leveraged due to the small batch sizes.…”

Section: State Of the Artmentioning

confidence: 99%

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

En-Nejjary

Pinet

Kang

2021

IJGI

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The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data.

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“…The system is able to maintain the privacy of the persons under observation by using the graph representation provided by the GNG instead of the original video images. The importance of GPUs has recently been recognized for different applications, such as video and image processing algorithms; in particular, real-time video surveillance applications [37,38], where it is possible to manage information from different cameras [39], thanks to the parallel processing capabilities of GPUs.…”

Section: Privacy and Security: Real-time Constraintmentioning

confidence: 99%

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Orts-Escolano

García-Rodríguez

Morell

et al. 2014

JSAN

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In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

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Experiences on Image and Video Processing with CUDA and OpenCL

Cited by 11 publications

References 0 publications

Uncovering the spatiotemporal impacts of built environment on traffic carbon emissions using multi-source big data

Uncovering the spatiotemporal impacts of built environment on traffic carbon emissions using multi-source big data

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

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