Canny edge detection on NVIDIA CUDA

Luo, Yuhua; Duraiswami, Ramani

doi:10.1109/cvprw.2008.4563088

Cited by 58 publications

(16 citation statements)

References 6 publications

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“…3), an interpolation is necessary to infer the neighboring gradients using Eq. (5)- (7). Here, d is the tangent function of the gradients direction θ, and M 1 and M 2 are the gradient magnitudes of neighbor pixels along the direction.…”

Section: Canny Edge Detection Algorithmmentioning

confidence: 99%

“…10and (11), where W and H denote the width and height of the images, respectively. IL = 12 + 4 × W (10) Table 8 shows that the proposed FPGA also has an evident advantage over the GPU implementation proposed in [7]. The FPGA speedup factor for the GPU implementation varies from 1.55 to 3.21.…”

Section: Hardware Performancementioning

confidence: 99%

“…Among the existing edge detection algorithms, the Canny edge detector [6] has remained a standard for many years and has best performance [7]. However, the computationally intensive nature of the Canny algorithm makes it costly on hardware resources and processing speed.…”

Section: Introductionmentioning

confidence: 99%

“…Many recent works have presented hardware implementation for real-time Canny edge detection. In [7], [8], constant threshold is used regardless of the image characteristics to reduce the computational complexity, where the performance of edge detection is relatively degraded compared with adaptive threshold approaches. He et al [9] present a pipelined implementation of an improved Canny edge detector with a self-adapt threshold mechanism.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Xiao

Fan

Feng

et al. 2020

IEICE Trans. Inf. & Syst.

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Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, lightweight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a lightweight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

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Section: Canny Edge Detection Algorithmmentioning

confidence: 99%

Section: Hardware Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Xiao

Fan

Feng

et al. 2020

IEICE Trans. Inf. & Syst.

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show abstract

“…The NPP library (Nvidia performance primitives) contains several functions for convolution, but they again only support two dimensional signals and filters stored as integers. In 2008, CUDA was used to accelerate the popular Canny algorithm for edge detection (Yuancheng and Duraiswami, 2008) and in 2010 for convolution with Gabor filters (Wang and Shi, 2010). Eklund et al (2012c) implemented non-separable 3D filtering with CUDA for volume registration, where both spatial convolution and FFT based filtering was tested.…”

Section: Filteringmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

351

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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On the benefits of the remote GPU virtualization mechanism: The rCUDA case

Silla

Iserte

Reaño

et al. 2017

Concurrency and Computation

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Graphics Processing Units (GPUs) are being adopted in many computing facilities given their extraordinary computing power, which makes it possible to accelerate many general purpose applications from different domains. However, GPUs also present several side effects, such as increased acquisition costs as well as larger space requirements. They also require more powerful energy supplies. Furthermore, GPUs still consume some amount of energy while idle and their utilization is usually low for most workloads. In a similar way to virtual machines, the use of virtual GPUs may address the aforementioned concerns. In this regard, the remote GPU virtualization mechanism allows an application being executed in a node of the cluster to transparently use the GPUs installed at other nodes. Moreover, this technique allows to share the GPUs present in the computing facility among the applications being executed in the cluster. In this way, several applications being executed in different (or the same) cluster nodes can share one or more GPUs located in other nodes of the cluster. Sharing GPUs should increase overall GPU utilization, thus reducing the negative impact of the side effects mentioned before. Reducing the total amount of GPUs installed in the cluster may also be possible. In this paper we explore some of the benefits that remote GPU virtualization brings to clusters. For instance, this mechanism allows an application to use all the GPUs present in the computing facility. Another benefit of this technique is that cluster throughput, measured as jobs completed per time unit, is noticeably increased when this technique is used. In this regard, cluster throughput can be doubled for some workloads. Furthermore, in addition to increase overall GPU utilization, total energy consumption can be reduced up to 40%. This may be key in the context of exascale computing facilities, which present an important energy constraint. Other benefits are related to the cloud computing domain, where a GPU can be easily shared among several virtual machines. Finally, GPU migration (and therefore server consolidation) is one more benefit of this novel technique.

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Canny edge detection on NVIDIA CUDA

Abstract: The

Cited by 58 publications

References 6 publications

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Medical image processing on the GPU – Past, present and future

On the benefits of the remote GPU virtualization mechanism: The rCUDA case

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