A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C

Yam-Uicab, R.; Martínez, José Luis López; Trejo-Sánchez, Joel Antonio; Hidalgo-Silva, Hugo; Gonzalez-Segura, S.

doi:10.1007/s11227-017-2051-5

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…However, the pupil tracking literature related to GPGPU is relatively small if it is compared with all the available papers about GPGPU. There is a huge number of papers related to medical image processing [33,37] and image processing [39]. Some of the problems those papers try to solve are closely related to the ones we try to solve in this paper.…”

Section: Related Workmentioning

confidence: 96%

Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs

et al. 2017

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An efficient and robust pupil tracking system is an important tool in visual optics and ophthalmology. It is also central to techniques for gaze tracking, of use in psychological and medical research, marketing, human-computer interaction, virtual reality and other areas. A typical setup for pupil tracking includes a camera linked to infrared LED illumination. In this work, we evaluate and parallelize several pupil tracking algorithms with the aim of accurately estimating the pupil position and size in both eyes simultaneously, to be applied in a high-speed binocular pupil tracking system. To achieve high processing speed, the original non-parallel algorithms have been parallelized by using CUDA and OpenMP. Modern graphics processors are designed to process images at high temporal frequencies and spatial resolution, and CUDA enables them to be used for general-purpose computing. Our implementation allows for efficient binocular pupil tracking at high speeds for high-resolution images (up to 988 fps with images of 1280 × 1024 pixels) using a state-of-the-art GPU. Keywords High-speed eye pupil tracking • GPU • GPGPU • Image processing • Real-time systems • Gaze tracking

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Section: Related Workmentioning

confidence: 96%

Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs

et al. 2017

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“…The next step is to perform a Hough transformation to find the real counter from Figure 10f. Compared with other methods, it is easy to implement and has strong strong robustness [28]. As shown in Figure 11, for any point P i line (x, y) on the line L, Equation 7is set up:…”

Section: Accurate Location Of Handling Hole Based On Hough Transformamentioning

confidence: 99%

Visual Locating of Reactor in an Industrial Environment Using the Composite Method

Cao

Ouyang

Hou

et al. 2020

Sensors

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To achieve an automatic unloading of a reactor during the sherardizing process, it is necessary to calculate the pose and position of the reactors in an industrial environment with various amounts of luminance and floating dust. In this study, the defects of classic image processing methods and deep learning methods used for locating the reactors are first analyzed. Next, an improved You Only Look Once(YOLO) model is employed to find the region of interest of the handling hole and a handling hole corner detection method based on the image morphology and a Hough transform is presented. Finally, the position and pose of the reactors will be obtained by establishing a 3D handling hole model according to the principle of a binocular stereo system. To test the performance of the proposed method, a set of experimental systems was set up and experiments were conducted. The results indicate that the proposed location method is effective and the precision of the position recognition can be controlled to within 4.64 mm and 1.68 ° when the cameras are approximately 5 m away from the reactor, meeting the requirements.

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“…To protect the privacy of recipients and senders, their true information has been removed from all collected images. Then uses the Hough transform algorithm to correct the rotated image [27]. The result of image correction using Hough transform is shown in Fig.…”

Section: Image Collection and Correctionmentioning

confidence: 99%

Fast identification method for express end sorting label code based on convolutional recurrent neural network

et al. 2020

SIViP

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In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.

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A fast Hough Transform algorithm for straight lines detection in an image using GPU parallel computing with CUDA-C

Cited by 14 publications

References 11 publications

Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs

Design of an accurate and high-speed binocular pupil tracking system based on GPGPUs

Visual Locating of Reactor in an Industrial Environment Using the Composite Method

Fast identification method for express end sorting label code based on convolutional recurrent neural network

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