Accelerated method for the optimization of quadratic image filter

Uzun, Süleyman; Akgün, Devrim

doi:10.1117/1.jei.28.3.033036

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The first one aims to evaluate the impact of the parallelism strategy on the Hessian processing execution time separately. The second objective consists in studying the execution time improvement of Eigen value computing, as proceeded in 34 . Thereafter, the third one consists at evaluating the impact of our proposed parallelism through a practical field application.…”

Section: Experiments Principles Of Our Implementation Method: a Case Study Of Thick Vessel Extraction From Image Fundusmentioning

confidence: 99%

Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

et al. 2020

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Several leading-edge applications such as pathology detection, biometric identification and face recognition are mainly based on blob and line detection. To address this problem, the Eigen value computing has been commonly employed due to its accuracy and robustness. However, the Eigen value computing requires a raised computational processing, an intensive memory data access and a data overlapping which involve higher execution times.To overcome these limitations, we propose in this paper a new parallel strategy to implement the Eigen value computing using a GPU. Our contributions are: (1) to optimize instruction scheduling in order to reduce the computation time, (2) to efficiently partition processing into blocks in order to increase the occupancy of streaming multiprocessors, (3) to provide efficient input data splitting on shared memory to take benefit from its lower access time, ( 4) and to propose new data management of shared memory so as to avoid access memory conflict and reduce memory bank accesses. Experimental results show that our proposed GPU parallel strategy for Eigen value computing achieves speedups of 27 compared to a multithreaded implementation, of 16 compared to a predefined function in the OpenCV library, and of 8 compared to a predefined function in the Cublas library, which are performed into a quad core multi-CPU platform. Next, our parallel strategy is evaluated through an Eigen value based method for retinal thick vessel segmentation which is essential for detecting ocular pathologies. The Eigen value computing is executed in 0.017 seconds, when using STARE database images. Accordingly, we have achieved real-time thick retinal vessel segmentation where average execution time is about 0.039 seconds.

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Section: Experiments Principles Of Our Implementation Method: a Case Study Of Thick Vessel Extraction From Image Fundusmentioning

confidence: 99%

Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

et al. 2020

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“…The larger the threshold is, the stronger the anti-jamming ability of the image edge detection is, but the edge of the image is easy to be lost. When the filter size is selected, the smaller the filter size can detect the small edge of the image, but the phenomenon of poor anti-jamming ability will occur [7][8].…”

Section: 2select Adaptive Thresholdmentioning

confidence: 99%

Analyzing scene image features with adaptive nonlinear diffusion equation filters for public safety project scheduling

Li¹,

You²,

2023

Preprint

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The current image feature detection methods fail to perform image edge filtering in all directions, leading to target deviation of the feature detection results, and narrower brightness level range of gray images. Hence, a novel approach based on adaptive nonlinear diffusion equation filtering is proposed for detecting security events in public safety project scheduling. An edge detection operator is developed that can work in any direction, and adaptive nonlinear diffusion image edge morphological composite filtering is implemented using lag threshold and NMS. The shape response of the image edge is calculated to select the adaptive threshold of feature extraction. A gaussian function pyramid is constructed to obtain the feature distribution sequence considering multi-scale factors. The wavelet analysis is used to analyze the gray scale and detail features of the scene images of public security events, and the gray projection method is utilized to detect the target features of dynamic images, completing the scene image feature detection of public security events. The experimental results demonstrate that the proposed feature detection method has high accuracy, shorter time, and ideal application performance.

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Rapeseed Storage Quality Detection Using Hyperspectral Image Technology - An Application for Future Smart Cities

Liao

Xiao

2023

Journal of Testing and Evaluation

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At present, the application of hyperspectral image technology in image target detection is lacking black-and-white correction, and the average spectral reflectance cannot be calculated, which leads to large error in image feature detection and classification. In this study, hyperspectral image technology was applied to the detection of rapeseed storage quality, and germination detection was completed during the storage of rapeseed. The black-and-white board correction to the hyperspectral data was completed and the spectral characteristic curve of the rapeseed sample hyperspectral image was obtained. The average spectral reflectance is calculated, the threshold of hyperspectral image is estimated, and the correlation technique is used to denoise the hyperspectral image. Based on this, the edge feature of the rapeseed hyperspectral image is recognized, and the feature classification of the hyperspectral rapeseed image is realized by combining the gray co-occurrence matrix. The experimental results show that the proposed method can detect the germination of rapeseed with high precision under the application of hyperspectral image technology. This study provides a reliable basis for the application of hyperspectral image technology.

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Accelerated method for the optimization of quadratic image filter

Cited by 3 publications

References 29 publications

Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

Analyzing scene image features with adaptive nonlinear diffusion equation filters for public safety project scheduling

Rapeseed Storage Quality Detection Using Hyperspectral Image Technology - An Application for Future Smart Cities

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