Hardware Implementation of Reconfigurable 1D Convolution

Rao, Lei; Bin, Zhang; Zhao, Jianqiang

doi:10.1007/s11265-015-0969-5

Cited by 13 publications

(11 citation statements)

References 32 publications

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“…e purpose of the neural network is to determine what an input represents, and CNN uses the idea of convolution to provide a method for extracting feature values [11]. CNN is a deep feedforward neural network composed of a convolutional layer, nonlinear layer, pooling layer, and fully connected layer [12].…”

Section: Semantic Image Segmentation Research Methods Based On Separable Convolutional Neural Network Codecmentioning

confidence: 99%

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Wang

Hawbani

et al. 2021

Mathematical Problems in Engineering

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With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.

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Section: Semantic Image Segmentation Research Methods Based On Separable Convolutional Neural Network Codecmentioning

confidence: 99%

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Wang

Hawbani

et al. 2021

Mathematical Problems in Engineering

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“…The smoothing process is usually performed using a fixed predetermined Sigma value [14]- [19]. However, because of the direct correlation between the degree of smoothing and the Sigma value (this value itself corresponds to the standard deviation of the Gaussian filter), it may be reasonable to change the Sigma value if the input image specification changes.…”

Section: A Canny Parameters and Performancementioning

confidence: 99%

Noise-Robust, Reconfigurable Canny Edge Detection and its Hardware Realization

Kalbasi

Nikmehr

2020

IEEE Access

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Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny's superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application's circumstances requires additional preprocessing, which increases the algorithm's computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection's performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations. INDEX TERMS Adaptive systems, Gaussian noise, computational complexity, image edge detection, reconfigurable architectures. HOOMAN NIKMEHR received the B.Sc. degree in electronic engineering and the M.Sc. degree in computer architecture engineering from the University of Tehran, Tehran, Iran, in 1992 and 1997, respectively, and the Ph.D. degree in computer engineering from

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“…Convolution has been widely used in computer vision and image processing, including object recognition [2] and image matching [3], However, convolution operation typically requires a significant amount of computing resources [4]. Image filtering is applied as pre-processing to eliminate useless details and noise from an image.…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of filtered image using 2D Gaussian filter

Kabbai¹,

Sghaier²,

Douik³

et al. 2016

ijacsa

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Abstract-Image filtering is one of the very useful techniques in image processing and computer vision. It is used to eliminate useless details and noise from an image. In this paper, a hardware implementation of image filtered using 2D Gaussian Filter will be present. The Gaussian filter architecture will be described using a different way to implement convolution module. Thus, multiplication is in the heart of convolution module, for this reason, three different ways to implement multiplication operations will be presented. The first way is done using the standard method. The second way uses Field Programmable Gate Array (FPGA) features Digital Signal Processor (DSP) to ensure and make fast the scalability of the effective FPGA resource and then to speed up calculation. The third way uses real multiplier for more precision and a the maximum uses of FPGA resources. In this paper, we compare the image quality of hardware (VHDL) and software (MATLAB) implementation using the Peak Signal-to-Noise Ratio (PSNR). Also, the FPGA resource usage for different sizes of Gaussian kernel will be presented in order to provide a comparison between fixed-point and floating point implementations.

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Hardware Implementation of Reconfigurable 1D Convolution

Cited by 13 publications

References 32 publications

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

Noise-Robust, Reconfigurable Canny Edge Detection and its Hardware Realization

FPGA implementation of filtered image using 2D Gaussian filter

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