Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Tariq, Nazish; Hamzah, Rostam Affendi; Ng, Theam Foo; Wang, Shir Li; Ibrahim, Haidi

doi:10.1109/access.2021.3089210

Cited by 27 publications

(11 citation statements)

References 82 publications

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“…The specific method is to first expand the height and width of the depth image to 1.437 times of the original image respectively, and then select the pixels with row coordinates of 25 and column coordinates of 18 in the image as the upper left corner of the clipping area, and the interception resolution is 700 × 540, and then the intercepted area is enlarged to a resolution of 1400 by bilinear interpolation × 1 080 depth image, and finally 1 024 is intercepted in the central area × 1 024 pixel depth image. At this time, the depth image and color image can realize pixel mapping, and these processed depth images can be used to generate depth mask annotation samples, and in the number of samples and samples [16]. The content is consistent with the sample set of RGB image.…”

Section: Construction Of Materials Methods Data Set a Test Materials ...mentioning

confidence: 98%

A Method of Crop Seedling Plant Segmentation on Edge Information Fusion Model

et al. 2022

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Automatic segmentation of plant images is a hot issue in plant phenotyping research. It is also one of the core technologies for applications such as crop growth process monitoring and pest identification. Due to the different scales and sizes of fruits, branches and leaves of fruit and vegetable plants in the natural environment, and irregular edges, it is difficult to accurately segment. In order to accurately segment crop seedlings in natural environment and realize automatic measurement of seedling location and phenotype, this paper proposes a crop seedling plant segmentation network model that fuses the semantic and edge information of target regions. The backbone network is composed of the UNET network, which guides the backbone network to perceive the plant edge information when extracting features; uses the spatial hole feature pyramid to build a feature fusion module, which fuses the features extracted by the UNET backbone network and the edge perception module. Combining edge-aware loss and feature fusion loss, a joint loss function is constructed for overall network optimization. The encoderdecoder network is referenced in the study. The encoding network uses densenet to reuse and fuse multilayer features to improve the way of information transmission; the decoding network uses transposed convolution for upsampling, combined with layer jump connections to fuse shallow detail information and deep semantic information; add a hole between encoding and decoding Atrous spatial pyramid pooling (ASPP) to extract feature maps of different receptive fields to integrate multi-scale features and aggregate contextual information. The experimental results show that under the same network training parameters, the average cross-merging rate and average recall rate obtained by testing the method in this paper are 58.13% and 64.72%, respectively, which are better than the segmentation results corresponding to the manually labeled samples; in addition, adding in the training samples After 10% of the outdoor seedling images, the average pixel accuracy of the proposed method on the outdoor test set can reach 90.54%, with good generalization ability.

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Section: Construction Of Materials Methods Data Set a Test Materials ...mentioning

confidence: 98%

A Method of Crop Seedling Plant Segmentation on Edge Information Fusion Model

et al. 2022

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“…The systematic literature review was conducted by referring to the guidelines found in [7]. The findings have been published on the IEEE, Wiley, MDPI, Hindawi, and Scopus websites.…”

Section: Proposed Methodsmentioning

confidence: 99%

Energy-efficient non-orthogonal multiple access for wireless communication system

Darus

Idris

Hashim

2023

IJECE

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<p>Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.</p>

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“…Edge detection contributes a lot of important information. The edge detection algorithm represents an image with a contour that makes it a recognizable object with detected edges [8][9]. One of the most important features of the edge detection method is that precise edge detection accompanies good object orientation in the image [10].…”

Section: Canny Edge Detectionmentioning

confidence: 99%

Identification of Signature Images with Edge Detection Canny

Tasri¹

2022

JOMAse

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In authenticating and verifying important documents, one of them is in the form of identifying the authenticity of a signature. In addition, the signature is also a form of ratification and a sign of approval in important documents is mandatory. Along with current technological developments, the signing process can be carried out in digital media such as cellphones and other media. The ability of the system to identify a person's signature becomes important because of the many forgeries that occur. This study aims to implement the Canny edge detection method to identify a person's signature. The number of signature images used is 10 signatures. The results of this study indicate that the Canny edge detection method has a similarity percentage of 70% to 100%, and the similarity values below 70% and above 100% are grouped into signature images that are not original.

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Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Cited by 27 publications

References 82 publications

A Method of Crop Seedling Plant Segmentation on Edge Information Fusion Model

A Method of Crop Seedling Plant Segmentation on Edge Information Fusion Model

Energy-efficient non-orthogonal multiple access for wireless communication system

Identification of Signature Images with Edge Detection Canny

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