Superpixels using morphology for rock image segmentation

Malladi, Sree Ramya S. P.; Ram, Sundaresh; Rodríguez, Jeffrey J.

doi:10.1109/ssiai.2014.6806050

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…While the above algorithms represent a large part of the proposed superpixel algorithms, some algorithms are not included due to missing, unnoticed or only recently published implementations 2 . These include [85,86,87,88,89,90,91,92,93,94,95,96,97,98,95,99,100].…”

Section: Further Algorithmsmentioning

confidence: 99%

Superpixels: An evaluation of the state-of-the-art

Stutz¹,

Hermans²,

Leibe³

2018

Computer Vision and Image Understanding

466

322

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Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003 [1]. By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the stateof-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/.

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Section: Further Algorithmsmentioning

confidence: 99%

Superpixels: An evaluation of the state-of-the-art

Stutz¹,

Hermans²,

Leibe³

2018

Computer Vision and Image Understanding

466

322

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show abstract

“…In this paper, an improved whole nested edge detection network based on deformable convolution and extended convolution [24] is adopted to solve the problem that HED network is not accurate enough to detect multi-overlapping image edges. The model uses residual deformable block instead of traditional convolution to learn local detail features and adaptively adjust the receptive field.…”

Section: Residual Deformable Convolutionmentioning

confidence: 99%

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Wang¹,

Li²,

Wang³

et al. 2022

Computers, Materials &Amp; Continua

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Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.

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A Method of Ore Image Segmentation Based on Deep Learning

Duan

2018

Intelligent Computing Methodologies

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Superpixels using morphology for rock image segmentation

Cited by 7 publications

References 17 publications

Superpixels: An evaluation of the state-of-the-art

Superpixels: An evaluation of the state-of-the-art

An Image Edge Detection Algorithm Based on Multi-Feature Fusion

A Method of Ore Image Segmentation Based on Deep Learning

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