A novel fast image segmentation algorithm for large topographic maps

Miao, Qiguang; Xu, Pengfei; Liu, Tiange; Song, Jianfeng; Chen, Xiaojiang

doi:10.1016/j.neucom.2015.05.043

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…The challenges in segmenting STM have been analyzed in detail in many previous works [7][8][9][10][11][12][13][14][15]. These studies focused on the issues of color changes caused by the long storage period of paper maps, the frequent overlapping of geographic elements, and misalignment of the scanner.…”

Section: Background and Challengesmentioning

confidence: 99%

“…Usually, a topographic map has a size of around one square meter, which leads to constraints on the scanning resolution of such a large map because of the memory cost. This is why some geographic elements, especially linear ones, have very thin shapes (two to four pixels The challenges in segmenting STM have been analyzed in detail in many previous works [7][8][9][10][11][12][13][14][15]. These studies focused on the issues of color changes caused by the long storage period of paper maps, the frequent overlapping of geographic elements, and misalignment of the scanner.…”

Section: Background and Challengesmentioning

confidence: 99%

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Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation

Liu

Miao

et al. 2020

Remote Sensing

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Motivated by applications in topographic map information extraction, our goal was to discover a practical method for scanned topographic map (STM) segmentation. We present an advanced guided watershed transform (AGWT) to generate superpixels on STM. AGWT utilizes the information from both linear and area elements to modify detected boundary maps and sequentially achieve superpixels based on the watershed transform. With achieving an average of 0.06 on under-segmentation error, 0.96 on boundary recall, and 0.95 on boundary precision, it has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues. Based on AGWT, a benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed. There are several notable ideas behind the proposed approach. Superpixels are employed to overcome the false color and color aliasing problems that exist in STMs. The unification method of random selection facilitates sufficient training data with little manual labeling while keeping the potential color information of each geographic element. Moreover, with the small number of parameters, SCNN can accurately and efficiently classify those unified pixel sequences. The experiments show that SSCNN achieves an overall F1 score of 0.73 on our STM testing dataset. They also show the quality of the segmentation results and the short run time of this approach, which makes it applicable to full-size maps.

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Section: Background and Challengesmentioning

confidence: 99%

Section: Background and Challengesmentioning

confidence: 99%

Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation

Liu

Miao

et al. 2020

Remote Sensing

Self Cite

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“…Image segmentation refers to a technology capable of partitioning an image into non-overlapping objects of interest based on the intrinsic features, such as intensity, texture, color and contrast [1,2]. Nowadays, image segmentation technique has been applied to various images: gray or color natural images [3][4][5], topographic maps [6], satellite images [7,8], and medical images [9,10].…”

Section: Introductionmentioning

confidence: 99%

A multi-scale 3D Otsu thresholding algorithm for medical image segmentation

Feng

Zhao²,

et al. 2017

Digital Signal Processing

128

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Thresholding technique is one of the most imperative practices to accomplish image segmentation. In this paper, a novel thresholding algorithm based on 3D Otsu and multi-scale image representation is proposed for medical image segmentation. Considering the high time complexity of 3D Otsu algorithm, an acceleration variant is invented using dimension decomposition rule. In order to reduce the effects of noises and weak edges, multi-scale image representation is brought into the segmentation algorithm. The whole segmentation algorithm is designed as an iteration procedure. In each iteration, the image is segmented by the efficient 3D Otsu, and then it is filtered by a fast local Laplacian filtering to get a smoothed image which will be input into the next iteration. Finally, the segmentation results are pooled to get a final segmentation using majority voting rules. The attractive features of the algorithm are that its segmentation results are stable, it is robust to noises and it holds for both bi-level and multi-level thresholding cases. Experiments on medical MR brain images are conducted to demonstrate the effectiveness of the proposed method. The experimental results indicate that the proposed algorithm is superior to the other multilevel thresholding algorithms consistently.

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