ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool

Qin, Xuebin; He, Shida; Zhang, Zichen; Dehghan, Masood; Jägersand, Martin

doi:10.1109/wacv.2018.00200

Cited by 36 publications

(21 citation statements)

References 27 publications

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“…A total of 30 original images of cross-sectional microscopic images of cotton fiber with 4272 × 2848 resolution were collected from different cotton samples [12], and a total of 720 microscopic images of cotton cross-sectional with 427 × 320 resolution were generated by the random cutting method. All of the cross-sectional microscopic images of cotton fiber in the dataset are manually labeled with the edge-based semi-automatic image annotation tool ByLable that was developed by Xuebin Qin et al [25]. Subsequently, the dataset containing 7200 cross-sectional microscopic images of cotton fiber was generated by data enhancement methods, such as scale scaling, image rotation, and mirror image.…”

Section: Construct Of the Experimental Datasetmentioning

confidence: 99%

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

He¹,

Wang²

2021

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Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cross-section intact, which will affect the measurement accuracy of maturity grade. In this paper, a new edge detection algorithm that is based on the RCF convolutional neural network (CNN) is proposed. For the microscopic image dataset of the cotton fiber cross-section constructed in this paper, the original RCF was firstly used to extract the edge of the cotton fiber cross-section in the image. After analyzing the output images of RCF in each convolution stage, the following two conclusions are drawn: (1) the shallow layers contain a lot of important edge information of the cotton fiber cross-section; (2) because the size of the cotton fiber cross-section in the image is relatively small and the receptive field of the convolutional layer gradually increases with the deepening of the number of layers, the edge information detected by the deeper layers becomes increasingly coarse. In view of the above two points, the following improvements are proposed in this paper: (1) modify the network supervision model and loss calculation structure; (2) the dilated convolution in the deeper layers is removed; therefore, the receptive field in the deeper layers is reduced to adapt to the detection of small objects. The experimental results show that the proposed method can effectively improve the accuracy of edge extraction of cotton fiber cross-section.

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Section: Construct Of the Experimental Datasetmentioning

confidence: 99%

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

He¹,

Wang²

2021

Information

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“…In a different approach, [34] have developed a recurrent neural network that iteratively proposes segmented objects to human annotators and refines the annotations with regard to their previous modifications. [35] presented a semi-automated platform that works based on edge detection, where high quality detected instances are proposed to annotators. It is worth mentioning that other studies have looked into novel tools based on different user interactions mechanisms, e.g.…”

Section: Assistive User Interfacesmentioning

confidence: 99%

A crowdsourcing semi-automatic image segmentation platform for cell biology

Bafti

Ang

Hossain

et al. 2021

Computers in Biology and Medicine

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“…Therefore, the meaningful regions of uniform size 540 × 360 pixels are cropped from the original images to form the datasets. To acquire pixel-level annotations, we adopt the publicly available semi-automatic boundary annotation tool ByLabel [27], in which edge fragments detected by EDLines [28] are selected by annotator and combined automatically. Compared with purely manual annotation, the semi-automatic annotation is much more accurate for the superiority of EDLines on edge localization.…”

Section: Power Line Datasetsmentioning

confidence: 99%

Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints

Zhang

Yang

et al. 2019

Remote Sensing

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Power line detection plays an important role in an automated UAV-based electricity inspection system, which is crucial for real-time motion planning and navigation along power lines. Previous methods which adopt traditional filters and gradients may fail to capture complete power lines due to noisy backgrounds. To overcome this, we develop an accurate power line detection method using convolutional and structured features. Specifically, we first build a convolutional neural network to obtain hierarchical responses from each layer. Simultaneously, the rich feature maps are integrated to produce a fusion output, then we extract the structured information including length, width, orientation and area from the coarsest feature map. Finally, we combine the fusion output with structured information to get a result with clear background. The proposed method fully exploits multiscale and structured prior information to conduct both accurate and efficient detection. In addition, we release two power line datasets due to the scarcity in the public domain. The method is evaluated on the well-annotated power line datasets and achieves competitive performance compared with state-of-the-art methods.

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ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool

Cited by 36 publications

References 27 publications

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

A crowdsourcing semi-automatic image segmentation platform for cell biology

Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints

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