computer-vision-1

Line Detection

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

M. Jeon, Jiwon Jun, Yeongmin Ko, Donghwuy Ko•Sat Feb 15 2020

A traffic line detection method called Point Instance Network (PINet), based on the key points estimation and instance segmentation approach, which achieves competitive accuracy and false positive on the TuSimple and Culane datasets.

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READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

R. Labahn, Tobias Grüning, Markus Diem, Florian Kleber, Stefan Fiel•Mon May 08 2017

This paper collects and annotates 2036 archival document images from different locations and time periods and proposes a new evaluation scheme that is based on baselines, which has no need for binarization and it can handle skewed as well as rotated text lines.

71 0

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PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition

Yuliang Liu, Lianwen Jin, Dezhi Peng, Canjie Luo, Songxuan Lai•Thu Jul 28 2022

Extensive experiments conducted on five datasets demonstrate the superiority of PageNet over existing weakly supervised and fully supervised page-level methods, and may spark further research beyond the realms of existing methods based on connectionist temporal classification or attention.

33 0

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Fast 3D Line Segment Detection From Unorganized Point Cloud

Xiaohu Lu, Yahui Liu, Kai Li•Mon Jan 07 2019

This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud based on point cloud segmentation and 2D line detection.

78 0

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Real-time salient closed boundary tracking via line segments perceptual grouping

Xuebin Qin, Shida He, C. P. Quintero, Abhineet Singh, Masood Dehghan, Martin Jägersand•Sat Apr 29 2017

A new tracking criterion is defined which combines a grouping cost and an area similarity constraint and makes the resulting boundary tracking more robust to local minima.

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A two-stage method for text line detection in historical documents

R. Labahn, Tobias Grüning, Gundram Leifert, Tobias Strauß, Johannes Michael•Fri Feb 09 2018

The developed method is capable of handling complex layouts as well as curved and arbitrarily oriented text lines and substantially outperforms current state-of-the-art approaches.

154 0

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Deep Hough Transform for Semantic Line Detection

Kai Zhao, Mingg-Ming Cheng, Qi Han, Jun Xu•Mon Mar 09 2020

This paper incorporates the classical Hough transform technique into deeply learned representations and proposes a one-shot end-to-end learning framework for line detection by parameterizing lines with slopes and biases, which transforms the problem of detecting semantic lines in the spatial domain into spotting individual points in the parametric domain.

219 0

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PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML

Jiaquan Ye, Xianbiao Qi, Yelin He, Yihao Chen, Dengyi Gu, Rong Xiao•Tue May 04 2021

This paper presents our solution for ICDAR 2021 competition on scientific literature parsing taskB: table recognition to HTML. In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment.Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm. PSENet [2] is used to detect each text line in the table image. For text linerecognition, our model is also built on MASTER. Finally, in the box assignment phase, we associatedthe text boxes detected by PSENet with the structure item reconstructed by table structure prediction,and fill the recognized content of the text line into the corresponding item. Our proposed methodachieves a 96.84% TEDS score on 9,115 validation samples in the development phase, and a 96.32%TEDS score on 9,064 samples in the final evaluation phase.

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Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection

S. K. Panda, Y. Lee, M. Jawed•Sun Apr 09 2023

Agronav is proposed, an end-to-end vision-based autonomous navigation framework, which outputs the centerline from the input image by sequentially processing it through semantic segmentation and semantic line detection models.

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GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection

Tal Hassner•Sun Oct 04 2015

A CUDA based algorithm for LMS computation is presented and shown to be much faster than the optimal state of the art single threaded CPU algorithm and used to modify the well-known Hough Transform in order to efficiently detect image lines in noisy images.

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