Repulsive attractive network for baseline extraction on document images

Date

1999-05-01

Author

Oztop, E
Mulayim, AY
Atalay, Mehmet Volkan
Yarman Vural, Fatoş Tunay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

37
views

0
downloads

This paper describes a new framework, called repulsive attractive (RA) network for baseline extraction on document images. The RA network is an energy minimizing dynamical system, which interacts with the document text image through the attractive and repulsive forces defined over the network components and the document image. Experimental results indicate that the network can successfully extract the baselines under heavy noise and overlaps between the ascending and descending portions of the characters of adjacent lines. The proposed framework is applicable to a wide range of image processing applications, such as curve fitting, segmentation and thinning.

Subject Keywords

Baseline, Repulsive Attractive Network, Hough Transform, Energy Minimization, Active Model

URI

https://hdl.handle.net/11511/48477

Journal

SIGNAL PROCESSING

DOI

https://doi.org/10.1016/s0165-1684(98)00220-5

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Repulsive attractive network for baseline extraction on document images Oztop, E; Mulayim, AY; Atalay, Mehmet Volkan; YarmanVural, F (1997-04-24) This paper describes a new framework, called, Repulsive Attractive (RA) Network for Baseline Extraction on document images. The R.A network is a self organizing feature detector which interacts with the document text image through the attractive and repulsive forces defined among the network components and the document image. Experimental results indicate that the network can successfully extract the baselines under heavy noise and with overlaps between the ascending and descending portions of the character...
Reconstruction and identification of tau lepton decays to hadrons and nu(tau) at CMS Khachatryan, V.; et. al. (IOP Publishing, 2016-01-01) This paper describes the algorithms used by the CMS experiment to reconstruct and identify tau -> hadrons + nu(tau) decays during Run 1 of the LHC. The performance of the algorithms is studied in proton-proton collisions recorded at a centre-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.7 fb(-1). The algorithms achieve an identification efficiency of 50-60%, with misidentification rates for quark and gluon jets, electrons, and muons between per mille and per cent levels.
Performance of CMS muon reconstruction in pp collision events at root s=7Tev Chatrchyan, S.; et. al. (IOP Publishing, 2012-10-01) The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40pb(-1) of data collected in pp collisions at root s = 7TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum p(T) larger than a few GeV/c is above 95% over the whole region of pseudorapidity covered by the CMS muon syste...
Deep Spectral Convolution Network for Hyperspectral Unmixing Akar, Gözde (2018-10-10) In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures with a deeper network architecture. Second, instead of batch normalization, we propose a spectral normalization layer which improves the selectivity of filters b...
DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images Koyuncu, Can Fahrettin; Gunesli, Gozde Nur; Atalay, Rengül; GÜNDÜZ DEMİR, Çiğdem (Elsevier BV, 2020-07-01) This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression pro...

Citation Formats

E. Oztop, A. Mulayim, M. V. Atalay, and F. T. Yarman Vural, “Repulsive attractive network for baseline extraction on document images,” SIGNAL PROCESSING, pp. 1–10, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48477.