Repulsive attractive network for baseline extraction on document images

Date

1997-04-24

Author

Oztop, E
Mulayim, AY
Atalay, Mehmet Volkan
YarmanVural, F

Metadata

Show full item record

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

Item Usage Stats

42
views

0
downloads

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 characters of adjacent lines. The proposed method is also applicable to a nide range of image processing applications, such as curve fitting, segmentation and thinning.

URI

https://hdl.handle.net/11511/55356

Conference Name

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 97)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Repulsive attractive network for baseline extraction on document images Oztop, E; Mulayim, AY; Atalay, Mehmet Volkan; Yarman Vural, Fatoş Tunay (1999-05-01) 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...
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...
TRIAD+Filtering approach for complete magnetometer calibration Söken, Halil Ersin (2019-06-01) © 2019 IEEE.This paper proposes using TRIAD and Unscented Kalman Filter (UKF)algorithms in a sequential architecture as a part of the small satellite attitude estimation algorithm. This TRIAD+UKF approach can provide accurate attitude estimates for the satellite by calibrating the magnetometers in real-time. A complete calibration model for the magnetometers, considering bias, scale factor, soft iron and nonorthogonality errors, is assumed. In algorithm's first stage, the TRIAD uses the available vector mea...
Piecewise-planar 3D reconstruction in rate-distortion sense Imre, Evren; Gueduekbay, Ugur; Alatan, Abdullah Aydın (2007-05-09) In this paper, a novel rate-distortion optimization inspired 3D piecewise-planar reconstruction algorithm is proposed. The algorithm refines a coarse 3D triangular mesh, by inserting vertices in a way to minimize the intensity difference between an image and its prediction. The preliminary experiments on synthetic and real data indicate the validity of the proposed approach.
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...

Citation Formats

E. Oztop, A. Mulayim, M. V. Atalay, and F. YarmanVural, “Repulsive attractive network for baseline extraction on document images,” presented at the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 97), MUNICH, GERMANY, 1997, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55356.