Turkish fingerspelling recognition system using Generalized Hough Transform, interest regions, and local descriptors

Altun O., Albayrak S.

PATTERN RECOGNITION LETTERS, cilt.32, sa.13, ss.1626-1632, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 13
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.patrec.2011.06.010
  • Dergi Adı: PATTERN RECOGNITION LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1626-1632
  • Anahtar Kelimeler: Generalized Hough Transform, DoG, SIFT, Interest regions, Local descriptors, Fingerspelling recognition, FEATURES
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

This paper presents a computer vision system that can recognize Turkish fingerspelling sign hand postures by a method based on the Generalized Hough Transform, interest regions, and local descriptors. A novel method for calculating the reference point for the Generalized Hough Transform, and a simpler but more effective Hough voting strategy are proposed. The stages of implementing a Generalized Hough Transform are examined in detail, and the issues that affect the method success are discussed. The system is tested on a data set with 29 classes of non-rigid hand postures signed by three different signers on non-uniform backgrounds. It attains a 0.93 success rate. (C) 2011 Elsevier B.V. All rights reserved.