A Defect Tea Leaf Identification Using Image Processing

• Autorzy: Karunamoorthy B. , Somasundereswari D.

Warianty tytułu

PL

Identyfikacja uszkodzonych liści herbaty z wykorzystaniem metod przetwarzania obrazu

• Języki publikacji: EN

Abstrakty

EN

This paper introduces defect identification on tea leaves during production .This frame work introduces an active learning strategy through a set of passively trained leaf parameters. Under the supervision of support vector machine the trained parameters and input image are compared to know the characteristics of the leaves This algorithm is been used for identification of defective leaves using image processing techniques and for the removal of defective leaves through real time techniques.

PL

W artykule opisano metodę oceny jakości liści herbaty w procesie produkcji. Na wstępnym etapie układ trenowany jest do rozpoznawania uszkodzonych liści z wykorzystaniem metody SVM. W metodzie wykorzystuje się narzędzia graficznej przetwarzania obrazu.

Słowa kluczowe

EN

detection; rejection; image processing; real time; SVM

PL

przetwarzanie obrazu; defektoskopia; metoda SVM

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 9
• Strony: 318--320
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Karunamoorthy B.

• Kumaraguru college of Technology, Anna University, Chennai

autor

Somasundereswari D.

• Adithya institute of Technology, Anna University

Bibliografia

• [1] Baladhandapani ARUNADEVI, Subramaniam Nachimuthu DEEPA “Texture Analysis for 3D Classification of Brain Tumor Tissues” PRZEGLĄD ELEKTROTECHNICZNY, R. 89 NR 4/2013 (2013), 342-348.
• [2] Leor Shoker, Saeid Sanei, and Jonathon Chambers ‘Artifact Removal From Electroencephalograms Using a Hybrid BSSSVM Algorithm’ ieee signal processing letters, vol. 12, no. 10, october 2005 721-724.
• [3] Ben Fei and Jinbai Liu’ Binary Tree of SVM: A New Fast Multiclass Training and Classification Algorithm, IEEE transactions on neural networks, vol. 17, no. 3, may 2006 . 696 - 704
• [4] Wen-Chang Cheng and Ding-Mao Jhan ,” Triaxial Accelerometer-Based Fall Detection Method Using a Self-Constructing Cascade-AdaBoost-SVM Classifier” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013 pages -411-419
• [5] Idan Ram, Michael Elad, Senior Member, IEEE, and Israel Cohen, Senior Member, IEEE “Generalized Tree-Based Wavelet Transform” IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 59, NO. 9, SEPTEMBER 2011 page no:4199-4209
• [6] Xin Wang, Member, IEEE” Moving Window-Based Double Haar Wavelet Transform for Image Processing” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 9, SEPTEMBER 2006 page no:2771-2780
• [7] H. K. Tönshoff, Xiaoli Li, and C. Lapp “ Application of Fast Haar Transform and Concurrent Learning to Tool-Breakage Detection in Milling” IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 3, SEPTEMBER 2003 page no: 414-419.
• [8] Lin Zhang, Member, IEEE, Hongyu Li, and Junyu Niu’ Fragile Bits in Palmprint Recognition’ IEEE SIGNAL PROCESSING LETTERS, VOL. 19, NO. 10, OCTOBER 2012 page no : 663-667
• [9] Philippe Schmid-Saugeon, and Avideh Zakhor,” Dictionary Design for Matching Pursuit and Application to Motion- Compensated Video Coding’ IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 6, JUNE 2004 page no 880-887