The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle segmentation in ultrasound

This paper examines several line-drawing pattern recognition methods for handwritten character recognition. They are the picture descriptive language (PDL), Berthod and Maroy (BM), extended Freeman's chain code (EFC), error transformation (ET), tree grammar (TG), and array grammar (AG) methods. A new character recognition scheme that uses improved extended octal codes as primitives is introduced. This scheme offers the advantages of handling flexible sizes, orientations, and variations, the need for fewer learning samples, and lower degree of ambiguity. Finally, the simulation of off-line character recognition by the real-time on-line counterpart is investigated.

Download Full-text

On-line re-training and segmentation with reduction of the training set: Application to the left ventricle detection in ultrasound imaging

2012 19th IEEE International Conference on Image Processing ◽

10.1109/icip.2012.6467281 ◽

2012 ◽

Author(s):

Jacinto C. Nascimento ◽

Gustavo Carneiro

Keyword(s):

Left Ventricle ◽

Ultrasound Imaging ◽

Training Set ◽

On Line

Download Full-text

METHOD OF DIAGNOSTICS OF FILLING MATERIAL STRENGTH BASED ON TIME SERIES

Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska ◽

10.35784/iapgos.231 ◽

2019 ◽

Vol 9 (3) ◽

pp. 46-49

Author(s):

Salim Mustafin ◽

Marat Arslanov ◽

Abdikarim Zeinullin ◽

Ekaterina Korobova

Keyword(s):

Time Series ◽

Pattern Recognition ◽

Observational Data ◽

Material Strength ◽

Filling Material ◽

Training Set ◽

Pattern Recognition Methods ◽

Hardening Process ◽

Strength Based

The method of forecasting of the filling concreting process according to the observational data of the stowing material’s state indices is carried out at earlier time series. The method of predicting the hardening process is based on pattern recognition methods. An algorithm for the case when the training set contains sets of time series of several classes is proposed.

Download Full-text

Spectral Pattern Recognition by a Two-Layer Perceptron: Effects of Training Set Size

Neurocomputation in Remote Sensing Data Analysis ◽

10.1007/978-3-642-59041-2_12 ◽

1997 ◽

pp. 105-116 ◽

Cited By ~ 4

Author(s):

Petra Staufer ◽

Manfred M. Fischer

Keyword(s):

Pattern Recognition ◽

Training Set ◽

Spectral Pattern ◽

Set Size

Download Full-text

SUPERVISED LEARNING ON LARGE REDUNDANT TRAINING SETS

International Journal of Neural Systems ◽

10.1142/s0129065793000031 ◽

1993 ◽

Vol 04 (01) ◽

pp. 15-25 ◽

Cited By ~ 19

Author(s):

MARTIN MØLLER

Keyword(s):

Supervised Learning ◽

Conjugate Gradient ◽

Training Set ◽

Backpropagation Algorithm ◽

Conjugate Gradient Algorithms ◽

Set Size ◽

Gradient Algorithms ◽

On Line ◽

Training Sets

Efficient supervised learning on large redundant training sets requires algorithms where the amount of computation involved in preparing each weight update is independent of the training set size. Off-line algorithms like the standard conjugate gradient algorithms do not have this property while on-line algorithms like the stochastic backpropagation algorithm do. A new algorithm combining the good properties of off-line and on-line algorithms is introduced.

Download Full-text

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014484x ◽

1986 ◽

Vol 44 ◽

pp. 694-695

Author(s):

G.Y. Fan ◽

J.M. Cowley

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Computer Software ◽

Processing System ◽

Light Level ◽

Host Computer ◽

Low Light ◽

Image Processing System ◽

Recent Developments ◽

On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Download Full-text