**8. References**

Anonymous. (2011a). FOOMA Japan 3D Measurement System That It Developed Jointly with the National Institute of Advanced Industrial Science and Technology*,* Date of access: 29.11.2011, Available from: http://www.diginfo.tv/2011/06/20/11-0123-d-en.php

We can conclude that differences between average of impacted surface areas at Golden Delicious and Stark Crimson apples were important but this was not the case for the Granny Smith apples. This result shows us that apple varieties, impact surface and tissue of apple

For the surface properties, wood surface creates more impacts on apples than plastic surface because of material properties. According to the results, plastic surface affects in a good way apple surfaces which are harvested or processed for postharvest. In this research we have shown that image analysis technique can easily be used for determining impacted surface areas and changes of colour values of the apples and other agricultural

The goal of machine vision research is to provide computers with humanlike perception capabilities so that they can sense the environment, understand the sensed data, take appropriate actions, and learn from this experience in order to enhance future performance. The field has evolved from the application of classical pattern recognition and image processing methods to advanced techniques in image understanding like model-based and

In recent years, there has been an increased demand for machine vision systems to address "real-world" problems. The current state-of-the-art in machine vision needs significant advancements to deal with real-world applications, such as agricultural navigation systems, target recognition, manufacturing, photo interpretation, remote sensing, etc. It is widely understood that many of these applications require vision algorithms and systems to work under partial occlusion, possibly under high clutter, low contrast, and changing environmental conditions. This requires that the vision techniques should be robust and

I wish to personally thank the following people for their contributions to my inspiration and

Anonymous. (2011a). FOOMA Japan 3D Measurement System That It Developed Jointly

with the National Institute of Advanced Industrial Science and Technology*,* Date of

varieties affect average of impacted surface area (Beyaz et al., 2010).

**6. Future of machine vision measurement technology** 

flexible to optimize performance in a given scenario (Sebe et al., 2005 ).

knowledge and other help in creating this chapter:

access: 29.11.2011, Available from:

http://www.diginfo.tv/2011/06/20/11-0123-d-en.php

products (Beyaz et al., 2010).

knowledge-based vision.

**7. Acknowledgement** 

 Prof. Dr. Ramazan OZTURK Prof. Dr. Ali Ihsan ACAR Prof. Dr. Mustafa VATANDAS Assistant Prof. Dr. Ufuk TURKER

MSc. Babak TALEBPOUR

**8. References** 

Anonymous. (2011b). History of Digital Image Processing, Date of access: 16.10.2011, Available from:

http://en.wikipedia.org/wiki/Digital\_image\_processing


**8** 

*Japan* 

**Measurement System of Fine Step-**

Takuya Kawamura, Kazuo Tani and Hironao Yamada *Department of Human and Information Systems, Gifu University,* 

In this study, to measure the human finger's tactile sensation capability of recognizing a fine surface texture using psychophysical experiments, a computer-controlled measurement system that presents fine step-heights of 0 to 1000 µm to human subjects' fingers at various presentation angles were developed. The measurement system can control four parameters of fine step presentation, i.e., the step-height, presentation velocity, presentation angle, and presentation temperature. In psychophysical experiments of this study, the measurement system calculates the amounts of step-heights based on the Parameter Estimation by Sequential Testing (PEST) method (Taylor & Creelman, 1982) and presents the step-heights to subjects' fingers in order to measure difference thresholds and subjective equalities for fine step-heights. Those values are considered to be the fine step-height discrimination

Human finger's tactile sense is a measurement system that can detect subtle surface roughness and smoothness by touching the surface. This finger's tactile sense is much more robust than the tactile sensors developed so far for robot tactile recognition. These sensors for robot still cannot reach the performance of recognizing such fine roughness or smoothness as humans can. Therefore, it is important for engineering, as well as for

So far several researchers have examined the finger's tactile sense mechanism in detail using microneurography and psychophysical experimentation. In the microneurography, a tungsten microelectrode was inserted into tactile-related nerve fibers in an arm of humans or monkeys and the reactions of the tactile sense to the stimuli presented to the hand were examined via the signals sensed by the microelectrode. In the psychophysical experiments, on the other hand, several magnitudes of stimuli were presented to human hands and the responses of the tactile sense to the stimuli are analyzed through the replies to questions

The microneurography found out that the human tactile organs consist of four types of mechanoreceptive units: Fast adapting type I unit (FA I), Fast adapting type II unit (FA II), Slowly adapting type I unit (SA I), and Slowly adapting type II unit (SA II) (Vallbo & Johansson, 1984; Salentijn, 1992), and it is considered that FA II responds to a subtle mechanical vibration, FA I or FA II to surface unevenness and SA I to a pattern like Braille

psychology, to investigate the finger's tactile recognition mechanism.

**1. Introduction** 

capability of finger's tactile sense.

regarding the stimulus magnitudes.

**Height Discrimination Capability of Human Finger's Tactile Sense** 

Sebe, N., Cohen, I., Garg, A., Huang, T. S. (2005). *Machine Learning in Computer Vision,* ISBN-13 978-1-4020-3275-2 Springer Dordrecht, Berlin, Heidelberg, New York, Published by Springer, Printed in the Netherlands
