Publication detail

Rozpoznávání a třídění objektů podle tvaru

Original Title

Rozpoznávání a třídění objektů podle tvaru

Czech Title

Rozpoznávání a třídění objektů podle tvaru

Language

cs

Original Abstract

This paper presents modern science appears from the basis of Computer Vision and classifier of Synthetic Neural Network. A picture of watched object has to be taken in high quality with the best lighting and camera has to be situated vertically upon the object. Different camera positions does not assure exact results. Subsequently, image has to be transformed into binary image and purged from noise and other interference. Moments are used to describe separate objects in picture. Via the central moments and normed moments I count seven moments characteristic for each object to be identified. These moments are practically independent on rotation or changing scale of the object. They fluctuate only in a short spread. It is input to Neural Network, which is used as the classifier. The system of Back-propagation is used as the Neural Network with type of learning called learning with teacher. In my work, each letter of alphabet is used as the object to be identified. Further, I tried to identify object by using a tolerance. But as described before, the counting moments are not changeless. By the change of scale, moments fluctuate more than by rotation, thereafter if any value of identified object is out of the tolerance set, then we are not able to make the identification. Neural Network solve this problem. Enumeration of each moment and Neural Network is programmed in the Matlab 6.5 environment.

Czech abstract

This paper presents modern science appears from the basis of Computer Vision and classifier of Synthetic Neural Network. A picture of watched object has to be taken in high quality with the best lighting and camera has to be situated vertically upon the object. Different camera positions does not assure exact results. Subsequently, image has to be transformed into binary image and purged from noise and other interference. Moments are used to describe separate objects in picture. Via the central moments and normed moments I count seven moments characteristic for each object to be identified. These moments are practically independent on rotation or changing scale of the object. They fluctuate only in a short spread. It is input to Neural Network, which is used as the classifier. The system of Back-propagation is used as the Neural Network with type of learning called learning with teacher. In my work, each letter of alphabet is used as the object to be identified. Further, I tried to identify object by using a tolerance. But as described before, the counting moments are not changeless. By the change of scale, moments fluctuate more than by rotation, thereafter if any value of identified object is out of the tolerance set, then we are not able to make the identification. Neural Network solve this problem. Enumeration of each moment and Neural Network is programmed in the Matlab 6.5 environment.

BibTex


@inproceedings{BUT23275,
  author="Pavel {Tofel}",
  title="Rozpoznávání a třídění objektů podle tvaru",
  annote="This paper presents modern science appears from the basis of Computer Vision and classifier of Synthetic Neural Network. A picture of watched object has to be taken in high quality with the best lighting and camera has to be situated vertically upon the object. Different camera positions does not assure exact results. Subsequently, image has to be transformed into binary image and purged from noise and other interference. Moments are used to describe separate objects in picture. Via the central moments and normed moments I count seven moments characteristic for each object to be identified. These moments are practically independent on rotation or changing scale of the object. They fluctuate only in a short spread. It is input to Neural Network, which is used as the classifier. The system of Back-propagation is used as the Neural Network with type of learning called learning with teacher. In my work, each letter of alphabet is used as the object to be identified. Further, I tried to identify object by using a tolerance. But as described before, the counting moments are not changeless. By the change of scale, moments fluctuate more than by rotation, thereafter if any value of identified object is out of the tolerance set, then we are not able to make the identification. Neural Network solve this problem. Enumeration of each moment and Neural Network is programmed in the Matlab 6.5 environment.",
  address="VUT Brno UFYZ",
  booktitle="NOVÉ TRENDY V MIKROELEKTRONICKÝCH SYSTÉMECH A NANOTECHNOLOGIÍCH",
  chapter="23275",
  institution="VUT Brno UFYZ",
  year="2007",
  month="january",
  pages="121--125",
  publisher="VUT Brno UFYZ",
  type="conference paper"
}