Partial erosion-based feature extraction approach for plastic bottle shape classification

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4 Citations (Scopus)

Abstract

In order to utilize or to extract the shape information of objects in an image for recognition, classification or retrieval, a method for representing a shape is needed. In this paper, a work on representing plastic bottle shape using morphological based approach for an automated classification is reported. Morphological operations are used to describe the structure or form of an image. There are three primary morphological functions: erosion, dilation, and hit-or-miss. By using the two-dimensional description of plastic bottle silhouettes, we perform edge detection of the object silhouette followed by the erosion process. This work will compare two versions of erosion which are regular erosion, the matlab function imerode and the improved version of erosion which is called partial erosion. The erosion technique involves defining a set of flat and linear structuring elements and specifying the angle at 1o apart to obtain the maximum number of elements of 180° degrees. This is followed by a normalization procedure in which we divide the sum pixel value after erosion by the sum pixel of the whole silhouette. The normalized values are grouped into histograms of 9 bins and find the maximum number of the 9 histogram bin of sum pixel value (9HbSPV) obtained forms a set of feature set and is then used as inputs to train a neural network for plastic bottle shape classification. Both feature sets from the two types of erosion were tested on their uniqueness to represent the shape. Results obtained showed that the proposed feature extraction method can be applied to discriminate plastic bottles according to shape, either slim or broad bottles, efficiently.

Original languageEnglish
Pages (from-to)77-83
Number of pages7
JournalModern Applied Science
Volume6
Issue number4
DOIs
Publication statusPublished - Apr 2012

Fingerprint

Plastic bottles
Feature extraction
Erosion
Pixels
Bins
Bottles
Edge detection
Neural networks

Keywords

  • Histogram of sum-pixel value
  • Morphological operation
  • Partial erosion
  • Structuring element

ASJC Scopus subject areas

  • General

Cite this

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abstract = "In order to utilize or to extract the shape information of objects in an image for recognition, classification or retrieval, a method for representing a shape is needed. In this paper, a work on representing plastic bottle shape using morphological based approach for an automated classification is reported. Morphological operations are used to describe the structure or form of an image. There are three primary morphological functions: erosion, dilation, and hit-or-miss. By using the two-dimensional description of plastic bottle silhouettes, we perform edge detection of the object silhouette followed by the erosion process. This work will compare two versions of erosion which are regular erosion, the matlab function imerode and the improved version of erosion which is called partial erosion. The erosion technique involves defining a set of flat and linear structuring elements and specifying the angle at 1o apart to obtain the maximum number of elements of 180° degrees. This is followed by a normalization procedure in which we divide the sum pixel value after erosion by the sum pixel of the whole silhouette. The normalized values are grouped into histograms of 9 bins and find the maximum number of the 9 histogram bin of sum pixel value (9HbSPV) obtained forms a set of feature set and is then used as inputs to train a neural network for plastic bottle shape classification. Both feature sets from the two types of erosion were tested on their uniqueness to represent the shape. Results obtained showed that the proposed feature extraction method can be applied to discriminate plastic bottles according to shape, either slim or broad bottles, efficiently.",
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