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 1 o apart to obtain the maximum number of elements of 180 o 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.
Highlights
Image classification is an active area of research with many applications, including object recognition for sorting, content-based image retrieval, and surveillance
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 used as inputs to train a neural network for plastic bottle shape classification
We have adopted the feature based approach to represent the shape of plastic bottles using TMBA and produced a set of feature vectors for representing the 2D bottle silhouette known as the maximum of 9HbSPV, short for 9 histogram bin of sum pixel value
Summary
Image classification is an active area of research with many applications, including object recognition for sorting, content-based image retrieval, and surveillance. Object contours contain detailed information about the shape of objects. Computer vision systems may use similar information to classify objects. We propose a new approach to classify plastic bottle shape by implementing the viability of imaging technology for automated sorting. A study has been proposed to determine the viability of using computer vision for automated classification of plastic bottles based on shape properties of a single object, or on patterns of objects present in the image. Plastic sorting or classification has been based on the type of material used -(3). This procedure can be considered as two stages; a) a feature vector is extracted from the analysis of partial erosion based technique and structure element used, and b) a classification technique is applied to that input vector in order to provide a meaningful categorization of the data content
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