This paper describes a shape recognition technique using boundary chain codes extracted by a method as described by Pavlidis and used an 8 connected neighbourhood. A chain code is a representation of a two dimensional contour using a one dimensional array. Feed forward neural networks were trained to recognise these chain codes. In addition, backpropagation network is trained using different training algorithms and the resulting optimal parameters are recorded. Depending upon the complexity of the object to be recognised, this technique can used to form the basis for object recognition or as the best method.
The research is also aimed to compare the performance of chain code representation as against centroidal profile extraction. The third objective is to determine the effectiveness of Feed forward artificial neural networks ANNs in recognising and classifying different medical waste items in the image form. The networks were trained on a large number of medical waste items. The wide variety of shapes and textures revealed that just a representation of an object’s boundary is not sufficient to recognise every object in the set, and some form of texture recognition will also be required in recognising medical wastes. The results have shown that chain code has lesser performance as compared to centroidal profile representation. Ramani Bai V. G. | Alla Kay R. | Andy Chan "Automation of Medical Waste Separation using Advanced Technologies to Minimize its Impact on Environment"
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018,
URL: https://www.ijtsrd.com/papers/ijtsrd19120.pdf
Paper URL: https://www.ijtsrd.com/engineering/environment-engineering/19120/automation-of-medical-waste-separation-using-advanced-technologies-to-minimize-its-impact-on-environment/ramani-bai-v-g
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