| Abstract: | Digital image processing is a rapidly evolving field with growing applications in science and engineering. Since, vision is the most advanced of our senses; it is not surprising that images play the single most important role in human perception. The fields such as computer vision, whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. Image processing holds the possibility of developing the ultimate machine that could perform the visual functions of all living beings. This paper presents methodology for identification and classification of bulk sugary food objects. Comprising of south indian typical sweets Mysorepak, Bundeladu, Ladakiladu, Applecake, Jilebi, Suraliholige, Burfi, Kalakand, Jamun, and Doodhpeda. When these sweets are arranged for display at the shops exhibit different patterns. And hence, texture is the basis used for recognition. The texture features are extracted using gray level co-occurrence matrix method. The multilayer feed forward neural network is established to classify bulk sugary food objects. Results achieved with a fair set of test images are presented. An analysis of the efficiency of methodology is 90%.
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