Fruit Recognition by Hierarchical Temporal Memory

Abstract

Food is a requirement for living, and traded in enormous amounts everyday. The globalization has led to optimization of the supermarkets and that a lot of stores have introduced self-scanning systems at check out and payment. When articles such as vegetables and fruits are traded the process becomes slower because the packages usually do not wear barcodes, which have to be added manually. This is a problem and the purpose with self-scanning drops out. In this thesis, a recognition system is built with the purpose to be used in self-scanning systems. The system thresholds the original image into a binary image. The binary image is sent to an advanced type of Neural Network called Hierarchical Temporal Memory. Such a network is independent of color, size, spatial space and rotations. These properties make it suitable for the given task. Two sorts of fruits were tested and the algorithm gave the accurate prediction in 97.5 % when tested on previously unseen images.