Content-based Feature and Decision Tree for Bread Recognition System
Date Issued
2014
Date
2014
Author(s)
Chang, Chin-Sheng
Abstract
Nowadays most merchandise checkout systems are used by way of scanning each items barcode. However, fresh food such as meat, seafood, bread, and vegetables cannot be scanned as easily since they usually do not come with a barcode. The handling of these products often require a clerk’s assistance. For example, fresh bread that is being cooled cannot be stored in the plastic bags right away, therefore making it difficult to attach a barcode label to the item. As a result, an image processing aided system would be a suitable solution to assist with issues related to barcode labeling, while also helping to improve overall checkout efficiency.
In this thesis, we propose an intelligent bread recognition system (IBRS) by using photographic equipment to capture bread images. The system includes three main modules: original image preprocessing, content-based feature extraction and decision tree classification. The input images will be first segmented and processed into binary images. Then, the geometry, color, and shape feature will be extracted from the preprocessed images. After that, the extreme range of features will be analyzed, and the best attribute will be chosen through calculating the entropy. Finally, the system will generate a decision tree classification model, which will then be used to identify the input bread images. The experiment indicates that the classification for 48 kinds of bread samples resulted in 93.75% accuracy. Each test used 30 samples for training and 10 samples for testing. As a result, our system can enhance checkout efficiency and reduce labor costs. In the future, the process architecture can be applied to Objection Recognition field.
Subjects
麵包辨識
影像前處理
圖像內容特徵擷取
決策樹分類
Type
thesis
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