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siritool: A Matlab Graphical User Interface for Image Analysis in Structured-Illumination Reflectance Imaging for Fruit Defect Detection
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 63(4): 1037-1047. (doi: 10.13031/trans.13612) @2020
Authors: Yuzhen Lu, Renfu Lu
Keywords: Defect detection, Demodulation, Image enhancement, Machine learning, Matlab, Structured illumination.
A Matlab GUI, siriTool, was developed for structured-illumination reflectance imaging. siriTool enables image preprocessing, feature extraction, and classification. siriTool was demonstrated for detection of spot defects on pickling cucumbers.
A Matlab GUI, siriTool, was developed for structured-illumination reflectance imaging.
siriTool enables image preprocessing, feature extraction, and classification.
siriTool was demonstrated for detection of spot defects on pickling cucumbers.
Abstract. Structured-illumination reflectance imaging (SIRI) is an emerging imaging modality that provides more useful discriminative features for enhancing detection of defects in fruit and other horticultural and food products. In this study, we developed a Matlab graphical user interface (GUI), siriTool (available at https://codeocean.com/capsule/5699671/tree), to facilitate image analysis in SIRI for fruit defect detection. The GUI enables image preprocessing (i.e., demodulation, object segmentation, and image enhancement), feature extraction and selection, and classification. Demodulation is done using a three-phase or two-phase approach depending on the image data acquired, object segmentation (or background removal) is implemented based on automatic unimodal thresholding, and image enhancement is achieved using fast bi-dimensional empirical decomposition followed by selective image reconstructions. For defect detection, features of different types are extracted from the enhanced images, and feature selection is performed to reduce the feature set. Finally, the full or reduced set of features are then input into different classifiers, e.g., support vector machine (SVM), for image-level classifications. An application example is presented on the detection of yellowish subsurface spot defects in pickling cucumbers. SIRI achieved over 98% classification accuracies based on SVM modeling with the extracted features, which were significantly better than the accuracies obtained under uniform illumination.(Download PDF) (Export to EndNotes)