Shadow detection and removal from images using machine learning and morphological operations

Abstract

In this study, the authors present a system for shadow detection and removal from images using machine learning technique. A machine learning algorithm ESRT (enhanced streaming random tree) model is proposed. The image is converted to HSV and 26 parameters are taken as image measurements. A dataset in Attribute Relation File Format is created for shadow and non-shadow images. The algorithm is trained using the training dataset and tested using the test dataset. Segmentation is performed. The similar threshold homogeneity pixel is grouped. Colour chromaticity is used to remove cast shadow. Morphological processing is performed to remove the shadow from the image. The algorithm shows better detection rate and accuracy compared with Bayesian classifiers available in WEKA.

Keywords

• object detection
• learning (artificial intelligence)
• Bayes methods
• image colour analysis
• image segmentation
• image morphing
• random processes
• trees (mathematics)
• nonshadow images
• cast shadow
• morphological operations
• shadow detection
• machine learning technique
• image measurements
• shadow removal
• ESRT
• attribute relation file format
• shadow images
• image segmentation
• colour chromaticity
• WEKA
• Bayesian classifiers
• enhanced streaming random tree
• HSV