Department of Electrical & Computer Engineering
Signal and Image Laboratory (SaIL)
The University of Arizona®
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Current Research |
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Performance Evaluation of Image Segmentation Metrics: Sree Ramya S. P. Malladi
Automatic image segmentation can incur several types of error: under-segmentation, over-segmentation, and boundary mismatch. This research aims to extensively study and analyze metrics designed to quantitatively describe the errors that occur in automatic image segmentation with respect to the ground-truth segmentation.
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Object Detection and Tracking: Rohit Chacko Philip, Xin Gao, Sundaresh Ram
Detection and tracking of an object in a video is a crucial and complex problem for applications such as video surveillance, traffic monitoring and tracking organs in medical images. There are many factors that make object detection and tracking a challenge task, such as large camera motion, low contrast between objects with backgrounds, illumination variation, clutter and occlusion. This research project is aimed at developing an automated system capable of detecting and tracking objects in low-resolution wide area imagery.
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Rock Image Segmentation and Classification: Ramya Malladi, Sundaresh Ram
Accurate rock size distribution is important for production blasting in order to control and minimize the overall production costs. They are typically measured by using sieves, which are time consuming and expensive, and do not provide information that can be used for online control or process improvement. In this research work we are aimed at developing a novel image segmentation algorithm capable of segmenting each rock particle separately in order to provide accurate rock size distribution.
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