A
Amer Dawoud
Researcher at University of Southern Mississippi
Publications - 23
Citations - 279
Amer Dawoud is an academic researcher from University of Southern Mississippi. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 9, co-authored 22 publications receiving 257 citations. Previous affiliations of Amer Dawoud include University of Waterloo & University of South Alabama.
Papers
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Journal ArticleDOI
Target tracking in infrared imagery using weighted composite reference function-based decision fusion
TL;DR: A novel decision fusion algorithm for target tracking in forward-looking infrared image sequences recorded from an airborne platform and the results furnished by competing ego-motion compensation and tracking algorithms are evaluated based on their similarity to a target model constructed using the weighted composite reference function.
Journal ArticleDOI
Iterative multimodel subimage binarization for handwritten character segmentation
Amer Dawoud,Mohamed S. Kamel +1 more
TL;DR: This paper presents a new category, where the image is considered a collection of subimages, where each subimage provides a statistical model for the handwritten characters that can be used to optimize the binarization of other subimages based on gray-level and stroke-run features.
Journal ArticleDOI
Lung segmentation in chest radiographs by fusing shape information in iterative thresholding
TL;DR: An algorithm for the segmentation of lung fields by fusing shape information priors into intensity-based thresholding in an iterative framework to maximise information utilisation by effectively combining intensity information with shape priors is presented.
Journal ArticleDOI
Iterative Cross Section Sequence Graph for Handwritten Character Segmentation
TL;DR: Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms, and improving the structural quality of the characters' skeleton facilitates better feature extraction and classification.
Book ChapterDOI
Fusing shape information in lung segmentation in chest radiographs
TL;DR: An algorithm for the segmentation of lung fields by fusing shape information priors into intensity-based thresholding in an iterative framework to maximize information utilization by effectively combining intensity information with shape priors is presented.