M
Muhammad Haroon Yousaf
Researcher at University of Engineering and Technology
Publications - 95
Citations - 1054
Muhammad Haroon Yousaf is an academic researcher from University of Engineering and Technology. The author has contributed to research in topics: Activity recognition & Computer science. The author has an hindex of 12, co-authored 83 publications receiving 577 citations. Previous affiliations of Muhammad Haroon Yousaf include University of Engineering and Technology, Lahore & National Court Reporters Association.
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Journal ArticleDOI
Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.
TL;DR: In contrast with state of the art systems, the RCNN is capable to compute deep features with amen representation of Melanoma, and hence improves the segmentation performance.
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Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour
TL;DR: A melanoma detection and segmentation approach is presented that brings significant improvement in terms of accuracy against state-of-the-art approaches and validates the practical bearing of the method in development of clinical decision support system for melanoma diagnosis in contrast to state of theart methods.
Journal ArticleDOI
Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description
TL;DR: This is the first report of results on the MuHAVi-uncut dataset having a large number of action categories and a large set of camera-views with noisy silhouettes which can be used by future workers as a baseline to improve on and compares well to similar state-of-the-art approaches.
Journal ArticleDOI
Visual analysis of asphalt pavement for detection and localization of potholes
TL;DR: Subjective and objective evaluation of potholes localization results in high recall with relatively good accuracy, however, the objective assessment shows the 91.4% accuracy for localization of pothsoles.
Proceedings ArticleDOI
Computer vision based detection and localization of potholes in asphalt pavement images
TL;DR: This paper addresses the detection and localization of one of the key pavement distresses, the potholes using computer vision and proposes normalized graph cut segmentation scheme, which showed 90 % accuracy for the detection of pothole images and high recall for the localization of pOTHole in the detected images.