M
Majid Mirmehdi
Researcher at University of Bristol
Publications - 247
Citations - 6104
Majid Mirmehdi is an academic researcher from University of Bristol. The author has contributed to research in topics: Image segmentation & Active contour model. The author has an hindex of 38, co-authored 237 publications receiving 5523 citations. Previous affiliations of Majid Mirmehdi include City University London & Vision Institute.
Papers
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Journal ArticleDOI
Real-Time Detection and Recognition of Road Traffic Signs
Jack Greenhalgh,Majid Mirmehdi +1 more
TL;DR: The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 20 frames per second, and recognizes all classes of ideogram-based (nontext) traffic symbols from an online road sign database.
Journal ArticleDOI
Automated identification of diabetic retinal exudates in digital colour images.
TL;DR: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem.
Book
Handbook of Texture Analysis
TL;DR: This collection of chapters brings together in one handy volume the major topics of importance, and categorizes the various techniques into comprehensible concepts of texture analysis.
Journal ArticleDOI
Segmentation of color textures
Majid Mirmehdi,Maria Petrou +1 more
TL;DR: The process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution.
Journal ArticleDOI
Bridging e-Health and the Internet of Things: The SPHERE Project
Ni Zhu,Tom Diethe,Massimo Camplani,Lili Tao,Alison Burrows,Niall Twomey,Dritan Kaleshi,Majid Mirmehdi,Peter A. Flach,Ian J Craddock +9 more
TL;DR: An overview of this rapidly growing body of work on sensing systems in the home, as well as the implications for machine learning are presented, with an aim of uncovering the gap between the state of the art and the broad needs of healthcare services in ambient assisted living.