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An introduction to digital image processing
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The article was published on 1986-01-01 and is currently open access. It has received 1745 citations till now. The article focuses on the topics: Digital image processing & Image processing.read more
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Journal ArticleDOI
An interactive and multi-functional refreshable Braille device for the visually impaired
Fatih Başçiftçi,Ayşe Eldem +1 more
TL;DR: A readable, vocalized and refreshable Braille device, which can ease the lives of visually impaired people, has been developed and was superior both in terms of features and 35% cheaper compared to the ones available on the market.
Object and Event Extraction for Video Processing and Representation in On-Line Video Applications
TL;DR: The goal of this thesis is to provide a stable content-based video representation rich in terms of generic semantic features and moving objects, and three processing levels are proposed: video enhancement to estimate and reduce noise, video analysis to extract meaningful objects and their spatio-temporal features, and video interpretation to extract context-independent semantics.
Journal ArticleDOI
How does co-registration affect geomorphic change estimates in multi-temporal surveys?
Sara Cucchiaro,Eleonora Maset,Marco Cavalli,Stefano Crema,Lorenzo Marchi,Alberto Beinat,Federico Cazorzi +6 more
TL;DR: In this article, high-resolution topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs).
Journal ArticleDOI
Sunlit soil surface extraction from remotely sensed imagery of perennial, discontinuous crop areas; the case of Mediterranean vineyards
TL;DR: In this article, a multi-scale segmentation method was proposed to extract the pure and sunlit soil surface from very high spatial resolution imagery, in order to enable the comparison of the resolution element's spectral properties with known bi-directional properties of different types of soil surface.
Proceedings ArticleDOI
Generative Adversarial Networks for Road Crack Image Segmentation
TL;DR: A road crack segmentation method based on generative adversarial networks (GAN) that can be used to segment the road crack images and achieves better performance than other state-of-the-art methods on three datasets.
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