4 citations · 7 across the 5 of their papers we have counts for
8 papers
Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
Grigorios Kalliatakis, Anca Sticlaru, George Stamatiadis +4
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than r…
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Bruno Ferrarini, Shoaib Ehsan, Ales Leonardis +2
Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.…
Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?
Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli +3
After setting the performance benchmarks for image, video, speech and audio processing, deep convolutional networks have been core to the greatest advances in image recognition tas…
Evaluating Deep Convolutional Neural Networks for Material Classification
Grigorios Kalliatakis, Georgios Stamatiadis, Shoaib Ehsan +4
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieve…
Automatic Selection of the Optimal Local Feature Detector
Bruno Ferrarini, Shoaib Ehsan, Naveed Ur Rehman +2
A large number of different feature detectors has been proposed so far. Any existing approach presents strengths and weaknesses, which make a detector optimal only for a limited ra…
A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors
Shoaib Ehsan, Adrian F. Clark, Ales Leonardis +2
Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed. The interest…