SEGMENTATION

Segment-then-segment : context-preserving crop-based segmentation for large biomedical images

Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We …

A robust dynamic classifier selection approach for hyperspectral images with imprecise label information

Context-aware patch-based image inpainting using Markov random field modeling

In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure …

Real-time detection of traffic events using smart cameras

With rapid increase of number of vehicles on roads it is necessary to maintain close monitoring of traffic. For this purpose many surveillance cameras are placed along roads and on crossroads, creating a huge communication load between the cameras …

Passive error concealment for wavelet-coded I-frames with an inhomogeneous Gauss-Markov random field model

In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to …