Research

Automated detection of malaria parasites using convolutional neural networks

Researchers from Imperial College London recently focused on developing an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving.

Automatic recognition of emotional subgroups in images

Researchers from Vrije Universiteit Amsterdam are advancing better ways to combine social group detection and group emotion recognition in images especially for use cases such as crowd surveillance or event analysis.

A hierarchical optical coherence tomography annotation workflow with crowds and medical experts

Researchers from Genentech are researching solutions to the heavy burden it takes to annotate clinical images due to the cost and availability of medical experts.

Evaluating terrain-dependent performance for martian frost detection in visible satellite observations

Researchers from Jet Propulsion Labs recently studied seasonal frosting and defrosting on the surface of Mars which is hypothesized to drive both climate processes and the formation and evolution of geomorphological features such as gullies.

The impact of artificial intelligence assessment on diabetic retinopathy

Researchers from the University of Rwanda studied the recent advances in computer-based analysis using artificial intelligence (AI), which present a promising opportunity to test and refine automatic grading of diabetic retinal images for screening.

Baby physical safety monitoring in smart home using action recognition system

Researchers from the University of Cincinnati recently studied advances in action recognition, as humans are able to intuitively deduce actions that took place between two states in observations via deductive reasoning.

Deep learning for live cell shape detection

Researchers from Iowa State University were looking to advance atomic force microscopy (AFM) in order to provide a platform for high-resolution topographical imaging.

HyperionSolarNet: Solar panel detection from aerial images

Researchers from UC Berkeley set out to create a comprehensive database for locating solar panels given the importance of being able to assist analysts and policymakers on defining strategies for further expansion of solar energy.

Multi-view deep learning for reliable post-disaster damage classification

Researchers from Texas A&M University, recently researched ways to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery.