no code implementations • 13 Apr 2022 • Ranju Mandal, Basim Azam, Brijesh Verma
However, deep learning models are not fully capable of exploiting visual and contextual information simultaneously.
no code implementations • 13 Apr 2022 • Ranju Mandal, Basim Azam, Brijesh Verma, Mengjie Zhang
The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models.
no code implementations • 24 Feb 2018 • Ligang Zhang, Brijesh Verma, Dian Tjondronegoro, Vinod Chandran
Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction.
no code implementations • 24 Feb 2018 • Ligang Zhang, Brijesh Verma, David Stockwell, Sujan Chowdhury
Semantic context is an important and useful cue for scene parsing in complicated natural images with a substantial amount of variations in objects and the environment.
no code implementations • 24 Feb 2018 • Ligang Zhang, Brijesh Verma
In this paper, we present a novel approach that generates class-semantic color-texture textons and aggregates superpixel based texton occurrences for vegetation segmentation in natural roadside images.
no code implementations • 21 Feb 2018 • Ligang Zhang, Brijesh Verma, David Stockwell, Sujan Chowdhury
Accurate estimation of the biomass of roadside grasses plays a significant role in applications such as fire-prone region identification.