29 citations · 111 across the 18 of their papers we have counts for
22 papers
Intrinsic Image Decomposition Using Point Cloud Representation
Xiaoyan Xing, Konrad Groh, Sezer Karaoglu +1
The purpose of intrinsic decomposition is to separate an image into its albedo (reflective properties) and shading components (illumination properties). This is challenging because…
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
Qi Bi, Shaodi You, Theo Gevers
Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized semantic predictions across diverse urban-scene styles. Unlike domain gap challenges, USSS is…
MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs
Rick Groenendijk, Leo Dorst, Theo Gevers
Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends…
SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes
Partha Das, Sezer Karaoglu, Arjan Gijsenij +1
Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints…
PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition
Partha Das, Sezer Karaoglu, Theo Gevers
Intrinsic image decomposition is the process of recovering the image formation components (reflectance and shading) from an image. Previous methods employ either explicit priors to…
Generative Models for Multi-Illumination Color Constancy
Partha Das, Yang Liu, Sezer Karaoglu +1
In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for…