Publications
Chosen filters:
Chosen filters:
A deep learning approach to crack detection on road surfaces Ghent University
Currently, modern achievements in the field of deep learning are increasingly being applied in practice. One of the practical uses of deep learning is to detect cracks on the surface of the roadway. The destruction of the roadway is the result of various factors: for example, the use of low-quality material, non-compliance with the standards of laying asphalt, external physical impact, etc. Detection of these damages in automatic mode with high ...
Crack detection in paintings using convolutional neural networks Ghent University
The accurate detection of cracks in paintings, which generally portray rich and varying content, is a challenging task. Traditional crack detection methods are often lacking on recent acquisitions of paintings as they are poorly adapted to high-resolutions and do not make use of the other imaging modalities often at hand. Furthermore, many paintings portray a complex or cluttered composition, significantly complicating a precise detection of ...
Automated visual inspection algorithm for the reflection detection and removing in image sequences Ghent University
Specular reflections are undesirable phenomena that can impair overall perception and subsequent image analysis. In this paper, we propose a modern solution to this problem, based on the latest achievements in this field. The proposed method includes three main steps: image enhancement, detection of specular reflections, and reconstruction of damaged areas. To enhance and equalize the brightness characteristics of the image, we use the ...
A deep learning-based approach for defect detection and removing on archival photos Ghent University
Many archival photos are unique, existed only in a single copy. Some of them are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have physical damage resulting in the appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. This paper proposed a system for detection and removing image defects based on machine learning. The method for detecting damage to an image ...
Variational auto-encoders without graph coarsening for fine mesh learning Ghent University
In this paper, we propose a Variational Auto-Encoder able to correctly reconstruct a fine mesh from a very low-dimensional latent space. The architecture avoids the usual coarsening of the graph and relies on pooling layers for the decoding phase and on the mean values of the training set for the up-sampling phase. We select new operators compared to previous work, and in particular, we define a new Dirac operator which can be extended to ...
A robust dynamic classifier selection approach for hyperspectral images with imprecise label information Ghent University
Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the ...
MRI reconstruction using Markov random field and total variation as composite prior Ghent University
Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and ...
Sketched sparse subspace clustering for large-scale hyperspectral images Ghent University
Sparse subspace clustering (SSC) has achieved the state-of-the-art performance in clustering of hyperspectral images. However, the computational complexity of SSC-based methods is prohibitive for large-scale problems. We propose a large-scale SSC-based method, which processes efficiently large-scale HSIs without sacrificing the clustering accuracy. The proposed approach incorporates sketching of the self-representation dictionary reducing ...