Search Results for author:
Found 7 papers, 1 papers with code
Unlocking Layer-wise Relevance Propagation for Autoencoders
Autoencoders are a powerful and versatile tool often used for various problems such as anomaly detection, image processing and machine translation.
Geometry aware convolutional filters for omnidirectional images representation
In particular we propose an algorithm that adapts convolutional layers, which often serve as a core building block of a CNN, to the properties of omnidirectional images.
Isometric Transformation Invariant Graph-based Deep Neural Network
In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images.
Noise generation for compression algorithms
In various Computer Vision and Signal Processing applications, noise is typically perceived as a drawback of the image capturing system that ought to be removed.
Graph-Based Classification of Omnidirectional Images
Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view.
Graph-based Isometry Invariant Representation Learning
Learning transformation invariant representations of visual data is an important problem in computer vision.
Multi-modal image retrieval with random walk on multi-layer graphs
The analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data.
