Academic Articles & Research Papers

A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction

Neural networks have been used in various computer vision applications, including noise removal. However, removing seismic noise via deep learning approaches faces a specific issue: the scarcity of labeled data. To address this difficulty, ...

Computer vision applications in construction safety assurance

Emerging Topics in Computer Vision

The state-of-the art in computer vision: theory, applications, and programmingWhether you're a working engineer, developer, researcher, or student, this is your single authoritative source for today's key computer vision innovations. Gerard...

Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by ...

A Review on Conventional Machine Learning vs Deep Learning

In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original ...

Empirical Evaluation of Invariances in Deep Vision Models

The ability of deep learning models to maintain consistent performance under image transformations-termed invariances, is critical for reliable deployment across diverse computer vision applications. This study presents a comprehensive empi...

Deep Learning in Neuroradiology

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applic...