Academic Articles & Research Papers

Enhanced gradient learning for deep neural networks

Abstract Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradie...

Non-Destructive Monitoring of External Quality of Date Palm Fruit (<i>Phoenix dactylifera</i> L.) During Frozen Storage Using Digital Camera and Flatbed Scanner

The emergence of new technologies focusing on “computer vision” has contributed significantly to the assessment of fruit quality. In this study, an innovative approach based on image analysis was used to assess the external quality of fresh...

Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM

Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, on...

Computer Vision: Evolution And Promise

In this paper we give a somewhat personal and perhaps biased overview of the field of Computer Vision.First, we define computer vision and give a very brief history of it.Then, we outline some of the reasons why computer vision is a very di...

A review of convolutional neural networks in computer vision

Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of dee...

The divergent 3D genome landscapes of aging and neurodegenerative mouse models.

Enhancing image caption generation through context-aware attention mechanism

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research ha...

Handbook of computer vision and applications

Contents of Volume One: Preface. Contributors. B. Jahne, Introduction. Illumination and Image Formation. H. Hausecker, Radiation. H. Hausecker, Interaction of Radiation with Matter. P. Geisler, Geometric and Wave Optics. H. Hausecker, Radio...

Geometric modeling for computer vision.

Abstract : The main contribution of this thesis is the development of a three dimensional geometric modeling system for application to computer vision. In computer vision geometric models provide a goal for descriptive image analysis, an or...

FIR-SDE: fast image restoration via mean-reverting stochastic differential equation

Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data...

YOLOv1 to v8: Unveiling Each Variant&#x2013;A Comprehensive Review of YOLO

This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural compone...

Computer Vision

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the worl...

Computer vision: algorithms and applications

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old ...

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control appli...

Neural Networks for River Flow Prediction

The surface‐water hydrographs of rivers exhibit large variations due to many natural phenomena. One of the most commonly used approaches for interpolating and extending streamflow records is to fit observed data with an analytic power model...

An Overview of Neural Network

Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in ...

Bayesian Regularization of Neural Networks

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...

Essence of Neural Networks

* Introduction * Classifying Patterns * Clustering Patterns * Pattern Association * A Sample of Recurrent Networks * Some Other Network Models and A Few Practical Points * Links to Artificial Intelligence * Synthesising Symbols with Neural ...

Artificial neural networks: a tutorial

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the arti...

Determining the Optimum Layer Combination for Cross-Laminated Timber Panels According to Timber Strength Classes Using Artificial Neural Networks

The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN)...