Deep learning approaches to pattern extraction and recognition in ...

Deep Learning Model of Image Classification Using Machine Learning

Compared with the traditional machine learning methods, the deep learning model does not need to rely on manual design and feature extraction.

Deep learning - Wikipedia

Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, ...

Multi-Features Extraction Based on Deep Learning for Skin ... - HAL

Deep learning has recently received much interested in skin lesion classification. Indeed, skin lesion classification methods usually needed the ...

Structural Pattern Recognition - Complexica

By leveraging advanced algorithms like Deep Learning and Automatic High Level Feature Extraction combined with Dynamic Time Warping and Grammatical ...

A Brief History of Deep Learning - DATAVERSITY

Feature extraction is another aspect of deep learning. It is used for pattern recognition and image processing. Feature extraction uses an ...

Investigation of Machine Learning Algorithms for Pattern ...

Abstract: In order to recognize patterns in images, this study tests the performance of many “machine learning algorithms” and feature extraction methods.

What Is Machine Learning (ML)? - IBM

Using unsupervised learning, clustering algorithms can identify patterns in data so that it can be grouped. Computers can help data scientists by identifying ...

Deep Learning Approaches Applied to Remote Sensing Datasets for ...

Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The ...

Machine Learning in Pattern Recognition

The statistical approach is the most popular approach that is practised among the several frameworks where pattern recognition is initially ...

Deep learning for pattern recognition in seismic reflection data

neural network (CNN) to extract ... This dissertation presents deep learning methods for pattern recognition in seismic reflection data from various perspectives.

A Spatial Pattern Extraction and Recognition Toolbox Supporting ...

In recent decades, attempts have been made to utilise machine learning methods such as artificial neural networks (ANNs), to extract features of ...

Deep Learning Techniques for Image Recognition and Object ...

The automated identification and classification of objects or patterns inside digital photographs is known as image recognition. Convolutional neural networks ( ...

Pattern recognition and signal parameters extraction using machine ...

Machine learning methods can be used for signal processing in different cases of physics research. A convolutional neural network was developed for the task of ...

Feature extraction and image classification using OpenCV

There are various features that can potentially be extracted using different machine learning algorithms. Lowe et al. (2004) developed Scale ...

Object Detection: The Definitive 2025 Guide - viso.ai

In general, deep learning-based object detectors extract features from the input image or video frame. An object detector solves two subsequent ...

A Deep Learning Approach to Detecting Objects in Underwater ...

A deep learning approach, also known as deep machine learning or deep structure learning, has recently been found to be successful in ...

Deep learning and transfer learning approaches for image ...

... machine learning systems in image processing, computer vision and pattern recognition. This paper provides a brief survey, beginning with Deep Neural Network ...

Deep Learning for Feature Extraction | Restackio

In conclusion, while handcrafted methods have their place, the advantages of deep learning, particularly CNNs, in feature extraction are evident ...

Deep learning approaches for neural decoding across architectures ...

The convolutional layers will then learn filters of the corresponding dimensions, in order to extract meaningful local structure (Figure 1C). The convolutional ...

A deep learning approach to photo–identification demonstrates high ...

This includes identities already in the catalogue as well as new individuals, which is known as an open–set recognition problem (Deng et al., ...