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Articles

Vol. 3 No. 3 (2024): December Edition 2024

A Hybrid Approach Combining Computer Vision and Machine Learning for Enhanced Image Analysis

Submitted
October 13, 2024
Published
2024-10-13

Abstract

In recent years, the convergence of computer vision (CV) and machine learning (ML) has led to significant advancements in image analysis, allowing for more sophisticated and accurate extraction of information from visual data. This research paper presents a hybrid approach that integrates both computer vision and machine learning techniques to improve the accuracy, efficiency, and robustness of image analysis systems. We explore the synergy between feature-based CV methods and data-driven ML algorithms, focusing on their application in object detection, image classification, and pattern recognition tasks. Our approach leverages traditional image processing techniques alongside advanced machine learning models like convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees to create a more versatile and adaptive framework for real-world applications such as autonomous driving, medical imaging, and industrial automation.

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