Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1209-1215· 0 citations
TL;DR
By combining intelligent image analysis with automated learning and enhancement mechanisms, the Image Processing Using Machine Learning system aims to provide a reliable, efficient, and scalable solution suitable for healthcare diagnostics, surveillance systems, multimedia applications, autonomous vehicles, satellite image analysis, agricultural monitoring, and industrial quality inspection systems.
Abstract
Image Processing Using Machine Learning is an intelligent Artificial Intelligence-based system designed to analyze,
process, classify, and enhance digital images using Machine Learning and Deep Learning techniques. In today's digital era,
enormous amounts of image data are generated from medical imaging systems, surveillance cameras, social media platforms,
satellite systems, and industrial applications. Traditional image processing methods often depend on manual feature extraction
and predefined algorithms, which face limitations in terms of accuracy, scalability, automation, and real-time performance. This
project addresses these challenges by providing an intelligent and automated image processing framework that utilizes Artificial
Intelligence, Machine Learning, and Computer Vision techniques.The system processes images through multiple stages,
including image acquisition, preprocessing, feature extraction, classification, segmentation, and image enhancement to generate
meaningful and accurate outputs. It analyzes image characteristics such as color, texture, edges, and shapes to identify patterns
and make intelligent decisions. The proposed system integrates modern technologies including Artificial Intelligence, Machine
Learning algorithms, Deep Learning models, Convolutional Neural Networks (CNNs), Computer Vision techniques, image
enhancement methods, and database management systems to ensure high accuracy, efficiency, scalability, and real-time
processing capabilities.By combining intelligent image analysis with automated learning and enhancement mechanisms, the
Image Processing Using Machine Learning system aims to provide a reliable, efficient, and scalable solution suitable for
healthcare diagnostics, surveillance systems, multimedia applications, autonomous vehicles, satellite image analysis,
agricultural monitoring, and industrial quality inspection systems.
An organized and perceptive overview of computer vision's present situation and promise in the deep learning age is offered, with an emphasis on important architectures including Convolutional Neural Networks, Vision Transformers, and new hybrid models.
The proposed framework provides reproducible reference results for evaluating supervised machine learning approaches to defective pixel detection while analyzing the influence of feature extraction window size on classification performance and reconstructed image quality.
Bárbaro M. López-Portilla, Kristian Balzer, Lorena Carballo et al.· Applied Sciences· 0 citations
The rapid growth of image processing and artificial intelligence applications has resulted in the generation of extremely high-dimensional datasets. Images obtained from medical imaging systems, satellite sensors, surveillance systems, and industrial inspection devices contain a large number of features, many of which are redundant, irrelevant, or noisy. High-dimensional data increases computational complexity, memory consumption, training time, and the risk of overfitting in machine learning and deep learning models. To address these issues, dimensionality reduction techniques are widely used as preprocessing methods to reduce the number of input features while preserving meaningful information. This review paper presents an in-depth analysis of dimensionality reduction techniques used in image processing applications. Both feature selection and feature extraction methods are discussed, including Correlation-Based Feature Selection, Forward Feature Selection, Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Empirical Mode Decomposition (EMD). Furthermore, modern deep learning-based dimensionality reduction approaches such as Autoencoders [8] and Convolutional Neural Networks (CNNs) are reviewed. A comparative performance evaluation is provided based on classification accuracy, computational efficiency, and robustness to noise, scalability, and suitability for various image processing tasks. The paper also highlights the role of dimensionality reduction in medical image diagnosis, face recognition, remote sensing, and object detection systems.
Raghunadh Pasunuri, Rajaram Jatothu· International Journal of Adv...· 0 citations
Traffic sign detection and recognition have become essential components of intelligent transportation systems and
Advanced Driver Assistance Systems (ADAS) due to the increasing need for road safety and automated driving. Conventional
traffic sign recognition approaches based on handcrafted features and traditional image processing techniques often struggle to
achieve high accuracy under varying environmental conditions such as poor lighting, occlusions, motion blur, and complex
backgrounds. To overcome these limitations, this work presents a Traffic Sign Detection and Recognition System Using
Convolutional Neural Networks (CNN), designed to accurately detect and classify traffic signs from input images. The proposed
framework utilizes computer vision techniques for image preprocessing, including resizing, normalization, and image
enhancement, followed by deep learning-based feature extraction and classification using a Convolutional Neural Network
(CNN). The CNN automatically learns discriminative visual features such as shapes, colors, and patterns from traffic sign
images, eliminating the need for manual feature engineering. The system is trained and evaluated using the German Traffic
Sign Recognition Benchmark (GTSRB) dataset, which contains more than 50,000 labeled images belonging to 43 different
traffic sign classes. A user-friendly interface is developed using Streamlit, enabling users to upload traffic sign images or capture
images through a webcam for real-time prediction. The trained model classifies the detected traffic sign and displays the
predicted class along with the confidence score. Experimental results are evaluated using Accuracy, Precision, Recall, F1-Score,
Confusion Matrix, and Training Performance Metrics, demonstrating the effectiveness of the proposed CNN-based framework
for accurate and reliable traffic sign recognition. The developed system contributes to improving road safety and can be
effectively integrated into intelligent transportation systems, driver assistance technologies, and autonomous vehicle
applications.
Yalla Lokesh Kumar, Dr. T. Siva Ramakrishna· International Journal for Re...· 0 citations
Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy, making it suitable for applications in digital forensics, media verification, and cybersecurity.
J. Kollu, Mortha Pavan, Putta Vardhan et al.· International Journal of Inn...· 0 citations
Artificial intelligence has significantly improved digital image editing capabilities, making it increasingly difficult to distinguish authentic images from manipulated ones [5, 7]. This paper proposes a Vision Transformer (ViT)-based framework for digital image forgery detection and localization by leveraging global contextual feature learning [4]. Unlike conventional Convolu-tional Neural Networks (CNNs), Vision Transformers capture long-range dependencies through self-attention mechanisms, enabling more effective identification of manipulated regions [4, 9]. The proposed framework performs image preprocessing, patch extraction, positional encod-ing, transformer-based feature learning, binary classification, and forgery localization. The model is evaluated using publicly available benchmark datasets, including CASIA V2, Co-MoFoD, and FaceForensics++ [20, 48], and its performance is assessed using Accuracy, Pre-cision, Recall, F1-score, Area Under Curve (AUC), Intersection over Union (IoU), and Pixel Accuracy [17, 49]. Experimental results demonstrate that the proposed Vision Transformer framework outperforms conventional CNN-based methods in terms of detection accuracy and localization precision [16, 19]. The proposed approach provides a robust and scalable solution for modern digital image forensics [15] and can be extended to hybrid transformer architectures and video forgery detection in future work.
G. Mary Pushpa, Dr. K. Sravan Adbhilash· International Journal of Lat...· 0 citations