MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641126256 A) filed by Karpagam Academy Of Higher Education; Karpagam College Of Engineering; Mrs. S. Gayathri; and Sonaimuthu. E on October 23, 2026, for Image Recognition.
Inventors include Mrs. S. Gayathri; and Sonaimuthu. E.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: Image recognition has become a fundamental component of modern intelligent systems, enabling machines to interpret and analyze visual information with high accuracy. The rapid expansion of digital imagery from cameras, mobile devices, and sensor networks has increased the demand for automated and reliable image recognition solutions across various application domains. Traditional image processing techniques, which rely on handcrafted features and rule-based methods, often fail to perform effectively in complex and dynamic environments. To address these limitations, deep learning–based approaches have emerged as a powerful alternative for visual data analysis. This project presents an advanced image recognition system that leverages deep learning techniques, particularly Convolutional Neural Networks (CNNs), to automatically detect, classify, and recognize objects and patterns within digital images. The proposed system integrates all essential stages of image recognition, including image acquisition, preprocessing, feature extraction, classification, and prediction, into a unified and efficient framework. Image preprocessing operations such as resizing, normalization, and noise reduction are applied to enhance image quality and ensure consistency across datasets. The core of the system is a CNN-based feature extraction model capable of learning hierarchical and discriminative features directly from raw image data. Unlike conventional approaches that require manual feature engineering, the proposed method enables automatic learning of complex visual patterns through multiple layers of convolution and pooling. The network is trained using a large labeled dataset, allowing the model to effectively differentiate between various object categories. To further improve performance and prevent overfitting, data augmentation techniques such as rotation, flipping, and scaling are employed, along with optimization and regularization strategies. After training, the system is evaluated using unseen images to measure its accuracy, robustness, and generalization capability. The trained model demonstrates reliable performance in recognizing objects under varying conditions, including changes in lighting, orientation, and background. The prediction module supports real-time or near real-time inference, making the system suitable for practical deployment scenarios. The proposed image recognition system is designed to be scalable and adaptable, allowing it to be customized for different applications without significant modifications to the core architecture. Its modular design facilitates integration with existing software platforms and hardware environments, including cloud-based infrastructures and edge devices. This flexibility enhances the usability of the system across diverse operational settings. Applications of the proposed system span multiple domains, including automated surveillance, healthcare image analysis, robotics, augmented reality, and industrial automation. By providing improved accuracy, efficiency, and adaptability, the system contributes to the development of intelligent visual recognition solutions capable of supporting automated decision-making processes. Overall, this project demonstrates the effectiveness of deep learning–based image recognition and highlights its potential to transform real-world systems through enhanced visual understanding
Disclaimer: Curated by HT Syndication.
