This study investigates the application of artificial intelligence (AI) algorithms for robust shape recognition in noisy binary images, addressing challenges in medical imaging (e.g., organ segmentation in MRI scans), industrial inspection (e.g., defect detection in automotive parts), and remote sensing (e.g., object identification in satellite imagery). Three AI-based methods—Hough Transform (HT), Fourier Descriptors (FD), and Zernike Moments (ZM)—were implemented and evaluated using Python-based tools (OpenCV, Mahotas). Experimental results demonstrate that Zernike Moments achieve the highest accuracy (95%) in high-noise conditions, Fourier Descriptors excel in reconstructing complex contours, and Hough Transform is fastest for detecting basic geometric shapes. A hybrid approach integrating these methods with deep learning, such as Convolutional Neural Networks (CNNs), is proposed to enhance accuracy and scalability.

Shape Recognition, Noisy Images, Hough Transform, Fourier Descriptors, Zernike Moments, Image Processing, Python, Pattern Recognition, Convolutional Neural Networks.

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