What is it about?
This study explores the integration of the Hough Transform (HT) with Convolutional Neural Networks (CNNs) to improve pattern recognition accuracy in image collections, specifically focusing on detecting straight lines. The Hough Transform, a classical image processing technique, is used to preprocess images by enhancing geometric features, while CNNs leverage deep learning to classify these features effectively.
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Why is it important?
Experimental results demonstrated that the integration of ESHT with CNNs improved classification accuracy. The model achieved 70% accuracy with ESHT preprocessing, compared to 66.67% without it. The confusion matrix, precision, recall, and F-measure metrics confirmed the enhanced performance, particularly in reducing false positives and negatives. Visual comparisons (Figures 3 and 4) showed clearer detection of lines in ESHT-processed images.
Perspectives
Limitations included a small, simulated dataset and hardware constraints (e.g., 32x32 image size due to memory limitations). Future directions include diversifying datasets, comparing the model with other neural networks, and exploring applications in fields like transportation.
Moussa BAMOGO
Universite Polytechnique de Bobo-Dioulasso
Read the Original
This page is a summary of: An Application of the Hough Transform and Convolutional Neural Networks to Detect Straight Lines, January 2025, Springer Science + Business Media,
DOI: 10.1007/978-3-031-86493-3_10.
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