Advanced image processing techniques for intelligent building environments using pattern recognition

Abstract

The use of smart building environments, along with high-technology image processing and pattern recognition, is discussed within this paper. The study shows that the Canny edge detection algorithm is better than the Sobel operator in the edge clarity, continuity and accuracy in segmenting those edges, posting 92.7% of edge detection accuracy. Incorporating fuzzy logic, the hybrid Hough transform, and sophisticated segmentation techniques, like adaptive simple linear iterative clustering (SLIC) superpixel division, the study advances line detection and feature identification in the images of buildings. The variational autoencoder (VAE) and principal component analysis (PCA) help optimise the feature extraction substantially by retaining more than 93% variance at a lower dimension. In addition, adaptive Otsu thresholding and region-growing segmentation allow improving the segmentation accuracy, resulting in a significant increase in building detection F1 score from 77.3% to 89.6%. Irrespective of the Hough transform issues like noise sensitivity and over-joining, the results suggest computing process ideas that are computationally effective, scalable, and applicable in smart building systems. This study suggests extending the current advancement of hybrid models and incorporating them with the urban planning procedures, energy control, and building security systems.