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@article{185736,
        author = {Pooja Ukey and Gopal Tantele and Kshitij Jathare and Shreya Bondre and Kush Chandekar and Vinay Bhudke},
        title = {Experimental Analysis of Image Segmentation Using Thresholding Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {3326-3328},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185736},
        abstract = {Image segmentation is a critical step in computer vision, enabling the separation of objects from the background for further analysis. Thresholding is one of the simplest and widely used segmentation techniques due to its computational efficiency and ease of implementation. This experimental study investigates the performance of global and adaptive thresholding methods for segmenting real-world images, including traffic signs and vehicle plates. MATLAB and Python (OpenCV, TensorFlow) were used for implementation. The study evaluates the methods based on accuracy, precision, recall, F1-score, and Intersection over Union (IoU). Experimental results demonstrate that adaptive thresholding significantly outperforms global thresholding in complex scenes with varying illumination, shadows, and noise, while also highlighting the benefits of post-processing with morphological operations.},
        keywords = {},
        month = {October},
        }