A Survey of Blob Detection Methods: Techniques, Evaluation, and Applications

Abstract

Blob detection is a critical task in image processing, encompassing a variety of techniques aimed at identifying regions in an image that differ in properties, such as brightness or color, compared to surrounding areas. This survey provides a comprehensive overview of the most prominent blob detection methods, categorizing them into differential, region-based, and watershed-based approaches. Each method is evaluated based on criteria such as computational complexity, robustness, and accuracy. The differential approach includes techniques like the Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG), known for their precision in identifying blob-like structures. This survey also delves into the applications of blob detection across various fields such as medical imaging, where it aids in detecting tumors and other abnormalities, and in computer vision for object recognition and tracking. By comparing and contrasting these methods, this paper aims to guide researchers and practitioners in selecting the most suitable blob detection technique for their specific application needs. Future directions in blob detection research are discussed, highlighting the potential of machine learning and deep learning approaches to enhance detection accuracy and efficiency.