What is Blob Detection?

Blob detection is the process of finding related areas in an image that share features. The size, form, and intensity of these areas, sometimes known as blobs, can change. Generally, the procedure entails thresholding to divide the image into segments, assembling linked pixels into clusters, and examining these clusters to derive important details like their centers, dimensions, and forms. Applications for blob detection include image segmentation, feature extraction, object tracking, and more.

The three techniques available for blob detection are.

1. Laplacian of Gaussian (LoG): Taking a Gaussian-smoothed image’s Laplacian is the method used here. Although the LoG approach can identify blobs of different sizes well, it can return several results for a single blob.
2. Difference of Gaussian (DoG): The DoG algorithm computes the difference between two Gaussian-smoothed pictures. It is handy for recognizing blobs of a given size range. We can vary the standard deviation of the Gaussian kernels to control the size of the detected blobs.
3. Determinant of Hessian (DoH): Blobs are identified via the DoH approach by using the determinant of the Hessian matrix. The local curvature of an image is represented by the Hessian matrix. The local maxima in the Hessian’s determinant can be used to locate blobs of various sizes and forms.

Blob detection is a basic method in computer vision used to locate areas of interest in a picture. These “blobs” frequently depict elements or items that have similar characteristics, like color, texture, or intensity. In this post, we explore the realm of blob identification with OpenCV, an effective computer vision toolkit. In this article, we look at the underlying ideas of blob detection, how OpenCV is used to build it, how to tweak its parameters, some applications, and finally some perspectives on its relevance across a range of domains.