Computer vision blob detector

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Computer vision blob detector

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Blob detection is a fundamental technique in computer vision used to identify regions of interest in an image that differ in properties such as brightness or color compared to their surroundings. These regions, often referred to as "blobs," typically represent objects or key features within the scene.

Blob detectors work by analyzing variations in pixel intensity or texture across an image. Common approaches include:

Laplacian of Gaussian (LoG) – Applies a Gaussian smoothing filter followed by a Laplacian operator to highlight regions with rapid intensity changes. The scale of the blob is controlled by adjusting the Gaussian kernel size.

Difference of Gaussians (DoG) – A computationally efficient approximation of LoG, where blobs are detected by subtracting two differently blurred versions of the image.

Determinant of Hessian (DoH) – Uses second-order partial derivatives to locate blobs, often more robust for scale-invariant detection.

Blob detection is widely used in applications like object tracking, medical imaging (e.g., detecting tumors), and industrial inspection (e.g., identifying defects). Advanced methods may incorporate machine learning to improve accuracy in complex scenes.

A key challenge is distinguishing relevant blobs from noise, often requiring post-processing steps like thresholding or size filtering. Modern implementations leverage optimized libraries (e.g., OpenCV) to handle real-time processing efficiently.