What is blob in computer vision?

In computer vision, a blob refers to a group of connected pixels in an image that share common properties, such as intensity, color, or texture. These regions are often treated as distinct objects or areas of interest. Blob detection is a technique used to identify and analyze such regions, typically by isolating them from the background or other parts of the image. For example, in a grayscale image, a blob might be a bright spot surrounded by darker pixels, like a white circle on a black background. Algorithms like thresholding, edge detection, or the Laplacian of Gaussian (LoG) are commonly used to locate blobs by highlighting areas where pixel values change abruptly or follow a specific pattern.

Blobs are useful in applications where the goal is to segment or count objects. A classic example is detecting coins in an image: each coin might appear as a circular blob with uniform color. Another use case is medical imaging, where blobs could represent tumors in an MRI scan. Tools like OpenCV’s SimpleBlobDetector automate this process by filtering regions based on parameters like size, shape, and intensity. For instance, developers can configure the detector to ignore small noise blobs or merge overlapping regions. Blob analysis is also foundational in industrial automation, such as identifying defects on a manufacturing line by flagging irregular shapes or textures in product images.

However, blob detection has limitations. It struggles with overlapping objects, uneven lighting, or complex backgrounds that obscure clear boundaries. For example, if two cells in a microscope image overlap, a basic blob detector might incorrectly classify them as a single object. To address this, preprocessing steps like noise reduction, contrast adjustment, or morphological operations (e.g., erosion/dilation) are often applied. Additionally, deep learning methods like semantic segmentation have begun to complement traditional blob detection by handling more nuanced scenarios. Despite these advancements, blob-based approaches remain valuable for their simplicity and efficiency in well-defined tasks, such as counting objects in controlled environments or tracking movement in video frames.