what is blob in computer vision

In computer vision, a blob refers to a connected region of pixels in a binary image with similar properties or characteristics. The term "blob" is derived from "binary large object." The process of blob analysis typically involves the following steps:

  1. Thresholding:

  2. Convert the input image to a binary image by applying a threshold. Pixels with intensities above a certain threshold are set to one value (e.g., white), while those below the threshold are set to another value (e.g., black).

  3. Connected Component Labeling:

  4. Identify and label connected regions in the binary image. Pixels that are part of the same connected region are assigned the same label. This step helps in grouping together pixels that form a coherent blob.

  5. Blob Filtering:

  6. Optionally, filter out blobs based on certain criteria. This may involve discarding blobs that are too small or too large, depending on the specific application requirements.

  7. Feature Extraction:

  8. Extract relevant features from each blob, such as its centroid, area, perimeter, and other properties. These features provide quantitative information about the characteristics of the blobs.

  9. Blob Analysis:

  10. Analyze the extracted features to make decisions or gain insights based on the properties of the blobs. This analysis may involve tasks like object recognition, tracking, or classification.

Blob analysis is commonly used in computer vision applications for tasks such as object detection, image segmentation, and pattern recognition. The ability to isolate and analyze distinct regions in an image based on their connectivity and properties makes blob analysis a valuable technique in image processing.