A binary image in image processing is an image where each pixel has one of only two possible values, typically black and white.
Understanding Binary Images
Binary images, also known as bilevel or two-level images, are fundamental in various image processing tasks. Instead of representing colors or shades of gray with multiple bits per pixel, each pixel is represented by a single bit. This bit indicates either the foreground (typically white or 1) or the background (typically black or 0).
Key Characteristics
- Two Colors: Consists of only two colors, usually black and white, although other color pairs can be used in some contexts.
- Single Bit Representation: Each pixel requires only one bit of storage.
- Simplicity: Simplifies image analysis due to the reduced information.
Importance in Image Processing
Binary images play a crucial role in several image processing applications:
- Segmentation: Facilitates easy separation of objects from the background. The process of converting a grayscale or color image into a binary image is often a crucial step in image segmentation.
- Object Detection: Simplifies the identification of objects by reducing image complexity.
- Optical Character Recognition (OCR): Used for recognizing text in scanned documents.
- Thresholding: Often created through thresholding techniques applied to grayscale images. Thresholding is the process of converting a grayscale image to a binary image by setting all pixels whose intensity values are above a certain threshold to one value (e.g., white) and all other pixels to another value (e.g., black).
Examples
Imagine a scanned document. Converting this document to a binary image allows you to clearly distinguish the text (foreground) from the paper (background). Another common use is in barcode recognition, where the clear distinction between black and white bars allows for easy decoding.
Conversion to Binary Images
Binary images are often created from grayscale or color images using techniques like:
- Thresholding: Setting a threshold value; pixels above the threshold become white, and those below become black.
- Edge Detection: Identifying edges in an image and representing them as white pixels on a black background.
