Introduction to Digital Images
Last updated
Last updated
To demonstrate the process of image interpretation by computers, the following example outlines a simplified approach. Computers do not perceive images in the same way humans do; instead, they interpret images as structured data. The process can be summarized as follows:
Acquired Image (Color Image) The process begins with capturing an image, such as a coffee bean. The acquired image is typically in color, where each pixel contains numerical values representing the intensities of red, green, and blue (RGB). These values serve as the foundation for further data interpretation by the computer.
Simplified Image (Black and White) In this example, the color image is simplified into a binary (black-and-white) format for ease of processing. Each pixel is assigned a value of either "true" (white) or "false" (black) based on a threshold criterion. This simplification preserves the essential structure of the image while reducing complexity, making it suitable for identifying key features or patterns.
Array of Numbers (Matrix of Values)
The binary image is then transformed into a matrix of numerical values. Each pixel is represented as a number, 1 corresponding to white and 0 to black. This numerical representation enables the computer to apply algorithms for pattern recognition, feature detection, or further analysis.
Computers represent images as structured data in the form of numerical matrices or tensors. Each pixel in an image corresponds to a numerical value that defines its intensity. For grayscale images, pixel values typically range from 0 to 255, where 0 represents black, 255 represents white, and intermediate values represent varying shades of gray. These numerical values are organized into a 2D tensor, with each row and column corresponding to the spatial arrangement of the pixels in the image.
This representation enables advanced image analysis by leveraging mathematical operations on the matrix. Features such as edges, shapes, or patterns can be detected using algorithms that analyze the relationships between pixel values. When working with colored images, the matrix expands into a 3D tensor, adding a third axis for the red, green, and blue (RGB) color channels.
This structured representation of images is foundational in computer vision applications, allowing systems to efficiently process, analyze, and extract meaningful insights from visual data.
Color images are composed of combinations of three primary colors: Red (R), Green (G), and Blue (B). These primary colors form the basis of the RGB Color Space. By mixing these colors in varying intensities, a broad spectrum of colors can be represented.
RGB Color Space
The RGB Color Space uses an additive color model, where different proportions of red, green, and blue light are added together to produce a wide range of colors. Each color in an RGB image is defined by three numerical values (one for each channel), typically ranging from 0 to 255. For example:
Black: (0, 0, 0)
White: (255, 255, 255)
Pure Red: (255, 0, 0)
These values determine the intensity of each color channel, allowing precise control over the color representation of an image.
RGB Cube Representation
The RGB Color Space can be visualized as a 3D cube:
The three axes represent the red, green, and blue channels.
Colors are defined by points within the cube, where each axis represents the intensity of the respective channel. This cube model provides an intuitive way to understand the relationships between colors and their compositions in the RGB space.
Dynamic Range of RGB
The dynamic range of the RGB Color Space is shown in a triangular chromaticity diagram. This diagram represents the gamut of colors that can be produced using the RGB model. While the RGB color space covers a significant portion of visible colors, it is limited to the range defined by the triangle. Other color spaces, like CMYK or LAB, may extend beyond this range depending on their purpose.
Applications in Digital Imaging
The RGB Color Space is used extensively in computer vision and image processing tasks. By representing color images in terms of their RGB components, computers can:
Analyze patterns and features in color.
Detect objects or regions based on color differences.
Enhance, filter, or transform images by manipulating their RGB channels.
