Basics of Image Representation | Computer Vision |

Basics of Image Representation in Computer Vision

At the heart of Computer Vision lies the image, a rich source of visual information. But for computers, which rely on numbers, how can something as intricate as an image be represented in a way machines can process? That’s where the concept of image representation comes in. Understanding this fundamental topic is crucial to grasping how machines "see" and interpret the visual world.

In this blog, we’ll explore the basics of image representation, different types of images, how images are stored, and real-world examples to clarify these concepts.

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What is an Image?

An image is a two-dimensional (2D) visual representation of a scene or object. It is made up of tiny units called pixels (picture elements). Each pixel holds specific information about the color and intensity of light at that point in the image.

For instance:

• A black-and-white photo has pixels that represent different shades of gray.
• A color image has pixels representing a mix of colors.

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How Are Images Represented in Computers?

Computers store images as arrays (or matrices) of numbers. Each number corresponds to a pixel's intensity (for grayscale images) or color value (for color images).

Key Terminology

• Pixel: The smallest unit of an image.
• Resolution: The number of pixels in an image, typically expressed as width × height (e.g., 1920 × 1080).
• Bit Depth: The number of bits used to represent the intensity or color of a pixel.

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Types of Image Representations

1. Grayscale Images

• Description: Each pixel is represented by a single value, indicating the intensity of light (from black to white).
• Range: Typically 0 (black) to 255 (white) for an 8-bit image.
• Storage: A 2D array where each value corresponds to a pixel’s intensity.
• Example: 
• 0 50 100 
• 150 200 255 
• This represents a small 3×2 grayscale image.

2. Binary Images

• Description: Each pixel is represented by either 0 or 1, indicating two possible states (black or white).
• Use Case: Document scanning, where text is black (1) and the background is white (0).

3. Color Images (RGB)

• Description: Represented using three channels—Red, Green, and Blue (RGB).
• Structure: A 3D array where each channel is a 2D matrix.
• Range: 0 to 255 for each color channel in an 8-bit image.
• Example: A pixel with RGB values (255, 0, 0) represents bright red.

4. Multispectral and Hyperspectral Images

• Description: These contain data from beyond the visible spectrum, such as infrared or ultraviolet.
• Use Case: Remote sensing, where each band captures a specific wavelength of light.

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Image File Formats

Images are stored in various file formats depending on their purpose. Common formats include:

• JPEG: Compressed format for general use (e.g., photos).
• PNG: Lossless compression, ideal for graphics.
• BMP: Raw pixel data without compression.
• TIFF: High-quality images, often used in professional editing.
• GIF: Supports simple animations and limited colors.

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How Does Color Work in Images?

The RGB Model

The RGB model combines Red, Green, and Blue channels to create colors. Each channel contributes to the final color of a pixel. For example:

• (255, 0, 0): Pure red.
• (0, 255, 0): Pure green.
• (0, 0, 255): Pure blue.
• (255, 255, 0): Yellow (Red + Green).

Other Color Models

While RGB is most common, other models include:

• CMYK (Cyan, Magenta, Yellow, Black): Used in printing.
• HSV (Hue, Saturation, Value): Represents color in terms of its shade and brightness.

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How Do Computers Process Images?

1. Image Acquisition: Capturing the image using cameras or sensors.
2. Digitization: Converting the image into numerical data.
3. Storage: Saving the numerical data in memory or as files.
4. Processing: Applying algorithms to extract or analyze information.

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Examples to Clarify Image Representation

Example 1: Grayscale Image

Imagine a grayscale image of size 4×4:

10  20  30  40
50  60  70  80
90  100 110 120
130 140 150 160

• Interpretation: Each number is a pixel intensity.
• Dark Regions: Lower values (e.g., 10).
• Bright Regions: Higher values (e.g., 160).

Example 2: RGB Image

A 2×2 color image:

R: [[255, 0],   G: [[0, 255],   B: [[0, 0],
     [0, 0]]        [255, 0]]       [255, 255]]

• The pixel at (0, 0): (255, 0, 0) = Red.
• The pixel at (1, 1): (0, 0, 255) = Blue.

Example 3: Binary Image

A 3×3 binary image:

1  0  1
0  1  0
1  0  1

• White areas = 1.
• Black areas = 0.

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Real-World Applications of Image Representation

1. Facial Recognition:

• Images of faces are represented as matrices and analyzed for patterns.

1. Object Detection:

• Images are divided into grids, and each section is analyzed for specific objects.

1. Medical Imaging:

• Grayscale or multispectral representations help detect abnormalities in scans.

1. Augmented Reality (AR):

• RGB and depth images are used to overlay virtual objects onto the real world.

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Challenges in Image Representation

1. Resolution Trade-offs: Higher resolution provides more detail but increases storage and processing requirements.
2. Noise and Distortion: Poor-quality images can affect representation accuracy.
3. Color Space Variations: Different devices may interpret color differently.

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Conclusion

Image representation is the foundation of Computer Vision. By converting visual information into numerical data, machines can analyze and interpret the world. Whether it’s a simple binary image or a complex hyperspectral one, the principles of representation enable the powerful applications we see today.

Understanding these basics is the first step in unraveling the deeper layers of Computer Vision. Stay tuned for more insights into how machines learn to see and understand!

What aspect of image representation intrigued you the most? Let me know in the comments below!