Digital Image Processing Quiz

Introduction to Image Processing

• Image processing is a method to perform operations on an image to extract meaningful information, improve image quality, and enable machine understanding of visual information
• Key applications include medical imaging (MRI, CT, X-rays), computer vision (autonomous vehicles, facial recognition), remote sensing (satellite image analysis), robotics (machine vision), biometrics (face, fingerprint, iris, voice recognition), AR/VR (augmented/virtual reality), photography/film, forensics, optical character recognition (OCR), and gaming.

Digital Image Basics

• A digital image is a set of numbers stored by a computer, representing a real image.
• Images are divided into small areas called pixels, each having numerical values representing properties like brightness or color.
• Pixels are arranged in rows and columns, corresponding to the vertical and horizontal position in the image.

Digital Image Basics (Continued)

• A pixel is the smallest unit of a digital image or display.
• Grayscale images use a pixel value to represent a shade of gray, typically ranging from 0 (black) to 255 (white)
• RGB images represent color using Red, Green, and Blue channels, where each pixel contains three values for these colors.
• Image resolution is the number of pixels in the width and height of an image, higher resolution implying more detail.
• Bit depth refers to the number of bits used to represent the color of a single pixel, more bits allowing more shades of colors.

Types of Images

• Binary images: Each pixel is either black (0) or white (1)
• Grayscale images: Contain shades of gray, with each pixel having an intensity value.
• Color images: Typically represented in RGB format, with each pixel containing color information from three channels (Red, Green, Blue).

Image Formats

• PNG: Lossless image format for web graphics, supports transparency, optimized for high quality
• JPEG: Lossy format optimized for photographs, reduces file size at cost of some quality, commonly used for photos.
• TIFF: Lossless format often used in professional imaging (medical/publishing) due to high quality.
• BMP: Uncompressed image format with large file sizes but excellent quality, frequently used in Windows applications.

OpenCV Library

• OpenCV is an open-source computer vision and machine learning library with over 2500 optimized algorithms covering classic and contemporary computer vision techniques.
• Install OpenCV in Python using pip: pip install opencv-python

Basic Operations

• Loading: Reading an image file from disk into memory.
• Saving: Writing an image from memory to disk in a specified format.
• Displaying: Showing an image in a graphical window.

Color Spaces

• RGB (Red, Green, Blue): Standard color model
• HSV (Hue, Saturation, Value): Alternative color representation where hue defines color, saturation defines intensity, and value defines brightness.
• Grayscale: Converts color images to shades of gray by averaging RGB values.

Image Transformations

• Resizing: Changing image dimensions by scaling up/down, often preserving aspect ratio.
• Cropping: Extracting a region of interest (ROI) from an image by cutting out a specific area.
• Rotation: Rotating the image around its center by a specified angle.
• Flipping: Mirroring the image horizontally or vertically.
• Transformation Matrix: Used for operations like rotation, scaling, and translation; mapping points to another coordinate system to modify the geometric properties of an image.
• Rotation Transformation Matrix: Used to rotate points around the origin by a specific angle using trigonometric functions.

Image Filtering

• Blurring: Reduces image details, smoothing by averaging pixel values, used to reduce noise.
  • Gaussian Blur: Weighted smoothing with a bell curve.
  • Mean Filter: Simple averaging.
  • Median Filter: Noise reduction without blurring, good for salt-pepper noise.
  • Bilateral Filter: Smoothing while preserving edges.
  • LoG: Edge detection combining smoothing.
• Sharpening: Enhances edges and fine details by amplifying differences between neighboring pixels.
• Noise Reduction: Removes unwanted random variations (noise) from images using techniques like Gaussian blur or median filtering.
• Edge Detection (Canny, Sobel): Algorithms for identifying edges based on gradient intensities and thresholds.

Report Task 1

• Prepare a report covering interpolation techniques and Canny/Sobel algorithms
• Create a simple Python program to apply Canny edge detection to a video.

Image Processing in OpenCV (2D Convolution)

• 2D convolution involves applying a kernel (small matrix of weights) over input data while performing element-wise multiplication and summing up the results for each output pixel.
• The kernel is called the image filter making the process image filtering where the result (or filtered output) is called i.

Image Thresholding

• Simple Thresholding: A straightforward method where each pixel's value is compared to a single threshold value, setting the pixel to 0 if below the threshold or maximum value if above.
• Adaptive Thresholding: An algorithm that determines the threshold for each pixel based on a local region around it. This provides better results for images with uneven lighting.

Image Histogram

• A histogram is a graph that shows the distribution of pixel intensities in an image.
• Bins display the number of pixels for each pixel value (typically 0-255).
• Dims refer to the number of parameters measured, often just intensity values in a single-channel image.
• The range of values typically used is 0 to 256.

Morphological Transformations

• Morphological operations are image processing techniques that process images based on their shapes, commonly used with binary (black and white) or grayscale images.
• Key operations include erosion (reducing object size), dilation (enlarging object size), opening (reducing noise while maintaining object shapes), closing (filling holes and gaps within objects), and gradient (outline of objects).

Contours

• Contours are the outlines of objects or shapes in an image, expressed as curves connecting continuous points of similar intensity.
• Methods for contour detection include thresholding or edge detection, followed by contour extraction using algorithms that identify and trace these curves.
• Contour detection is used in object detection and identification.

Report Task 2

• Report on Counting Objects:
  1. Read image, convert to grayscale
  2. Blur image
  3. Detect edges
  4. Dilate image to connect edges
  5. Find contours
  6. Count detected objects