Instructions: Answer all questions. Show your work clearly.

Explain the fundamental steps in digital image processing which can be applied to images.

Marks: 8

• Digital Image Processing (DIP) is a field that deals with the manipulation and analysis of digital images using computers. It involves transforming an image into a digital format and then performing operations on it to extract useful information or enhance its quality. The fundamental steps in DIP can be broadly categorized as follows:
• 1. Image Acquisition: This is the first step where an image is captured. It involves the sensor (e.g., CCD, CMOS camera) that converts the light intensity into an electrical signal, which is then digitized. The output of this stage is a digital image, typically represented as a 2D array of pixels.
• 2. Image Enhancement: This step aims to improve the visual appearance of an image or to highlight certain features for further analysis. Common enhancement techniques include:
  • Point Processing: Operations that modify the intensity of individual pixels based on a mapping function. Examples include brightness adjustment, contrast stretching, and thresholding.
  • Spatial Filtering: Operations that modify pixel values based on the values of their neighbors. This includes smoothing filters (e.g., mean, Gaussian) to reduce noise and sharpening filters (e.g., Laplacian, Sobel) to enhance edges.
  • Color Image Processing: Techniques applied to color images, such as color balancing, color correction, and pseudo-coloring.
• 3. Image Restoration: This step attempts to remove or reduce known degradations from an image, such as blur caused by motion or out-of-focus optics, or noise introduced during acquisition or transmission. Techniques like inverse filtering, Wiener filtering, and geometric mean filtering are used.
• 4. Color Image Processing: This involves processing color images. It includes representing colors in different color spaces (e.g., RGB, HSV, YCbCr), color transformations, and color segmentation.
• 5. Image Segmentation: This is a crucial step where an image is partitioned into multiple segments or regions, each corresponding to a different object or part of an object. Segmentation is often based on pixel properties like intensity, color, or texture, or on spatial relationships between pixels. Common methods include:
  • Thresholding: Separating pixels into regions based on their intensity values.
  • Region-based segmentation: Grouping pixels into regions based on similarity criteria.
  • Edge detection: Identifying pixels that lie on the boundaries of objects.
  • Clustering: Grouping pixels into clusters based on their features.
• 6. Image Representation and Description: Once an image is segmented into meaningful regions, it needs to be represented in a format suitable for further processing. This involves extracting features that describe the regions or objects. Descriptions can be:
  • Boundary descriptors: Properties of the object's boundary, such as length, curvature, or shape.
  • Region descriptors: Properties of the region itself, such as area, perimeter, texture, or color.
  • Features: More complex characteristics like shape, size, orientation, and texture.
• 7. Object Recognition: This is the final step where objects are identified based on their descriptions. It involves matching the extracted features with a database of known objects.
• 8. Image Compression: This step reduces the amount of data required to represent an image, making storage and transmission more efficient. Techniques can be lossless (no information loss) or lossy (some information loss).

Instructions: Answer all questions. Show your work clearly.

Discuss in detail about Mathematical Morphology.

Marks: 7

• Structuring Element (SE): The structuring element is a small binary image (or a template) of a specific shape and size. It is used as a probe to interact with the input image. The shape of the SE is crucial as it determines the nature of the morphological operation. Common shapes include squares, disks, and lines.
• Dilation: Dilation is an operation that expands or grows the boundaries of foreground objects in an image. For a binary image A and a structuring element B, the dilation of A by B is defined as:

• Erosion: Erosion is an operation that shrinks or erodes the boundaries of foreground objects. For a binary image A and a structuring element B, the erosion of A by B is defined as:

• Opening: Opening is a two-step operation that consists of an erosion followed by a dilation using the same structuring element. It is denoted as A ∘ B.

• Closing: Closing is the dual operation of opening. It consists of a dilation followed by an erosion using the same structuring element. It is denoted as A • B.

• Applications: Mathematical morphology finds applications in various areas, including:
  • Noise reduction (e.g., removing salt-and-pepper noise).
  • Segmentation of objects.
  • Skeletonization and thinning of objects.
  • Feature extraction (e.g., finding corners, holes).
  • Pattern recognition.
  • Medical imaging analysis.