Digital Image Processing: Operations and Applications

Explore image enhancement and point processing operations, including digital negative, contrast stretching, thresholding, gray level slicing, bit-plane slicing, dynamic range compression, and gamma correction in the spatial domain.

Apply digital negative and thresholding on a 5x5 8-bit image using s = 255 − r and a 150-threshold, then analyze gray level slicing for a 3-bit image (a=3, b=5).

Explore how neighborhood processing uses filters to enhance images, including low pass and high pass filters, and the median filter to reduce salt and pepper noise.

Examine zooming techniques, including replication with zero interlacing and interpolation by averaging neighbors, and learn shrinking by under sampling through row and column deletion.

Explore histogram processing, histogram stretching, and histogram equalization to enhance image contrast, using the stretch formula to map original gray levels to a dynamic range, then plot the stretched histogram.

Learn histogram equalization by plotting the histogram, computing probabilities and the cumulative distribution, deriving modified gray levels, and drawing the equalized histogram.

Explore the frequency domain for image enhancement, using transforms such as Fourier transform, and applying ideal, Butterworth, and Gaussian filters to control frequencies and reduce ringing.

Explore discrete image transforms like DFT and DCT, using transform matrices F and C on the input image f, and preview Hadamard and Walsh transforms for upcoming lessons.

Learn how the Hadamard transform and Walsh transform are constructed from 2x2, 4x4, and 8x8 matrices, including quadrant patterns and sign changes, with their relation to the DFT.

Explore how the fast Fourier transform speeds up the discrete Fourier transform. Contrast dit and dif approaches, and work through a four point fft example.

Master the eight-point fft using a butterfly diagram, detailing step-by-step additions and multiplications by minus one and minus g, with numbered parts and crossing signs.

Explore image segmentation with point and line detection, using high-pass masks and convolution to identify features, including horizontal, vertical, and 45-degree lines via dissimilarity-based methods.

Learn to apply first-derivative and second-derivative edge detection, use Sobel, Prewitt, and Robert's masks, and address noise with Gaussian smoothing or Laplacian of Gaussian.

Explore how gradient operators like Robert, Sobel, and Prewitt enhance edges and how edge linking forms continuous edges from edge pixels using local and global processing.

explore region growing, the first region-based segmentation method, where a seed pixel and its neighbors are grouped into regions if max-seed ≤ T, highlighting how seed selection can be time-consuming.

Explore region splitting and merging for image segmentation, applying quadrant-based subdivision with a max-min threshold, then merge regions; learn global and local thresholding to separate objects from background.

Explore how digital watermarking embeds a hidden image or text into a carrier image to protect copyrights and identify owners, using LSB and MSB bit changes for watermark visibility.

Explore how image processing enables biometric authentication by analyzing fingerprints, face recognition, hand geometry, and iris scanning using segmentation, pattern matching, and transforms.

Develop automatic license plate recognition by preprocessing and plate area localization, then segmentation to convert vehicle images into plate strings using Sobel edges, thresholding, and morphological operators for character recognition.