Principles of Digital Image Processing by Burger and Burge (open-access companion materials).
The role of soft computing in image processing is explored, offering insights into intelligent image processing systems.
: The foundational version focusing on core principles and 12 primary chapters.
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Segmentation, the process of partitioning an image into meaningful regions, is the bridge between low-level processing and high-level description. Techniques such as thresholding, edge detection using operators like Sobel or Canny, and region-based segmentation are covered extensively. Sridhar’s treatment of morphological processing—algorithms that process images based on shapes—is particularly relevant. By using structuring elements to probe the image, morphological operations like erosion and dilation allow for the separation of overlapping objects and the filling of holes, a critical step in automated inspection systems.
Covers image acquisition, sampling, quantization, and basic digital imaging operations.
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How to apply morphological features for object classification, such as in the study of rice kernels.
: Detailed coverage of Fourier, Walsh, and Hadamard transforms.
: The book uses a step-by-step algorithmic style to illustrate core image processing concepts, making it easier for students to translate theory into code. Comprehensive Topic Coverage