Digital image processing is a multidisciplinary field that has evolved significantly with the advancement of computer science and technology. Over time, image processing and analysis have developed into their own structured scientific systems, with new techniques continuously emerging. Although its history is relatively short, it has attracted widespread attention across various industries. First, vision is the primary means of human perception, and images serve as the foundation for visual understanding. As a result, digital images have become an essential tool for researchers in fields like psychology, physiology, and computer science to study visual perception. Second, the demand for image processing continues to grow in large-scale applications such as military operations, remote sensing, and meteorology.
Image segmentation is a crucial technique used to divide an image into distinct regions with specific characteristics, allowing for the extraction of objects of interest. It serves as a key step in transitioning from image processing to image analysis. Existing segmentation methods can generally be categorized into threshold-based, region-based, edge-based, and theory-driven approaches. Since 1998, researchers have continuously refined these methods by incorporating new theories and techniques from other disciplines, leading to the development of more advanced segmentation algorithms. The segmented objects can then be applied in areas like image semantic recognition and search.
**What are the methods of image segmentation?**
**1. Region-based Image Segmentation**
Region-based segmentation includes techniques such as histogram thresholding, region growing, random field models, and relaxed marker segmentation. These methods focus on grouping pixels based on similarity in intensity or other properties.
(1) Histogram thresholding involves dividing the image's gray-level histogram into multiple classes using one or more thresholds. This method relies on criteria such as minimum intra-class variance, maximum inter-class variance, or maximum entropy. However, it often ignores spatial information, making it less effective in cases where there is little contrast between different regions.
(2) Region growing starts with a seed point or small area and gradually expands it by adding neighboring pixels that meet certain criteria. This approach can lead to over-segmentation if not carefully controlled.
(3) The random field model method uses Markov Random Fields (MRFs) to represent images, assuming they follow a Gibbs distribution. This approach requires defining a neighborhood system, selecting an energy function, and minimizing it to achieve optimal segmentation.
(4) Labeling assigns unique identifiers to different regions within an image. Techniques like discrete relaxation, probability relaxation, and fuzzy relaxation are commonly used to assign labels efficiently.
**2. Edge-based Image Segmentation**
Edge-based segmentation is closely tied to edge detection, which identifies boundaries between regions. Common methods include local image function-based approaches, image filtering, reaction-diffusion equations, boundary curve fitting, and active contour models.
(1) Local image function-based methods use surface fitting to detect edges by analyzing pixel intensities within a small window.
(2) Image filtering involves applying filters such as Gaussian derivatives to detect edges through first or second-order derivatives.
(3) Reaction-diffusion equations provide multi-scale filtering, though detailed explanations are limited due to space constraints.
(4) Boundary curve fitting represents image boundaries as continuous curves, offering advantages for high-level processing tasks such as object recognition.
(5) Active contours, also known as snake models, are deformable models that evolve to fit the boundaries of objects. They combine internal and external energies to achieve accurate segmentation.
These methods form the foundation of modern image segmentation, enabling a wide range of applications from medical imaging to autonomous systems.
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