Image Segmentation Techniques: Partitioning Images into Characteristic Regions and Target Extraction

The term "image segmentation" used in this study refers to partitioning images into regions with specific characteristics and extracting target objects of interest. This process represents a crucial step in transitioning from image processing to image analysis. In wheat image processing and research, segmentation algorithms are applied to extract useful image information, achieve separation of adhered wheat kernels, and establish foundations for further quality analysis using image processing technologies. Therefore, research on wheat image segmentation methods holds significant importance.

This paper first categorizes image segmentation algorithms and introduces fundamental principles of commonly used methods. Subsequently, based on characteristics of wheat images, it investigates segmentation approaches applicable under different lighting conditions, backgrounds, and connectivity situations, while comparing, combining, and improving various algorithms. The research emphasizes the application of wavelet transform-based watershed algorithms and threshold segmentation techniques in wheat image processing. Through MATLAB implementation, selected images were successfully processed to achieve separation of adhered wheat kernels, demonstrating practical code implementation involving image preprocessing, feature extraction, and segmentation parameter optimization.

Finally, the study discusses future development prospects for image segmentation technology. This research provides valuable references for applying image segmentation techniques to wheat quality assessment. Furthermore, we aim to continue exploring and improving segmentation methodologies to enhance practical applications and contribute to advancements in wheat quality evaluation through optimized algorithm development and implementation strategies.