In the context of segmentation, classical machine learning and deep learning represent two distinct approaches to pixel classification and object identification.

• Classical Machine Learning: This approach relies on algorithms such as decision trees, random forests, or support vector machines to classify pixels based on manually selected features (e.g., intensity, texture, or shape). It requires feature engineering, where users define the relevant characteristics of the data. Tools like the Intellesis Trainable Class Segmenter in ZEN and ZEN core utilize classical machine learning to provide robust and customizable segmentation workflows.
• Deep Learning: In contrast, deep learning uses neural networks to automatically learn features from data during the training process. This method excels in handling complex datasets and identifying patterns that may not be apparent through manual feature selection. For instance, AI Instance Segmentation in ZEN and ZEN core applies deep learning models to distinguish and separate overlapping objects with high precision.

Both approaches are available in ZEN and ZEN core, allowing you to choose the method that best suits their data and analysis requirements. While classical machine learning is often faster to implement for simpler tasks, deep learning is ideal for more challenging segmentation problems involving complex or heterogeneous datasets.

Related Topics

• Intellesis Segmentation
• Image Analysis Workflows in ZEN core
• arivis Cloud Integration

See also

• Intellesis Segmentation
• Global Thresholding
• Background Subtraction
• Variance-based Thresholding
• Intellesis Trainable Segmentation
• AI Instance Segmentation
• AI Semantic Segmentation