A computer vision algorithm refers to a set of mathematical and computational techniques used to enable computers to interpret and understand visual data, such as images or video. These algorithms process visual information to perform tasks such as object recognition, feature matching, image segmentation, and motion detection. Some of the most commonly used computer vision algorithms include edge detection algorithms (e.g., Canny Edge Detector), which identify boundaries within images; feature extraction algorithms (e.g., SIFT and SURF), which extract distinctive points or features from an image for recognition or matching purposes; and object detection algorithms (e.g., Haar Cascades and YOLO), which locate and classify objects within images. For example, an object detection algorithm like YOLO (You Only Look Once) uses deep learning to recognize and label various objects (such as people, cars, or animals) in real-time video. These algorithms are essential for practical applications such as autonomous driving, facial recognition, and industrial automation, where understanding and processing visual information are critical for decision-making and automation.
What is computer vision algorithm?
- Natural Language Processing (NLP) Basics
- Natural Language Processing (NLP) Advanced Guide
- AI & Machine Learning
- Exploring Vector Database Use Cases
- Information Retrieval 101
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is the TPC benchmark suite?
The TPC benchmark suite consists of a set of standardized tests that measure the performance of transaction processing a
What strategies can be used to update or improve embeddings over time as new data becomes available, and how would that affect ongoing RAG evaluations?
To update embeddings as new data arrives, one effective approach is incremental retraining combined with versioning. Per
What are the trade-offs between accuracy and speed in speech recognition?
In speech recognition systems, the trade-off between accuracy and speed is a common challenge faced by developers. Accur