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Computer vision

Computer vision in AI refers to the field of study that focuses on enabling computers to interpret and understand the visual world. It involves developing algorithms and techniques that allow computers to extract meaningful information from digital images or videos, similar to how humans perceive and understand visual information.

Computer vision tasks can range from simple image processing tasks, such as image enhancement and noise reduction, to more complex tasks such as object recognition, scene understanding, and image generation. Some of the key tasks in computer vision include:

1. Image Classification
Classifying images into predefined categories or classes based on their visual content. This is a fundamental task in computer vision and is often used as a building block for more complex tasks.

2. **Object Detection:** Detecting and locating objects within an image and drawing bounding boxes around them. Object detection algorithms are used in applications such as autonomous driving, surveillance, and image retrieval.

3. Image Segmentation
Dividing an image into multiple segments or regions to simplify its representation or to make it more meaningful for analysis. Image segmentation is used in tasks such as medical image analysis and video object tracking.

4. Pose Estimation 
Estimating the pose or position of objects in an image, such as the orientation of a person's body or the position of a robot in a scene. Pose estimation is used in applications such as augmented reality and robotics.

5. Feature Detection and Description
 Detecting and describing distinctive features in an image, such as corners, edges, or keypoints. These features are used for tasks such as image matching and object recognition.

6. Scene Understanding 
Understanding the content and context of a scene, including the relationships between objects and the overall scene layout. Scene understanding is used in applications such as autonomous navigation and image captioning.

Computer vision is a rapidly evolving field with applications in various industries, including healthcare, automotive, entertainment, and security. Advances in deep learning, particularly convolutional neural networks (CNNs), have significantly advanced the state-of-the-art in computer vision, enabling computers to perform complex visual tasks with human-like accuracy.

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