Introduction-to-Computer-Vision & AI.pptx

introduction of Computer Vision

1. Introduction to Computer Vision Computer vision is a field of artificial intelligence that enables computers to "see" and interpret images and videos. It's a fascinating and rapidly evolving field with countless applications. by Arief Budiman

2. What is Computer Vision? Computer vision aims to mimic human visual perception by extracting meaningful information from visual data. It involves techniques for image acquisition, processing, analysis, and understanding. 1 Image Acquisition Capturing images from real-world scenes using cameras or other sensors. 2 Image Processing Manipulating and enhancing images to improve their quality or extract relevant features. 3 Image Analysis Analyzing images to understand their content, identify objects, and recognize patterns. 4 Image Understanding Interpreting the meaning of images and making decisions based on the information extracted.

3. Applications of Computer Vision Computer vision has revolutionized numerous industries, from healthcare and transportation to manufacturing and entertainment. Healthcare Medical imaging analysis, disease detection, and surgical assistance. Transportation Self-driving cars, traffic monitoring, and lane detection. Retail Customer analytics, inventory management, and automated checkout.

4. Fundamental Concepts in Computer Vision Understanding key concepts is crucial for building robust computer vision systems. 1 Image Segmentation Dividing an image into meaningful regions based on color, texture, or shape. 2 Feature Extraction Identifying and representing salient features in an image, such as edges, corners, or textures. 3 Object Recognition Identifying and classifying objects within an image based on learned patterns and features.

5. Image Acquisition and Preprocessing The first step in computer vision is acquiring images from the real world. Image Acquisition Capturing images using cameras, scanners, or other sensors. Preprocessing Cleaning and enhancing images to remove noise, improve contrast, and prepare them for further processing. Image Enhancement Improving the visual quality of images by adjusting brightness, contrast, and sharpness.

6. Feature Extraction and Representation Extracting meaningful features from images is essential for object recognition and scene understanding. Feature Type Description Edges Boundaries between different regions in an image. Corners Points where edges intersect or change direction. Textures Patterns or surface properties that provide information about an object's material. Shapes Geometric descriptions of objects, such as circles, squares, or triangles.

7. Image Classification and Recognition Image classification assigns labels to images based on their content, while recognition identifies specific objects within an image. Image Classification Categorizing images into predefined classes, such as "dog," "cat," or "car." Object Recognition Identifying specific objects within an image, such as recognizing individual faces or cars. Image Segmentation Separating an image into different regions, each representing a distinct object or part of the scene. Object Tracking Following the movement of objects over time, such as tracking a car in a video sequence.

8. Conclusion and Future Trends Computer vision has already made significant strides and continues to evolve rapidly, promising to revolutionize various aspects of our lives. Deep Learning Advanced neural networks are enabling breakthroughs in image understanding and object recognition. 3D Vision Computer vision is expanding into 3D space, enabling applications in robotics, autonomous navigation, and virtual reality. Real-Time Processing The ability to process images in real-time is crucial for applications like autonomous vehicles and surveillance.