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Computer Vision: Concepts, Techniques, and Applications

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Course
COMPUTER SCIENCE

Institution
Harvard University

This document covers Computer Vision, focusing on its core concepts, techniques, and applications. It explores image processing, feature extraction, and object detection, along with image classification using Convolutional Neural Networks (CNNs). The document also discusses real-world applications,...

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Uploaded on February 1, 2025
Number of pages 6
Written in 2024/2025
Type Other
Person Unknown

machine learning
cs1004
computer vision
image processing
feature extraction
object detection
image classification
deep learning in vision
facial recognition
edge det
convolutional neural networks cnns

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Computer Vision
Computer Vision is an interdisciplinary field that empowers machines to
interpret, analyze, and understand visual information from the world, much like
humans do. It is a branch of artificial intelligence (AI) that combines elements of
machine learning, deep learning, and image processing techniques to give
computers the ability to process and make decisions based on visual inputs, such
as images, videos, and real-time data.

The ultimate goal of computer vision is to enable computers to perceive and
understand the visual world with the same accuracy and flexibility that humans
use to process and comprehend their environment. By mimicking human vision,
computer vision algorithms are designed to understand and process images and
videos to perform tasks such as object recognition, face detection, scene
segmentation, motion analysis, and much more.

Applications of Computer Vision
Computer vision has a wide range of applications across different fields, from
healthcare and automotive to entertainment and security. Some key applications
include:

1. Image Classification
o Definition: Image classification is the process of categorizing an
image into one of several predefined classes. The model processes
the image and assigns it to the most relevant category.
o Example: In a medical setting, computer vision systems can classify
medical images, such as X-rays, into categories like healthy,
cancerous, or abnormal.
2. Object Detection
o Definition: Object detection not only detects objects in an image but
also identifies their specific locations by drawing bounding boxes
around them. It is often used in combination with image
classification.

, o Example: In autonomous driving, computer vision systems detect
and track vehicles, pedestrians, traffic signs, and obstacles in real
time to navigate safely.
3. Face Recognition
o Definition: Face recognition uses computer vision algorithms to
identify or verify a person based on facial features. It is often used for
security purposes, such as unlocking smartphones or surveillance.
o Example: Face recognition systems are used in airports for passenger
identification and in smartphones for secure authentication.
4. Scene Segmentation
o Definition: Scene segmentation refers to dividing an image into
multiple segments or regions that represent different objects or parts
of a scene. This process helps in understanding the content of the
image.
o Example: In autonomous vehicles, scene segmentation is used to
separate the road, pedestrians, vehicles, and traffic signs to make
real-time driving decisions.
5. Gesture Recognition
o Definition: Gesture recognition involves identifying human gestures,
such as hand or body movements, to enable interaction with
machines or devices.
o Example: Gesture-based controls are used in virtual reality (VR)
environments or gaming consoles like Xbox Kinect.
6. Optical Character Recognition (OCR)
o Definition: OCR is a technology used to convert different types of
documents—such as scanned paper documents, PDFs, or images—
into editable and searchable text.
o Example: OCR is used in document scanning apps to extract text
from images for digitization and data processing.
7. Autonomous Vehicles
o Definition: Computer vision plays a critical role in self-driving cars by
enabling them to interpret and understand their surroundings
through cameras, sensors, and real-time processing.
o Example: Autonomous vehicles use computer vision to detect traffic
signals, other vehicles, pedestrians, and road signs to navigate safely
and make driving decisions.

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