Computer Vision Basics MCQ
Test your understanding of what Computer Vision is, how digital images are represented, typical applications, and how CV relates to image processing and machine learning.
Definition
What is CV?
Digital images
Pixels, RGB
Applications
Real-world use
Pipeline
Low vs high level
Computer Vision Basics: Introduction for Beginners
Computer Vision (CV) is the field of AI that enables machines to interpret visual information from images or video—similar in spirit to human vision, but implemented with cameras, algorithms, and often machine learning models.
What is Computer Vision?
At a high level, CV turns pixels into decisions: from simple measurements (edges, colors) to complex tasks like object detection, segmentation, and scene understanding.
Topics Covered in This Quiz
Digital images
Images are usually stored as grids of pixels. Color images often use multiple channels (e.g., RGB) per pixel; resolution and bit depth affect quality and memory.
CV vs image processing
Image processing focuses on transforming images (filtering, enhancement). Computer vision aims to extract semantic understanding (what and where things are).
Applications
Medical imaging, autonomous systems, quality inspection, augmented reality, and security use CV for perception and automation.
Traditional vs deep CV
Classical pipelines use hand-crafted features and rules; deep learning learns representations from data—many modern systems combine both ideas.
Typical perception stack
Sensor → Preprocessing → Features / model → Post-processing → Decision
Why practice CV basics MCQs?
Short multiple-choice questions help you verify definitions, spot confusions (e.g., detection vs classification), and prepare for coursework and interviews before diving into OpenCV, deep networks, or geometry.