Introduction to Computer Vision MCQ
What computer vision is, how it differs from image processing, history, applications, and digital image basics—pixels, channels, resolution, and Python loading examples.
Computer Vision Basics MCQ
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.
Image Processing Basics MCQ
Digital Image Basics for Computer Vision
Before filters and detectors, images are discrete grids of samples. Understanding resolution, bit depth, and how sampling and quantization affect quality helps you interpret algorithms and artifacts.
Core idea
Spatial sampling picks where you measure; quantization picks how finely you record brightness or color at each sample.
Quick topics
Resolution
W×H in pixels sets spatial detail; memory and compute often scale with pixel count.
Channels
Grayscale: one value per pixel. RGB: three. More channels (alpha, multispectral) pack extra information.
Aliasing
Shrinking images without low-pass filtering can create jaggies and moiré—mitigate with blur before downsample.
Compression
Lossless preserves pixels exactly; lossy trades fidelity for smaller files—important for storage and pipelines.
Digitization chain
Scene → Optics & sensor → Sampling & quantization → Stored raster → Processing