CVML knowledge self-assessment

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Self-assessment of Computer Vision and Machine Learning knowledge


You may want to try the following understanding questionnaires to assess your knowledge for CVML and autonomous systems!

You will get an instant assessment. Do not reply questions at random, if you want to get a true picture.

Assessment is fully private, Google forms records are deleted.

If you want to check your background knowledge for Computer Vision and Machine Learning studies go here.


If you want to upgrade your knowledge, you may try the course: and repeat your assessment.


Each test should take approximately 15 minutes.


2D Computer Vision/Image Analysis

Edge Detection

Image Features

Introduction to 2D Computer Vision

Region Segmentation

Shape Description

Autonomous Drones

Drone cinematography

Drone mission simulations

Introduction to Multiple Drone Systems

Multiple Drone Communications

Multiple Drone Mission Planning and Control

UAV infrastructure inspection

Autonomous Systems and Robotics

Autonomous Systems Sensors

Introduction to Autonomous Systems

Computer vision

3D Robot Localization and Mapping

Digital Images and Videos

Image Acquisition. Camera Geometry

Introduction to Computer Vision

Stereo and Multiview Imaging

Structure from Motion

CVML Development and Programming Tools

CVML Software Development Tools

CVML Mathematical Foundations

Probability Theory

Image Processing

2D Digital Filter Design and Implementation

2D Systems

Color Theory

Digital Image Formation

Digital Image Processing

Digital Images

Fast 2D Convolution Algorithms

Human Visual System

Image Perception

Image Sampling

Image Transforms

Image Typology

Introduction to Image Processing

Machine Learning. Pattern Recognition

Bayesian Learning

Data Clustering

Decision Surfaces. Support Vector Machines

Dimensionality Reduction

Distance-based Classification

Introduction to Machine Learning

Kernel Μethods

Neural Networks. Deep learning

Artificial Neural Networks. Perceptron

Convolutional Neural Networks

Deep Object Detection

Deep Reinforcement Learning

Deep Semantic Image Segmentation

Generative Adversarial Networks for Multimedia Content Creation

Multilayer Perceptron. Backpropagation

Recurrent Neural Networks. LSTMs

Signal and Systems

Continuous-time Signals and Systems

Discrete Fourier Transform

Discrete-time Signals and Systems

Fast 1D Convolution Algorithms

Fast Fourier Transform

Fourier Transform

Introduction to Signals and Systems

Laplace Transform

Orthogonal Signal Transforms

Signal Sampling

State-Space Equations

Video Processing and Analysis

2D Visual Object Tracking

Transform Video Compression



Fast convolution algorithms


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