Colour Image Processing Quiz

The field of computer science that allows computers to interpret and understand objects and people in images and videos. It aims to automate tasks that mimic human abilities.

An image that lacks depth information, meaning it can't represent objects in three dimensions.

A device that captures images without collecting depth information. Examples include phone cameras and webcams.

A device that captures both images and depth information, enabling the computer to understand objects in three dimensions. Examples include lidar cameras and Kinect cameras.

A method of creating the illusion of depth in a 2-D image. Your eyes see two different parts of the image, but your brain interprets them as a single object in a 3D space.

Most computer vision applications currently use 2-D images due to the ease of access and cost of 2-D capture devices compared to expensive and less accurate 3-D devices.

A way to represent color information in digital images. Different color models use different mathematical methods to represent color.

A visual representation of an image. It can be a simple grayscale image or a complex color image.

The original image, represented as a continuous function in a 2D X-Y domain and with an amplitude value.

The process of converting a continuous image into a discrete representation by sampling its values at regular intervals in the X-Y domain.

Converting the amplitude (brightness) values of the sampled image into discrete levels, typically represented as integers.

A representation of a sampled image as a 2D array, where each element represents an individual pixel's value.

The process of reading an image file into a program, typically using a library like OpenCV.

A function in a programming library like OpenCV used to read an image file.

The format or properties of the image file, indicating how it should be interpreted by the reading function.

When the cv2.imread() function successfully reads an image, it returns a NumPy array, a data structure commonly used for representing images in python.

A color model specifically designed for commercial TV broadcasting, with Y representing luminance, and I and Q representing chrominance. It prioritizes luminance information, aiming to maintain compatibility with older monochrome TVs.

A color model that decomposes color information into hue, saturation, and intensity (luminance). It emphasizes the perceptual aspects of color, reflecting how humans perceive them.

A color model where colors are described as a combination of red, green, and blue components, widely used in computer displays and image processing.

Represents the color of a digital image. Each color model defines a different method for representing color information, with variations in accuracy, efficiency, and target applications.

A method of converting an image from one color space to another. This is often necessary when specific functions require images in a particular color space.

A color model that prioritizes the perceptual aspects of color, similar to HSI. It's a more intuitive model for users, as it's closer to how we perceive color.

Used in color printers and copiers, this model represents colors based on the amount of cyan, magenta, and yellow pigments used.

A color model that defines color using only one component, lightness, measured from 0 (black) to 255 (white).

An image composed of only one color or shades of a single color. Grayscale images are a common type of monochrome image.

A standard way to represent colors in digital images. It defines the specific values for red, green, and blue components to create an image.

A color model based on light, where combining red, green, and blue creates various colors. Think of adding light sources.

A method for converting a color image to a grayscale image by calculating the average of the RGB values. Creating a black and white image.

A technique for enhancing the contrast of an image by distributing the histogram of pixel intensities more evenly.

A 3D Cartesian system representation of the RGB color model where each axis represents a primary color. The range of each color is typically normalized between 0 and 1.

A color model used for digital displays like monitors and cameras, where red, green, and blue light are combined to create other colors.

A color model used for printing, where cyan, magenta, and yellow inks are combined on paper to create colors.

A color model used for television broadcasting, where luminance and chrominance information are combined to create a color image.

The individual components of a color model, which together represent an image.

A color model used for image manipulation, which represents color based on hue (color), saturation (intensity), and value (brightness).

A color model used for image manipulation, which represents color based on hue (color), saturation (intensity), and value (brightness).

The primary colors in pigment-based color mixing, used in printing and painting.

The colors that result from mixing primary pigment colors, used in printing and painting.

The perceived color of an object is determined by the light it reflects. Visible light falls within a specific range of electromagnetic spectrum wavelengths (≈400 -700 nm). A single color can be viewed as a combination of different wavelengths. For example, when all wavelengths of visible light reflect, we perceive white.

Various quantities are used to describe the quality of a light source, including Radiance (energy emitted), Luminance (energy perceived by an observer), and Brightness (subjective description). For example, infrared light might have high radiance but appear rather dim to humans.

Most colors can be approximated by combinations of the three primary colors: Red (R), Green (G), and Blue (B). This is the basis for color models used in digital image processing.