Computer Vision Basics Quiz

The ability of computers to understand and interpret images and videos, similar to how humans do.

Images that only capture two dimensions (width and height) as they lack depth information.

Devices that capture images without recording depth information, resulting in 2-D images.

Devices that capture three-dimensional data including depth information.

A technique that tricks the human brain into perceiving depth in a 2-D image by using patterns and overlapping images. While it appears 3D, there's no actual depth information recorded.

Most computer vision applications use 2-D images due to the easy availability and affordability of 2-D capture devices. Accurate and affordable 3-D capture devices remain less common.

Color is represented as a combination of red, green, and blue (RGB) light.

A color model uses a combination of cyan, magenta, yellow, and black (CMYK) pigments to represent colors.

A 2D CV application uses images for analysis. Examples include X-ray analysis and image recognition. These applications focus on extracting information from 2D images.

Image representation is the process of converting the original continuous image into a digital format, allowing computers to process it.

The original image is a continuous function, represented as f(x, y) where x and y are spatial coordinates and the value of f(x, y) represents the brightness at that point. This function is continuous in both the 2D spatial domain and the amplitude.

Discretizing the spatial coordinates of an image. This involves sampling the image at regular intervals, creating a grid of discrete pixels.

Discretizing the amplitude values of an image. This involves dividing the range of possible brightness values into a set of discrete levels, typically represented as integers.

A matrix representing the image is created by sampling it at regular intervals. Each element in the matrix represents the brightness value of a pixel at a specific location in the image. The matrix size is determined by the number of rows and columns in the image.

The image is stored as an array of unsigned characters, making it efficient for storing and processing. Each element of the array represents the gray-level value of a pixel.

The cv2.imread() function loads an image from a specified file path and returns a NumPy array representing the image. It takes two arguments: filename (path to the image) and flag (specifies how the image should be read).

The range of colors humans can see is determined by the wavelengths of light reflected from objects and falls within the visible spectrum, approximately 400 to 700 nanometers.

The color perception of white is achieved when light reflects equally across all wavelengths within the visible spectrum.

Different ways to quantify the color of a light source exist. Radiance refers to the amount of energy emitted. Luminance describes the amount of energy perceived by the observer. Brightness is a subjective descriptor of how bright something appears.

The intensity of each primary color (red, green, blue) can be varied to create a wide range of colors. This is the most common color model used in digital displays.

While the color of an object is perceived by the reflected light, the color's characterization might not always use the frequencies of light.

Color image processing involves understanding and manipulating the color information in images.

The way colors are represented and displayed is important for understanding and manipulating images.

A color model in which colors are represented by combining red, green, and blue light. Each color is represented by an intensity value for each of the three components.

A color model used in printing, where colors are created by mixing cyan, magenta, yellow, and black pigments.

A color model that represents colors using only shades of gray, ranging from black to white.

A color model that represents colors using one single color and its variations. The term "monochrome" is often used to describe images that use only shades of grey.

Each color channel (R, G, or B) in an RGB image is enhanced individually, resulting in a change in color.

Each color channel (R, G, or B) in an RGB image is enhanced individually, resulting in a change in color.

A color model that defines colors using a single value, representing the intensity of light, ranging from 0 (black) to 255 (white).

A color model that defines colors using a single value, representing the intensity of light, ranging from 0 (black) to 255 (white).

A color model representing colors based on hue, saturation, and value.

The actual color of a color, expressed as a number from 0 to 360 degrees. Red, yellow, green, cyan, blue, and magenta are defined within these degrees.

The amount of gray mixed into a color, ranging from 0 to 100 percent. A high saturation means a pure color, while a low one makes the color grayer.

The brightness or intensity of a color, ranging from 0 to 100 percent. A higher value represents more color and brightness.

The use of HSV in design. The HSV model better reflects how people perceive colors than the RGB model.

The RGB color model is often converted to CMYK for printing. Cyan is derived from the absence of red in the original color.

A color space is a mathematical model that describes how colors are represented and processed. It defines a range of reproducible colors and helps in understanding color capabilities of devices and the human visual system.

The YIQ color model is primarily used in commercial color television broadcasting. It represents colors as a combination of luminance (Y) and two chrominance components (I and Q). The luminance channel (Y) carries the brightness information, while the chrominance channels (I and Q) carry color information.

It is a widely used color model in image processing and computer graphics. It represents color as a combination of hue, saturation, and intensity (HSI). Hue represents the pure color, saturation indicates the degree of purity, and intensity describes the brightness level.

Different color spaces exist to address specific requirements and cater to the needs of different applications. Factors like device capabilities, color perception, and image processing techniques influence the choice of color space.

The HSV color model is often used in computer graphics. It combines the hue, saturation, and value to represent colors. Hue represents the pure color, saturation indicates the degree of purity, and value represents the brightness level.

The conversion process between different color spaces allows for seamless integration across various applications and devices. It can be used to convert images from one color model to another, ensuring that colors are displayed accurately across different platforms and devices.