Digital Image Fundamentals Quiz

Image sampling refers to the process of converting a continuous image into a digital format by selecting a limited number of points or samples from the continuous image. Image quantization involves assigning a finite number of intensity levels to each sample point, thereby converting the sampled image into a digital format with discrete intensity levels.

The basic relationships between pixels include neighborhood, which refers to the spatial arrangement of pixels around a central pixel; adjacency, which indicates whether two pixels share an edge or a corner; connectivity, which describes whether pixels are connected in a continuous path; and distance measures, which quantify the spatial separation between pixels.

The elements of visual perception in digital image fundamentals encompass the physiological and psychological processes involved in the reception and interpretation of visual stimuli. This includes aspects such as brightness perception, color perception, spatial perception, and visual illusions.

The key principles of image sensing and acquisition include capturing and converting visual information into digital data, often through the use of sensors and analog-to-digital converters. This process involves sampling the continuous image signal and quantizing the samples to discrete levels for digital representation.

In digital image fundamentals, neighborhood, adjacency, connectivity, and distance measures are used to define the spatial relationships between pixels. These concepts are fundamental to tasks such as image segmentation, edge detection, and object recognition.

Image sampling and quantization are critical in digital image fundamentals as they determine the resolution and fidelity of the digital representation of an image. Sampling governs the spatial resolution, while quantization determines the intensity resolution of the image.