Digital Image Processing

Histogram equalization, contrast adjustment, noise reduction, and sharpening are all image processing techniques used to enhance the quality of an image by improving its contrast, brightness, and sharpness.

Background subtraction, optical flow, mean-shift algorithm, and Kalman filter are all image processing techniques used to detect and track objects in a video by identifying moving objects and estimating their trajectories.

Thresholding, region growing, clustering, and watershed segmentation are all image processing techniques used to segment medical images into different regions of interest, such as organs, tissues, and lesions.

Edge detection, template matching, blob analysis, and Fourier transform are all image processing techniques used to inspect products for defects by identifying anomalies in the product's appearance or structure.

Increasing the saturation is not a common technique for correcting overexposure in post-processing. Instead, techniques such as decreasing the exposure value, adjusting the contrast, or applying a graduated neutral density filter are typically used.

Decreasing the contrast is not a common technique for correcting underexposure in post-processing. Instead, techniques such as increasing the exposure value, adjusting the brightness, or applying a graduated neutral density filter are typically used.

A properly exposed photograph typically has a histogram that is evenly distributed across the entire tonal range, indicating that the image contains a good balance of highlights, midtones, and shadows.

The flicker fusion threshold is the rate at which a series of still images must be presented in order to appear to be moving. The flicker fusion threshold is typically around 10 to 12 images per second.

The critical flicker fusion threshold is the rate at which a series of still images must be presented in order to appear to be moving. The critical flicker fusion threshold is typically around 16 to 20 images per second.

Motion blur is typically not used in architectural photography, as it creates a sense of movement that is not appropriate for capturing static structures.

Infrared filters are not commonly used in architectural photography, as they capture infrared light, which is invisible to the human eye.

Region Growing is a segmentation technique that starts with a seed point and iteratively adds neighboring pixels that meet certain criteria, such as similarity in color or texture.

Edge-based segmentation techniques aim to identify and extract boundaries or edges between different regions in an image.

Thresholding is a simple segmentation technique that assigns pixels to different segments based on whether their intensity values are above or below a specified threshold.

Watershed segmentation aims to extract individual objects from an image by simulating the flooding of a landscape, where each pixel is assigned to the catchment basin of its nearest local minimum.