Explain the use of computer vision in medical diagnostics. Also explain the following terms in the context of computer vision: (i) Face detection (ii) Illumination (iii) Photogrammetry (iv) Occlusion

Computer vision in medical diagnostics utilizes image processing and analysis techniques to interpret medical images for diagnosis, treatment planning, and monitoring. It enables automated detection of anomalies, segmentation of organs or tissues, and extraction of relevant features from medical images such as X-rays, MRIs, and CT scans. Computer vision algorithms can assist healthcare professionals in detecting diseases like cancer, analyzing brain structures, and monitoring the progression of medical conditions.

(i) Face Detection: In computer vision, face detection refers to the process of identifying and locating human faces within images or video frames.

(ii) Illumination: Illumination in computer vision refers to the lighting conditions or brightness levels present in an image or scene, which can affect the visibility and quality of captured images.

(iii) Photogrammetry: Photogrammetry is the process of obtaining accurate measurements and 3D information from 2D images, typically achieved through triangulation techniques using multiple images of the same object or scene.

(iv) Occlusion: In computer vision, occlusion occurs when an object or part of an object is partially or completely obscured from view by another object in the scene, making it challenging to accurately detect or identify the occluded object.