Learn the differences between four types of computer vision: object detection, OCR, semantic segmentation, and image classification.
Table of Contents
Question
Which type of computer vision was used?
A. Optical character recognition (OCR)
B. Object detection
C. Semantic segmentation
D. Image classification
Answer
B. Object detection
Explanation
Object detection is a form of machine learning based computer vision in which a model is trained to recognize individual types of object in an image, and to identify their location in the image.
The correct answer is B. Object detection.
Object detection is a type of computer vision that locates and identifies objects in an image or video. It can output the coordinates of a bounding box around each object, as well as the class label of the object. For example, object detection can be used to detect and count cars, pedestrians, and traffic signs in a street scene.
Optical character recognition (OCR) is a type of computer vision that extracts text from images. It can recognize the characters, words, and sentences in an image and convert them into a machine-readable format. For example, OCR can be used to scan and digitize documents, receipts, and business cards.
Semantic segmentation is a type of computer vision that assigns a class label to each pixel in an image. It can output a mask that shows the boundaries and regions of different objects in an image. For example, semantic segmentation can be used to segment and label the parts of a human body, such as skin, hair, eyes, and mouth.
Image classification is a type of computer vision that assigns a single class label to an image. It can output the probability of an image belonging to a certain category. For example, image classification can be used to identify the type of animal, plant, or food in an image.
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