You use drones to identify where weeds grow between rows of crops to send an instruction for the removal of the weeds.
This is an example of which type of computer vision?
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A. B. C.A
Object detection is similar to tagging, but the API returns the bounding box coordinates for each tag applied. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image.
Incorrect Answers:
B: Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
C: Scene segmentation determines when a scene changes in video based on visual cues. A scene depicts a single event and it's composed by a series of consecutive shots, which are semantically related.
https://docs.microsoft.com/en-us/ai-builder/object-detection-overview https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overviewThe described scenario is an example of object detection, which is a type of computer vision task that involves identifying and localizing specific objects within an image or video. In this case, the objects of interest are the weeds growing between rows of crops, and the goal is to detect their location and send instructions for their removal.
Object detection typically involves using machine learning algorithms to analyze an image and identify regions of interest that may contain objects. These regions are then further processed to classify the objects and determine their precise location within the image.
There are many different approaches to object detection, including deep learning methods such as convolutional neural networks (CNNs) and region-based techniques like Faster R-CNN and YOLO (You Only Look Once). These methods typically require large amounts of labeled training data to learn to accurately identify and locate objects in new images.
In contrast, optical character recognition (OCR) is a type of computer vision task that involves identifying and converting text within an image or video into machine-readable text. OCR is commonly used in applications like document scanning and image-to-text conversion.
Scene segmentation, on the other hand, involves dividing an image or video into semantically meaningful regions or objects. This can be useful in tasks like image and video editing, as well as in autonomous navigation and robotics applications. However, it is not directly relevant to the scenario described in the question.