Describe & Caption Images Automatically Vision AI

Automatic image captions with Microsoft Azure Computer Vision API

which computer vision feature can you use to generate automatic captions for digital photographs?

Gone are the days when digital entertainment meant that the viewer had to sit and watch without participating. Today, interactive entertainment solutions leverage computer vision to deliver truly immersive experiences. Cutting-edge entertainment services use artificial intelligence to allow users to partake in dynamic experiences. Launched in 2018, Facebook 3D Photo originally required a smartphone with dual cameras to generate 3D images and create a depth map. While this originally limited the popularity of this feature, the widespread availability of economically priced dual-camera phones has since increased the use of this computer vision-powered feature.

which computer vision feature can you use to generate automatic captions for digital photographs?

The operating system breaks down the query and uses a combination of metadata and machine learning to find you relevant photographs. As its name implies, YOLO can detect objects by passing an image through a neural network only once. The algorithm completes the prediction for an entire image within one algorithm run. It is also capable of ‘learning’ new things quickly and effectively, storing data on object representations and leveraging this information for object detection. Computer vision is defined as a solution that leverages artificial intelligence (AI) to allow computers to obtain meaningful data from visual inputs. The insights gained from computer vision are then used to take automated actions.

Download and prepare the MS-COCO dataset

The process by which light interacts with surfaces is explained using physics. Physics explains the behavior of optics which are a core part of most imaging systems. Sophisticated image sensors even require quantum mechanics to provide a complete understanding of the image formation process.[10] Also, various measurement problems in physics can be addressed using computer vision, for example, motion in fluids. The new voice capability is powered by a new text-to-speech model, capable of generating human-like audio from just text and a few seconds of sample speech. We collaborated with professional voice actors to create each of the voices.

which computer vision feature can you use to generate automatic captions for digital photographs?

A well-known application has been automatic speech recognition, to cope with different speaking speeds. In general, it is a method that allows a computer to find an optimal match between two given sequences (e.g., time series) with certain restrictions. This sequence alignment method is often used in the context of hidden Markov models. The term voice recognition[3][4][5] or speaker identification[6][7][8] refers to identifying the speaker, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person’s voice or it can be used to authenticate or verify the identity of a speaker as part of a security process. Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers.

Transportation – Violations Detection, Traffic Flow Analysis

The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems. While a convolutional neural network understands single images, a recurrent neural network processes video inputs to enable computers to ‘learn’ how a series of pictures relate to each other. To see images just like a human would, neural networks execute convolutions and examine the accuracy of the output in numerous iterations. Just like humans would discern an object far away, a convolutional neural network begins by identifying rudimentary shapes and hard edges. Once this is done, the model patches the gaps in its data and executes iterations of its output. This goes on until the output accurately ‘predicts’ what is going to happen.

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