Using Google Cloud Vision in assistive technology scenarios

Mulfari, Davide; Celesti, Antonio; Fazio, Maria; Villari, Massimo; Puliafito, Antonio

doi:10.1109/iscc.2016.7543742

Cited by 42 publications

(22 citation statements)

References 9 publications

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“…Computer vision, as a research field, is the science that uses a machine to collect and analyze images and videos “to extract information from the processed visual data” (Mulfari, Celesti, Fazio, Villari, & Puliafito, 2016, p. 1). Computer vision has been used for different purposes.…”

Section: Background and Research Questionsmentioning

confidence: 99%

“…Computer vision has been used for different purposes. Image labeling, face and landmark recognition, optical character recognition, and tagging of explicit content (Mulfari et al, 2016) are only a few examples of information and data that can be extracted from images. However, the opportunities of applying computer vision to implement new measurement methods and techniques have been little explored in survey research.…”

Section: Background and Research Questionsmentioning

confidence: 99%

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Answering Mobile Surveys With Images: An Exploration Using a Computer Vision API

Bosch

Revilla

Paura³

2018

Social Science Computer Review

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Most mobile devices nowadays have a camera. Besides, posting and sharing images have been found as one of the most frequent and engaging Internet activities. However, to our knowledge, no research has explored the feasibility of asking respondents of online surveys to upload images to answer survey questions. The main goal of this article is to investigate the viability of asking respondents of an online opt-in panel to upload during a mobile web survey: First, a photo taken in the moment, and second, an image already saved on their smartphone. In addition, we want to test to what extent the Google Vision application programming interface (API), which can label images into categories, produces similar tags than a human coder. Overall, results from a survey conducted among millennials in Spain and Mexico ( N = 1,614) show that more than half of the respondents uploaded an image. Of those, 77.3% and 83.4%, respectively, complied with what the question asked. Moreover, respectively, 52.4% and 65.0% of the images were similarly codified by the Google Vision API and the human coder. In addition, the API codified 1,818 images in less than 5 min, whereas the human coder spent nearly 35 hours to complete the same task.

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Section: Background and Research Questionsmentioning

confidence: 99%

Section: Background and Research Questionsmentioning

confidence: 99%

Answering Mobile Surveys With Images: An Exploration Using a Computer Vision API

Bosch

Revilla

Paura³

2018

Social Science Computer Review

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“…Moreover, similar methods were also created to analyze skin diseases from skin image [12][13][14][15][16][17][18][19][20][21]. If we take a look in more detail on the method, deep learning has been chosen recently and massively as one of the methods for automatically analyzing the medical image [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. Deep learning has been widely used to analyze medical images in skin lesion classification [19][20][21], breast cancer classification [23][24][25][26], and melanoma detection [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Medical vision: web and mobile medical image retrieval system based on google cloud vision

Putra

Asra

Hardijaya

et al. 2020

IJECE

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The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.

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“…In this case, we use Google Cloud Vision, an image recognition technology that enables users to remotely process the content of an image and retrieve its main features [23]. Launched in February of 2016 [24], the tool enables access to image classification algorithms that were built with very large training sets and provides features such as face detection, optical character recognition, label detection, SafeSearch detection, landmark detection, logo detection, and properties detection [23,24]. After an image is uploaded to the Application Programming Interface (API), the label detection feature returns a list of labels for the image, along with a confidence score for each label.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of the Digital Content about Energy-Saving in Different Countries around the World

Ureña

Rogers

2019

Sustainability

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Environmental sustainability is fundamental for human well-being, and energy-saving behaviors are fundamental for promoting environmental sustainability. This paper explores the types of information consumed on the Internet concerning the topic of energy-saving. The qualitative study used digital methods to analyze, from 17 different countries, one-year of Google searches, 170 webpages, and 6800 images. The results demonstrated that (a) most topics related to energy-saving involved commercial products; (b) in countries from North America, Europe, and Oceania, Google’s highest-ranked webpages were of a commercial nature, and in countries from Latin America, Africa, and Asia, webpages were of an educational nature; and (c) most images found in Google with the “energy-saving” query were related to lighting products. By and large, commercial interests were found to dominate the digital rhetoric around energy-saving, regardless of the countries’ region.

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Using Google Cloud Vision in assistive technology scenarios

Cited by 42 publications

References 9 publications

Answering Mobile Surveys With Images: An Exploration Using a Computer Vision API

Answering Mobile Surveys With Images: An Exploration Using a Computer Vision API

Medical vision: web and mobile medical image retrieval system based on google cloud vision

Characteristics of the Digital Content about Energy-Saving in Different Countries around the World

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