Computer Vision Services

  • What is a Computer Vision API?

    An application programming interface (API) is a set of functions and definitions that enables two different software applications or systems to communicate with each other. Without APIs, developers would need to write custom integrations and scripts in order for these systems to exchange information—a time-consuming and technically challenging process. Chooch’s computer vision API helps users get fast, highly accurate identifications of the objects and concepts in their visual content. Given an image or video, the Chooch computer vision API will return the requested output (e.g. the items or faces in an image), as well as the relevant pixel coordinates.

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  • Kurumsal Dünyada Görsel Yapay Zeka Uygulamaları

    Görsel yapay zeka artık hayatımızda. Turkcell CEO’su Murat Erkan ve Chooch AI CEO’su Emrah Gültekin ile gerçekleşen sohbetin video kaydını izleyebilirsiniz. Hakan Erdemli (Chooch AI) moderatörlüğündeki bu söyleşide, yapay zeka ve bilgisayarlı görü ile elde edilecek hızlı ve anlık iş değerini keşfedin.

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  • Protected: Chooch.ai eyeing future public listing, executive says

    There is no excerpt because this is a protected post.

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  • How can computer vision help detect the spillage of hazardous material?

    The potential applications of computer vision are nearly limitless: from counting the number of customers in a store to diagnosing complex medical issues. Practically any use case that involves visual data (i.e. images and videos) is suitable for computer vision, with state-of-the-art models often matching or exceeding human performance.

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  • Visual AI Railway Inspections: Better Detection of Railroad Defects and Obstacles

    Railway operators must conduct routine inspections and maintenance of tracks, trains, and other equipment to ensure the safe operation of railways. Through these inspection and maintenance activities, railway operators prevent service interruptions and, most importantly, reduce the chances of catastrophic railway accidents by resolving some of the most common causes of accidents, such as train and equipment failures, track defects, and other issues.

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  • What is Action Recognition?

    In computer vision, action recognition is the task of identifying when a person in an image or video is performing a given action. AI models can be trained to recognize a variety of actions, from running and sleeping to drinking, falling, or riding a bike.

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  • Leveraging AI for Better Patient Monitoring

    Healthcare facilities throughout the world are suffering from critical staff shortages, and the COVID-19 pandemic has only made the situation worse. According to November 2020 statistics from the U.S. Department of Health and Human Services, 18% of U.S. hospitals said that they were critically short on medical staff. Patient monitoring AI can dramatically improve the ability of hospitals and medical facilities to monitor situations.

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  • What is Event Detection?

    Within the field of computer vision, there are many possible subfields and tasks, each of them suitable for different applications. Event detection is a subfield of computer vision that analyzes input videos with the goal of determining when a particular anomalous event has occurred. AI models for event detection can be trained on a wide range of potential occurrences, from recognizing fights and traffic accidents to identifying falls, smoke, and fire.

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  • What is Facial Authentication?

    The field of computer vision seeks to help computers “see” images and videos at a high level, in the same way that humans can—but within this domain, there are many different subfields and applications. Facial authentication is a task in computer vision that seeks to verify a person’s identity by using their facial characteristics and features as biometric identifiers.

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  • What is Image Recognition?

    Computer vision is an extremely broad domain with dozens of subfields—so it’s no surprise you might get all these applications mixed up. Image recognition is a task in computer vision that seeks to identify the single best label for a given image, based on the most prominent object, person, or activity within the image.

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