"Image understanding (IU) is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent."
– Image Understanding, by J.K. Tsotos. In Encyclopedia of Artificial Intelligence. Stuart C. Shapiro, editor. 1987. New York: John Wiley & Sons.
When launching SAP Leonardo Machine Learning Foundation, SAP started on a mission to overcome these challenges and help all customers transition to the intelligent enterprise, no matter their level of digital maturity and AI expertise. Now, SAP expands the capabilities of its machine learning platform, including the training of image classification services with training data belonging to the customer, the opportunity for customers to deploy their own models on SAP Leonardo Machine Learning Foundation, and new ready-to-use services. To help customers and partners address a larger number of use cases, SAP has opened the enterprise-class model training capability of SAP Leonardo Machine Learning Foundation. It is now possible for customer to tailor services to their business needs by training services on their unique data. This new functionality is enabled via a secure extraction of data via SAP Cloud Platform and predefined training routines.
Computers can be fooled into thinking a picture of a taxi is a dog just by changing one pixel, suggests research. The limitations emerged from Japanese work on ways to fool widely used AI-based image recognition systems. Many other scientists are now creating "adversarial" example images to expose the fragility of certain types of recognition software. There is no quick and easy way to fix image recognition systems to stop them being fooled in this way, warn experts. In their research, Su Jiawei and colleagues at Kyushu University made tiny changes to lots of pictures that were then analysed by widely used AI-based image recognition systems.
Apple has used image recognition algorithms to search and organize its Photos app since iOS 10 debuted in 2016 -- but a viral tweet put the tool in the spotlight after it appeared to be stockpiling photos of women's bras in a separate storage category within the Photos app. Before going any further, it's important to make a few things clear: There isn't a separate "folder" filled with your intimate pics within the Photos app, and no one else can access your photos without you giving them permission. So the chance of your private photos leaking is exactly the same as it was before. And what if you already have folders of your intimate photos on your phone, secret or otherwise? SEE ALSO: Photographer creates a'dudeoir' photoshoot to perfectly capture the essence of autumn Now back to this weird controversy: It all started when Twitter user @ellieeewbu spotted the search category and took to Twitter to spread the word.
Apple isn't making a special folder of your nude photos. But it does seem that way. A newly viral post is encouraging people to find out the "folder", and look at what is contained in there. And while some of the reports are true, they aren't all – or as intimate – they seem. The tweet – since reposted more than 10,000 times – instructs all women to go and search "brassiere" in their pictures.
Attention readers: We invite you to access the corresponding Python code and iPython notebook for this article on GitHub. Image classification can perform some pretty amazing feats, but a large drawback of many image classification applications is that the model can only detect one class per image. With an object detection model, not only can you classify multiple classes in one image, but you can specify exactly where that object is in an image with a bounding box framing the object. The TensorFlow Models GitHub repository has a large variety of pre-trained models for various machine learning tasks, and one excellent resource is their object detection API. The object detection API makes it extremely easy to train your own object detection model for a large variety of different applications.
Digital image processing is a discipline that studies image processing techniques. The image referred in this research is a static image form vision sensors (webcam). Mathematically, the image is a continuous function of light intensity on two-dimensional field. In order to be processed by a computer, an image should be presented numerically with discrete values. A digital image can be represented by a two-dimensional matrix f (x, y) consisting of M columns and N rows.
This tutorial shows how to build an image recognition service in Go using pre-trained TensorFlow Inception-V3 model. The service will run inside a Docker container, use TensorFlow Go package to process images and return labels that best describe them. Full source code is available on GitHub. Inside project's root directory create docker-compose.yaml It uses official TensorFlow Docker image as its base image.
The front of the device features a cutout at the top of the new OLED Super Retina display housing a new True Depth camera system for the Face ID facial recognition system and for taking selfies with Apple's Portrait Mode. The iPhone X will have Apple's latest processor, the A11 Bionic with an integrated Neural Engine for face recognition, which now has six cores – up from last year's A10 with four cores. Apple also unveiled new animated emoji characters it calls "animoji", which allow users to map facial expressions on to little characters, such as a robot, fox, unicorn, or anthropomorphised poo using the iPhone X's facial recognition system. The iPhone 8 and 8 Plus both have Apple's new A11 Bionic chip, but without the Neural Engine of that fitted to the iPhone X, have improved screens with the company's True Tone feature, improved speakers and keep its current form with a home button with Touch ID 2 fingerprint scanner, but lack facial recognition and an all-screen design.
Amarjot Singh at the University of Cambridge and his colleagues trained a machine learning algorithm to locate 14 key facial points. The researchers then hand-labelled 2000 photos of people wearing hats, glasses, scarves and fake beards to indicate the location of those same key points, even if they couldn't be seen. The system accurately identified people a wearing scarf 77 per cent of the time – a cap and scarf 69 per cent of the time and a cap, scarf and glasses 55 per cent of the time. Last year, a team of researchers from Carnegie Mellon University found they could trick face recognition software by wearing specially designed glasses.
We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.