Last year, eBay launched a visual search capability for its mobile app that makes it possible to find items with pictures instead of words. Now, the auction platform is making it even easier to use -- you don't even have to take screenshots of whatever it is you want to find anymore, because eBay will soon allow you to drag and drop images into its search bar. For instance, if you search for a "Hello Kitty purse" in the app and find one that catches your eye, you can drag that photo into the search bar to find listings featuring identical or similar items. It won't only give you a way to search for purchases quickly, but also to find the best deals on the website. According to eBay, its convolutional neural networks process the photo you use by transforming it into a vector representation.

Now you can start run the Python file and start the training.

This is the second story in our continuing series covering the basics of artificial intelligence. While it isn't necessary to read the first article, which …

This is the second story in our continuing series covering the basics of artificial intelligence. While it isn't necessary to read the first article, which covers neural networks, doing so may add to your understanding of the topics covered in this one. Teaching a computer how to'see' is no small feat. You can slap a camera on a PC, but that won't give it sight. In order for a machine to actually view the world like people or animals do, it relies on computer vision and image recognition.

Neural networks show impressive results working with image data. Today, well-trained technology out-performs the human brain when it comes to classifying millions of images or recognizing patterns in the photos taken by Kepler telescope. As a result, AI-enabled image analysis and processing have made their way to diverse areas, far beyond photography or social media. EBay, for example, launched a computer vision feature that allows to search products using image instead of keywords or description. Opting for Image Search, a customer can simply take a picture of the product and use it to find a similar one in the marketplace.

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.

Energy is limited in mobile systems, however, so for this possibility to become a viable opportunity, energy usage must be conservative. The Low-Power Image Recognition Challenge (LPIRC) is the only competition integrating image recognition with low power. LPIRC has been held annually since 2015 as an on-site competition. To encourage innovation, LPIRC has no restriction on hardware or software platforms: the only requirement is that a solution be able to use HTTP to communicate with the referee system to retrieve images and report answers. Each team has 10 minutes to recognize the objects in 5,000 (year 2015) or 20,000 (years 2016 and 2017) images.

Google hasn't been shy about borrowing cues from Pinterest. Its latest effort, however, may be more transparent than others. The company has confirmed to TechCrunch that it's testing a new Image Search on desktop with vertical results that will seem familiar if you're regularly browsing Pinterest for ideas. Each image now has captions along with badges describing what those images entail, such as a product or a video. And it won't surprise you to hear that clicking on a picture provides much, much more than before.

Researchers from the Massachusetts Institute of Technology (MIT) in Cambridge have been studying a machine learning algorithm they say makes the process of medical image registration more than 1,000 times faster.

The applications of image analytics are endless. Organizations are starting to realize the possibilities of how to extract value from unstructured data, such as images or video footage, to create a new and enticing customer experience within retail, entertainment, transportation and airport security, insurance claims, and more. Here are five image analytics applications that are unexpected, disruptive, and creative. On June 26th, I'll be talking about image analytics use cases at the Boston Area SAS Users Group in my Image Processing: Seeing the World through the Eyes of SAS Viya talk. Curious to know who attended the Royal Wedding?