"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.
Please Note: I reserve the rights of all the media used in this blog -- photographs, animations, videos, etc. they are my work (except the 7 mentioned artworks by artists which were used as style images). GIFs might take a while to load, please be patient. If that is the case please open in browser instead. The world today doesn't make sense, so why should I paint pictures that do? -- Pablo Picasso Here are the results, some combinations produced astounding artwork. Here's an image of a bride & graffiti, combining them results in an output similar to doodle painting. Here, you can see the buildings being popped up in the background.
The data set would be astronomy sub-images that are either bad (edge of chip artifacts, bright star saturation and spikes, internal reflections, chip flaws) or good (populated with fuzzy-dot stars and galaxies and asteroids and stuff). Let's say the typical image is 512x512 but it varies a lot. Because the bad features tend to be big, I'd probably like to bin the images down to say 64x64 for compactness and speed. It has to run fast on tens of thousands of images. I'm sort of tempted by the solution of adopting PlaidML as my back end (if I understand what its role is), because it can compile the problem for many architectures, like CUDA, CPU-only, OpenCL.
The Benelux Office for Intellectual Property (BOIP) has partnered with Clarivate Plc, a global leader in providing trusted information and insights to accelerate the pace of innovation, to improve its trademark research services. Using AI-powered technology from Clarivate, BOIP has simplified the process of researching image trademarks for uniqueness and availability. BOIP joins innovative IP offices around the world like the EU Intellectual Property Office, IP Australia and the Intellectual Property Office of Singapore who have adopted image recognition (IR)1 and new technologies to deliver innovative and more accessible services to users. Technology has transformed trademark research, automating a previously time-consuming and manual task. Today, the ability to search and compare image trademarks is essential as 40% of trademarks worldwide contain an image component2.
Pytorch does not come with "fit" and "predict" functions. We need a training loop! Now for training, we need the dataset loaders from torch, an optimizer and a learning rate scheduler. Please note that we have defined the loss criterion inside the training function. The loss function we are using here is Binary Cross Entropy with Logits (BCEWithLogitsLoss).
It's a while ago that I wrote about supervised image classification combining ImageJ and R in Bio7 (see video here). Later I decided to create a classification Graphical User Interface for Bio7 to make this process easier and implement typical functions for convenience. However it took some time to finish a first version of this plugin and also to create a first documentation – working on it when I had some time to spare. The plugin uses mainly the Java API of ImageJ to load, transform and filter images for a feature stack and transfer ROI (Region Of Interest) selections of this stack (pixel values) to R to train and classify the data with dedicated R scripts. The GUI itself reuses a powerful ImageJ component to collect selections (ROI Manager) and is embedded in a flexible view container (can be dragged around and detached).
In a new tutorial, Google researchers demonstrate how quantum computing techniques can be used to classify 28-pixel-by-28-pixel images illuminated by a single photon. By transforming the quantum state of that photon, they show they're able to achieve "at least" 41.27% accuracy on the popular MNIST corpus of handwritten digits -- a 21.27% improvement over classical computing approaches. The work, which the researchers say is intended to show how textbook quantum mechanics can shed new light on AI problems, considers the maximum achievable classification accuracy if an algorithm must make a decision after spotting the first "quantum" of light (i.e. On MNIST, the most classical computing can accomplish is detecting a photon that lands on one of the image's pixels and guessing at the digit from the light intensity distribution, obtained by rescaling the brightness of every image to a unit sum. The researchers' quantum mechanical approach employs beam splitters, phase shifters, and other optical elements to create a hologram-like inference pattern.
This post was originally published at thinkmobile.dev Looking for how to automatically test TensorFlow Lite model on a mobile device? Check the 2nd part of this article. Building TensorFlow Lite models and deploying them on mobile applications is getting simpler over time. There is a set of information that needs to be passed between those steps -- model input/output shape, values format, etc.
Image Classification is a pivotal pillar when it comes to the healthy functioning of Social Media. Classifying content on the basis of certain tags are in lieu of various laws and regulations. It becomes important so as to hide content from a certain set of audiences. I regularly encounter posts with a "Sensitive Content" on some of the images while scrolling on my Instagram feed. I am sure you must have too.
This post is a practical example of Neural Style Transfer based on the paper A Neural Algorithm of Artistic Style (Gatys et al.). For this example we will use the pretained Arbitrary Image Stylization module which is available in TensorFlow Hub. We will work with Python and tensorflow 2.x. Neural style transfer is an optimization technique used to take two images--a content image and a style reference image (such as an artwork by a famous painter)--and blend them together so the output image looks like the content image, but "painted" in the style of the style reference image. This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image.