"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
If Facebook has an unofficial slogan, an equivalent to Google's "Don't Be Evil" or Apple's "Think Different," it is "Move Fast and Break Things." It means, at least in theory, that one should iterate to try news things and not be afraid of the possibility of failure. In 2021, however, with social media currently being blamed for a plethora of societal ills, the phrase should, perhaps, be modified to: "Move Fast and Fix Things." One of the many areas social media, not just Facebook, has been pilloried for is its spreading of certain images online. It's a challenging problem by any stretch of the imagination: Some 4,000 photo uploads are made to Facebook every single second.
This breakthrough does not really require someone to feed the information to the computer or be their eyes so to say. Because this new technique allows machines to interpret and categorize whatever they see in images or videos. In other words, computers now have their own eyes. Therefore, they work independently with the ability to recognize whatever is around them. Here the model will predict only one label per image. What this means that no matter the input or the diversity in the image, the machine will assign only a single label.
Got a stack of Magic: The Gathering cards sitting somewhere in storage? With the game's "Modern" format, chances are you might be sitting on at least a couple of ones that could be worth selling. One of the most popular places to buy and sell trading cards online is eBay. What keeps most people parting with their collections is that it can be time-consuming to list every individual card. But eBay has a plan to speed up the process. In an announcement that flew under our radar until Gizmodo picked it up this morning, eBay said it's updating its Android and iOS app with image recognition capabilities.
Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Deep Convolutional Neural Networks in deep learning take an hour or day to train the mode if the dataset we are playing is vast. The approach is we reuse the weights of the pre-trained model, which was trained for some standard Computer Vision datasets such as Image classification (Image Net). Extensive deep Convolutional networks for large-scale image classification are available in Keras, which we can directly import and can be used with their pre-trained weights. Let's now understand how to use VGG16 pre-trained on 10,000 categories(Image Net) for the Distracted driver Detection dataset.
Machine learning is a field of computational science which first emerged in the 1950s. However, our ability to effectively harness the power of machine learning techniques was only truly realised in the 1990s. In ecology, the earliest adoption of machine learning came about in the early 2000s, when regression tree algorithms were applied to spatial data to predict species distributions. This was quickly adapted in the field of marine ecology to study the distribution of many pelagic species. Since that time, machine learning algorithms have been adapted and applied in various studies in the marine environment, from population models, image recognition, and experimental studies.
The paper trained their networks by crops from the renowned ImageNet image recognition dataset. We then define a function to map each image from the dataset to (128, 128) crops and a (32, 32) low-resolution copy of it. This will be executed before every training epoch. We define the residual generator architecture using Tensorflow. Functions were defined to build an entire residual block, and element-wise sum skip connections were also implemented.
The Image Recognition has over 300 startups that comprise of companies offering software that can identify places, people, objects and actions in images or digital videos. This includes companies offering services like image recognition software facial recognition software, object recognition software amd optical character recognition. Image Recognition is one of the most active sectors for investors, with an overall funding of USD 8.2B in 150 companies. It is also interesting to note that more than half of the funding has been raised in the last 3 years (2018-2020). Plug and Play Tech Center, Deep Learning, Y Combinator, Capital Factory and Alibaba Group are amongst the most active investors in this sector, by number of investments. Applications, facial recognition, offline retail, shelf management and security systems are some of the top business models attracting major funding.
Welcome to the new world. Glimmers of optimism are beginning to invade the public debate. And by public, I don't just mean those progressive spring breakers fighting for their freedom on the beaches. Many businesses are now seriously considering a return to their office buildings, some abandoned a year ago. Things, of course, still won't be quite the same.
Handwriting recognition is of crucial importance to both Human Computer Interaction (HCI) and paperwork digitization. In the general field of Optical Character Recognition (OCR), handwritten Chinese character recognition faces tremendous challenges due to the enormously large character sets and the amazing diversity of writing styles. Learning an appropriate distance metric to measure the difference between data inputs is the foundation of accurate handwritten character recognition. Existing distance metric learning approaches either produce unacceptable error rates, or provide little interpretability in the results. In this paper, we propose an interpretable distance metric learning approach for handwritten Chinese character recognition. The learned metric is a linear combination of intelligible base metrics, and thus provides meaningful insights to ordinary users. Our experimental results on a benchmark dataset demonstrate the superior efficiency, accuracy and interpretability of our proposed approach.
This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines. The new dataset may be useful for researchers to train handwriting text recognition models as a benchmark for comparing different models. It consists of 9 694 images and text files corresponding to lines in historical documents. The open machine learning competition Digital Peter was held based on the considered dataset. The baseline solution for this competition as well as more advanced methods on handwritten text recognition are described in the article. Full dataset and all code are publicly available.