With the emergence of IoT, there is a rising interest in applying Internet of Things (IoT) technology in the smart homes for making occupants' life more convenient. The convenience is underpinned by the principle of the least effort, i.e. the premise that humans would usually want to achieve goals with the least cognitive and physical efforts [2]. IoT refers to the networked interconnection of everyday things, which are augmented with capabilities such as sensing, actuating, and communication [21]. The availability of IoT devices including switch sensors, infrared motion sensors, pressure sensor, wearable sensors, accelerators, temperature, humidity, and light sensors have the potential to realize the convenience. It is a challenge that IoT devices are highly diverse in supporting infrastructure such as different programming language and communication protocols [5].

Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that we're using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. The unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that we're enabling machines to do the thing that our brains seem to do so easily: identify what we're perceiving in the real world around us. The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest.

One of the most widely adopted of the seven patterns of AI is the Patterns and Anomalies pattern. Machine learning is particularly good at digesting large amounts of data very quickly and identifying patterns or finding anomalies or outliers in that data. The "pattern-matching pattern" is one of those applications of AI that itself seems to repeat often, and for good reason as it has broad applicability. The goal of the Patterns and Anomalies pattern of AI is to use machine learning and other cognitive approaches to learn patterns in the data and discover higher order connections between that data. The objective is to determine whether a given data point fits an existing pattern or if it is an outlier or anomaly, and as a result find what fits with existing data and what doesn't.

We provide end-to-end image analysis and vision AI expertise on different business verticals. Your simple data can be turned into a working AI. We will clean and analyze the data you'll provide. We will provide and maintain an AI-powered interface you can use. From trademark searches based on your logo (rather than the traditional text-based searches others offer), AI-powered image search solution for your enterprise, to our generative image-building solution that takes the images you input and develops new iterations.

It belongs to every aspect of our daily lives. Starting from the design and colour of our clothes to using intelligent voice assistants, everything involves some kind of pattern. When we say that everything consists of a pattern or everything has a pattern, the common question that comes up to our minds is, what is a pattern? How can we say that it constitutes almost everything and anything surrounding us? How can it be implemented in the technologies that we use every day?

Image features For this task, first of all, we need to understand what is an Image Feature and how we can use it. Image feature is a simple image pattern, based on which we can describe what we see on the image. For example cat eye will be a feature on a image of a cat. The main role of features in computer vision(and not only) is to transform visual information into the vector space. Ok, but how to get this features from the image?

Computer vision is a field in computer science that falls under the umbrella of artificial intelligence (AI). Computer vision (CV) software developers strive to give computers the ability to process images in much the same way that humans do. They expect the computer will be able to identify objects, to make appropriate decisions based on what it "sees," and then to produce relevant outputs. Today, facial recognition software, autonomous vehicles, certain forms of surveillance, and gesture recognition are just a few examples of CV systems at work. Why is computer vision so complicated? Every parent can recall their child going through phases when "what's that?" became a recurring question.

Researchers from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences have proposed a product image recognition method with guidance learning and noisy supervision. The study was published in Computer Vision and Image Understanding. Instead of collecting product images by laborious and time-intensive image capturing, the team introduced a novel large-scale dataset called Product-90. Consisting of more than 140K images with 90 categories, the dataset was related to Clothing1M (a large-scale public dataset designed for learning from noisy data with human supervision), but contained many more categories. Images were collected from reviews on e-commerce websites.

Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? With the rise in Artificial Intelligence enabled Face and Image Recognition technologies, the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today.

Matching shapes can be much difficult task then just matching images, for example recognition of hand-written text, or fingerprints. Because most of shapes that we trying to match is heavy augmented. I can bet that you will never write to identical letters for all your life. And look at this from the point of people detection algorithm based on handwriting matching -- it would be just hell. Of course in the age of Neural networks and RNNs it also can be solved in a different way then just straight mathematics, but not always you can use heavy and memory hungry things like NNs.