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What are Smart Cities?

As Internet-of-Things (IoT) technology has gone mainstream, you may have heard of the smart home, but have you heard of the smart city? If not, you may be hearing more about these soon as smart cities using computer vision AI and a large network of information and communications technologies (ICT) are becoming a reality.

In a smart home, networked automation devices like smart thermostats, automated lighting and smartphone-controlled door locks allow homeowners to keep tabs on everything going on in and around the home using computers and smart devices. In a smart city, the same concept applies, just on a massive scale.

Increasing Municipal Efficiency

One of the biggest goals of smart cities is to improve efficiency. When large numbers of people, vehicles, residences, stores and government offices are crammed into tight quarters, trying to keep the gears of a city running smoothly can be difficult. Government agencies tasked with carrying out official duties tend to have a harder time as city populations grow, but the smart cities concept can help.

Think about this: In a traditional city, traffic management is a big job. If roadwork needs to be completed, traffic needs to be diverted and traffic signal operations may need to be changed. In a smart city, computer vision AI can monitor areas where traffic has been diverted to intelligently change traffic signals to create more efficient traffic patterns. This reduces the labor required for city officials and can reduce accidents as well.

Reduced Crime is a Benefit

Smart cities may also be safer due to connected technologies. In a traditional city setting, a gunshot that goes off in the commission of a crime may go undetected by people outside the immediate vicinity of the shot.

In a smart city, audio sensors that are trained to listen for gunshots can immediately detect the sound of the shot and pinpoint its location by measuring the echoes off of nearby structures. Simultaneously, the sensor can send a recording of the shot along with a video of the area and a notification to the nearest available police unit. A faster response time leads to more bad guys behind bars and safer communities for all.

Read a similar article about annotation services for computer vision here at this page.

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What is Annotation Ontology

Machine learning and computer vision applications require substantial training before deployment. Systems must learn to understand and recognize what they're looking at before reacting and executing functions. Whether in a healthcare setting or a warehouse, AI systems must understand the context surrounding the objects they see.

That's where ontology comes in. Ontologies provide more than top-level visual information about an object. They offer more detailed conceptual information, such as the relationships one object has to another or how it's represented in data. Also known as taxonomies or labeling protocols, ontologies play a big part in allowing you to semantically program computer vision and machine learning models to understand the complexities of the data they view.

It's what makes these intelligent systems capable of understanding complex information similarly to the human brain.

How Annotation Ontology Works

Think of how you recognize objects in your everyday life. If you see a dog walking on the street, you can easily define it as such. But you use tons of semantic information to get there. For example, you know how a dog relates to a cat, allowing you to differentiate the two animals. You can also use semantic information to see if that dog is a stray, separated from its owner or aggressive. All that information combines to help you learn about the dog you see. It's about using observations and drawing inferences from everything you see.

Computer vision and machine learning models need the same deep level of understanding to perform efficiently.

In annotation, ontologies are hierarchical structures that capture different levels of information. They allow for fine-grain differentiation and can provide more detailed annotations. It goes beyond top-level descriptors, including nested attributes to provide a more comprehensive understanding of the target object.

At the top of the hierarchy are classes or categories. They represent the highest level of concepts you want to express. Below that are nested classifications that go deeper into the object's attributes, for example, a top-level class could identify an object as a dog. Nested categories can then differentiate things like color, the presence of a collar, movement, speed, etc.

Read a similar article about rareplanes dataset here at this page.

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Data Engineering Your AI Model

Once we’ve started training and testing a model, we most likely will need to identify some missing data types. How much data should we generate and add to our training set to address the problem? Well, it depends on the goal we are trying to achieve. Similar to architecture engineering, by choosing the sample types and their distribution, we actually define the problem our model will eventually be able to solve and whether our model will be biased or unbiased with respect to any of the attributes we can control when we generate the data. We call this task data engineering read more

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How to Debug Label Quality

When it comes to preparing machine learning models, label quality is paramount. What you feed a model directly impacts its efficiency and accuracy. Inaccurate labels will prolong the training process and require more time before deployment.

Fortunately, there are ways to debug label quality to establish the best ground truth for your models.

Automated Data Annotation

One of the best ways to debug labels is to invest in automated annotation systems. Image annotation for machine learning teams is a game-changer that can dramatically reduce the amount of manual work required before deployment.

Manual annotation is a time-consuming and resource-heavy process. With automated annotation, you can save time, reduce costs and accelerate active learning workflows.

Automation uses micro-models. Teams have full control over the models, allowing them to utilize tools for maximum efficiency. Micro-models can apply problem-specific heuristics while discovering classification and geometric errors on a much smaller scale. The models are refinable, letting teams validate performance, version label sets and more.

The beauty of automated annotation is that it enables teams to focus on more pressing tasks. They can spend less time debugging labels, devoting resources to evaluation and refinement to keep things running smoothly.

Rich Labeling Structures

With automated annotation, you need ways to accommodate modalities. Having the means to configure taxonomy provides greater flexibility. Teams can create nested labeling structures while keeping modalities in one place, giving automated annotation systems the rich context to label images more accurately than ever.

It's one of many features that can improve the efficiency of automated labeling systems while reducing errors.

Automated Quality Control

The best tools that handle image annotation for machine learning teams will also use automation to debug labels. Assessment and visualization tools offer precise estimations of the label quality. Teams can analyze model performance and spot potential issues negatively impacting ground truth.

That insight gives teams the insights they need to refine micro-models. Additional features like versioned data facilitate experimentation to get things right. Teams can also create custom pipelines and filters to maximize accuracy and reduce time to deployment.

Read a similar article about computer vision models for radiology annotation here at this page.

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4 Ways to Deliver Cleaner Colonoscopy Datasets

Colonoscopies are exams that can help healthcare providers spot potentially life-threatening issues within the intestines. These exams have been around for decades and are largely effective. But the human element still provides varying results for patients.

Heterogeneous performance during endoscopy can lead to differing opinions and inaccurate diagnoses. Fortunately, things are changing, and there are many ways to deliver cleaner colonoscopy datasets.

Computer Vision

Computer vision is a form of AI that's dramatically changing healthcare. Instead of relying solely on a healthcare provider's knowledge and keen eye, operators can use this technology to spot issues. It works similarly to the human eye. Machines can "view" colonoscopy imaging and learn how to identify artifacts that matter.

AI models for gastroenterology computer vision have the potential to substantially improve diagnostic accuracy, helping providers make better decisions for their patients.

AI-Assisted Annotation

Of course, computer vision needs detailed training before operators can deploy it. That's where annotation comes in.

Accurate annotation involves creating the context for the images computer vision sees. Before deployment, computer vision needs to process thousands of datasets. The more information it learns, the better it performs.

Traditionally, manual annotation was the only way to get clear colonoscopy datasets. But AI-assisted annotation changes that. High-quality training platforms can label colonoscopy videos of any length, annotating content in many ways. Because AI is at the helm, operators can focus less time on menial tasks and focus on other ways to improve training efficiency.

Infinite Oncologies

Another way to improve AI models for gastroenterology computer vision is to create as many gastroenterology models as possible. Colon issues are complex, and there are many potential issues to look for during endoscopy. Training computer vision to spot only polyps can result in missed problems.

Multiple classifications and sub-classifications can help operators build a highly accurate gastroenterology model capable of identifying several issues.

AI and Human Review

Finally, it's important to review and correct datasets. Accuracy is paramount, as a computer vision model is only as accurate as its training models.

AI can help improve accuracy by spotting areas of interest and tagging artifacts. Operators can then go in to review datasets, make necessary corrections and feed machine learning models highly accurate datasets for faster deployment.

Read a similar article about computer vision models here at this page.

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The Advantages of AI Development in Data Analysis

Artificial intelligence is no longer a subject of science fiction. It's already revolutionizing industries far and wide. Every year, AI improves and paves the way for more innovation.

While most people think of what AI can do for healthcare, robotics and consumer technology, new developments are also changing the face of data analysis. Here's how.

Streamlined Data Preparation

We live in the age of big data, and organizations of all sizes have a repository of information they need to process. In the past, the only way to make use of any of that collected data was to prepare it manually. Data scientists had to generate reports and handle exploration on their own.

Fortunately, that's not the case now. AI can produce models and help visualize key information in only a few clicks. Companies investing in machine learning can also benefit from continued AI development. For example, AI labeling for computer vision significantly reduces the amount of manual work required to train models.

Better Data Accuracy

Another common problem in data analysis is potential inaccuracies. Inaccurate data can lead to false interpretations and countless flaws. It defeats the entire purpose of having the data.

Errors are more common than most realize. Humans aren't perfect, so issues are bound to happen.

AI can greatly reduce the number of flaws in massive data sets. The technology can learn common human-caused errors and detect them when they occur. The technology can detect and resolve deficiencies automatically, providing peace of mind that you're working with correct data.

Less Human Intervention

The biggest advantage of AI in data analysis is the reduced reliance on manual work. Not only is manual work expensive, but it's also prone to errors. AI takes most of the human aspect out of the equation entirely.

AI labeling for computer vision only needs manual intervention when clarifying errors, reducing the resources invested in getting systems deployed. AI can also surface insights from enormous data sets in minutes. Instead of manually sifting through numerical data, you can rely on technology to get the information you want accurately and efficiently.

Continued developments can help AI learn data nuances, allowing it to spot patterns, alert you to anomalies and more.

Read a similar article about tools for machine learning teams here at this page.

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What Are Datasets?

Datasets are an integral part of data management. Modern businesses utilize mountains of data in many ways. It can help detect new opportunities, prepare organizations to respond to market changes, predict trends, refine operations and more. Datasets help make sense of the information, paving the way for automation and AI manipulation.

The best way to think of datasets is to look at them as collections of information. When presented, datasets typically appear in a tabular pattern that consists of several columns and rows. The columns provide information about specific variables. Meanwhile, the rows represent an item in the dataset. This setup can answer questions and describe values for variable queries.

There are many types of datasets available. The ones utilized will depend on the needs of data scientists. It's an ordered collection of information that pertains to a specific topic. Whether you use an image annotation tool to prepare computer vision models or collect data to track and analyze changes in a production line, datasets make those tasks possible.

Types of Datasets

Datasets are versatile, and data scientists use different types based on the information they're observing and what they want to learn.

The most basic is numerical. A numerical dataset uses numbers over natural language to represent values. They provide quantitative data and pave the way for complex mathematical operations.

When there is more than one variable to observe, scientists will use bivariate or multivariate datasets. The former focuses on the relationship between two variables. Meanwhile, the latter contains measurements gathered as a function of at least three variables. Both are versatile and relevant in many industries.

Categorical and correlation datasets are additional models that prioritize more complex information. Categorical datasets represent the features of an object. An image annotation tool can use these datasets to recognize characteristics in visual data, preparing computer vision models. Correlation datasets provide information about the statistical relationship between two values and are commonly used to predict correlations.

Data science is a complex facet of modern business, but it can provide meaningful insight. Using the right technology allows organizations to stay competitive, maximize productivity and boost the bottom line.

Read a similar article about SAR image segmentation here at this page.

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Benefits of Video Annotation

Artificial intelligence and machine learning have transformed many industries, streamlining workflows and making many impressive feats possible. One of the more compelling aspects of AI is how it interacts with visual inputs like photos and videos. Computer vision enables computers to identify objects and understand complex visuals.

But before machines can learn to process that content, it needs data annotation. Data annotation labels the information in videos, paving the way for training and eventual AI-powered recognition.

Whether it's for government applications, healthcare, or manufacturing, here are some benefits of AI video annotation.

Easy Object Detection

Implementing computer vision and training a machine to distinguish between objects in a video requires tons of data processing. Computers must use machine learning to determine what it's viewing accurately. Video annotation can streamline training and fast-track how computers learn.

Think of it as repeatedly telling the computer what objects it's trying to identify. When you have annotation, you're creating a point of reference and a single source of truth. Over time, the computer understands that annotation and learns to identify it automatically. Once machines reach that point, it opens up a world of possible applications.

Training for Object Tracking

Videos are different from static images. The media is ever-changing, and computers must learn more than simple shapes and outlines. AI video annotation provides a deeper understanding of objects. It helps differentiate unique variations, allowing computers to track objects the entire time they're in focus.

Think of what's possible after machine learning does its job. Manufacturing facilities can monitor production cycles, following specific objects through the line. Transportation hubs can keep track of passengers as they make their way through busy terminals and gateways. Annotation facilitates those capabilities, unlocking more potential with the technology.

Object Localization

Annotation can also locate objects once they come into focus. Video annotation teaches machines the complex way target objects move and change as they move through video. Computers can then automatically detect, follow, and locate those objects for however long they are in focus.

Those capabilities can help many industries avoid delays, improve efficiency, and boost data accuracy.

Find the best AI image annotation platform by visiting this website.

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SLAM and the Evolution of Spatial AI

Andrew Davison is a professor of Robot Vision at the Department of Computing, Imperial College London. In addition, he is the director and founder of the Dyson robotics laboratory. Andrew pioneered the cornerstone algorithm for robotic vision, gaming, drones and many other applications – SLAM (Simultaneous Localisation and Mapping) and has continued to develop the SLAM direction in substantial ways read more