Human sight is based on a lifetime of learning with context to train how to identify specific objects or recognize human faces or individuals in visual scenes. Hence, modern artificial vision technology uses machine learning and deep learning methods to train machines how to recognize objects, faces, or people in visual scenes.

It is important to note that to successfully build any image classification model that can scale or be used in production, the model has to learn from enough data. Transfer learning is an image classification technique leveraging existing architectures that have been trained to learn enough from huge data samples. The learned feature or task is then utilized to identify similar samples. Another term for this is knowledge transfer.

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After a decade of deep learning training, aiming to improve the accuracy and performance of algorithms, we now enter the era of deep learning deployment. AI model optimization and new architectures made it possible to drastically reduce the size of machine learning models while increasing computational efficiency. This makes it possible to run deep learning computer vision without depending on expensive and energy-consuming AI hardware and GPUs in data centers.

Object Detection is often applied to video streams, whereby the user tracks multiple objects simultaneously with unique identities. Popular architectures of object detection include the AI vision algorithms YOLO, R-CNN, or MobileNet.

As a product manager of a popular workspace tool for creating, organizing, and sharing various types of content, you’ve been working on an AI writing assistant.

About us: Viso.ai provides the leading end-to-end Computer Vision Platform Viso Suite. Our technology automates how teams can build, deliver, and scale their computer vision applications. Get a demo for your company.

Such layered neural networks enable a computer to learn about the context of visual data from images. If enough data is available, the computer learns how to tell one image from another. As image data is fed through the model, the computer applies a CNN to “look” at the data.

Used as a key strategy in smart cities for crowd analysis, weapon detection, traffic analysis, vehicle counting, self-driving cars/autonomous vehicles, and infrastructure inspection.

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Naturally, nobody likes being taken for a ride so when your users actually click on the feature, you need to explain what you’ve just done and why. Like in the modal below.

Traditional machine vision systems commonly depend on special cameras and highly standardized settings. In contrast, modern deep learning algorithms are much more robust, easy to re-use and re-train, and allow the development of applications across industries.

With the idea of transfer learning, Computer Vision engineers have built scalable solutions in the business world with a small amount of data. Existing architectures for image classification include ResNet-50, ResNet-100, ImageNet, AlexNet, VggNet, and more.

Meanwhile, we face a boom in high-performance deep learning chips that are increasingly energy-efficient and run on small form-factor devices and edge computers. Current popular deep learning AI hardware includes edge computing devices such as embedded computers and SoC devices, including the Nvidia Jetson Tx2, Intel NUC, or Google Coral.

Traditionally, CV and AI, in general, were pure cloud solutions due to the unlimited availability of computing resources and easy scalability to increase resources. Web or cloud CV solutions require uploading all images or photos to the cloud, either directly or using a computer vision API such as AWS Rekognition, Google Vision API, Microsoft image recognition API (Azure Cognitive Services), or Clarifai API.

AI accelerators for neural networks can be attached to embedded computing systems. The most popular hardware neural network AI accelerators include the Intel Myriad X VPU, Google Coral, or Nvidia NVDLA.

Computer vision for augmented and virtual reality creates immersive experiences by integrating real-world or virtual environment perception, thus allowing users to interact with virtual surroundings in real-time.

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Deep learning tasks are computationally heavy and expensive, depending on significant computing resources, and require massive datasets to train models on. Compared to traditional image processing, deep learning algorithms enable machines to learn by themselves, without a developer programming it to recognize an image based on pre-determined features. As a result, deep learning methods achieve very high accuracy.

Among applications of computer vision in healthcare, a prominent example is automated human fall detection to create a fall risk score and trigger alerts.

To be able to roll out the feature in such a way, you wrap it in a feature flag or feature toggle. They allow you to switch the feature on and off quickly without modifying the code.

The extensible architecture of Viso Suite helps companies to re-use and integrate existing infrastructure (cameras, AI models, etc.) and connect computer vision with BI tools (PowerBI, Tableau) and external databases (Google Cloud, AWS, Azure, Oracle, etc.).

However, if low-fidelity testing gives you promising results, you can invest more effort into the idea and make your prototypes progressively more realistic.

Image processing is a key aspect of AI vision systems since it involves transforming images to extract certain information or optimize it for subsequent tasks in a CV system. Basic image processing techniques include smoothing, sharpening, contrasting, de-noising, or colorization.

Whenever issues arise, you fix them. Gradually, as the product gets better, you start releasing it to new user cohorts and continue testing.

Computer Vision (CV) is a field of Artificial Intelligence (AI) that deals with computational methods to help computers understand and interpret the content of digital images and videos. Hence,  CV  aims to make computers see and understand visual data input from cameras or sensors.

Computer vision in Insurance leverages AI vision for automated risk management and assessment, claims management, visual inspection, and forward-looking analytics.

Or you can use other existing solutions and botch them together to achieve the desired functionality before you develop your own code.

Computer vision in pharmaceutical industries is used for packaging and blister detection, capsule recognition, and visual inspection for equipment cleaning.

While this provides a short-term solution, it is not realistic for evolving with the needs of the use case or business. Additionally, stringing together multiple tools and platforms quickly becomes complicated and expensive.

Hence, CV at the edge leverages the advantages of the cloud and the edge to make AI vision technology scalable, and flexible. This supports the implementation of real-world applications. On-device CV does not depend on data offloading and inefficient centralized image processing in the cloud.

Since Edge AI  involves the Internet of Things (AIoT) to manage distributed devices, the superior performance of Edge CV comes at the cost of increased technical complexity.

The focal length refers to the distance between the optical center of the lens and the image sensor.

Industry leaders worldwide deliver their computer vision projects with Viso Suite infrastructure to build, deploy, and monitor computer vision applications.

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Computational vision with deep learning has also achieved human performance in classifying skin cancer with a level of competence comparable to dermatologist experts.

Monitoring customer support calls and chats is a good way to get qualitative data from customers, and it’s dead easy to automate it with AI tools these days.

According to an analysis of the AI vision market by Verified Market Research (Nov 2022), the AI in Computer Vision Market was valued at USD 12 Billion in 2021 and is projected to reach USD 205 Billion by 2030. Accordingly, the computer vision market is rapidly growing at a CAGR of 37.05% from 2023 to 2030.

Computer vision tasks seek to enable computer systems to automatically see, identify, and understand the visual world, simulating human vision using computational methods.

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The computer vision platform Viso Suite enables leading organizations worldwide to develop, scale, and operate their AI vision applications. As the world’s only end-to-end AI vision platform, Viso Suite provides software infrastructure to dramatically accelerate the development and maintenance of computer vision applications across industries (Get the Economic Impact Study).

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Modern deep-learning computer vision methods can analyze video streams of common, inexpensive surveillance cameras or webcams to perform state-of-the-art AI video analytics.

If the feedback is positive, that’s great. However, when users submit negative feedback, follow up with them to know in detail about their issues with the new feature.

Next, the trained algorithm can be applied to newly generated images, for example, videos of surveillance cameras, to recognize a helmet. This is, for example, used in computer vision applications for equipment inspection to reduce accidents in construction or manufacturing.

In computer vision, specifically, real-time object detection, there are single-stage and multi-stage algorithm families.

As a result, computer vision systems use image processing algorithms to allow computers to find, classify, and analyze objects and their surroundings from data provided by a camera.

For example, the next step could be pretending that the feature exists but doing all the work manually behind the scenes. This kind of experiment is called Wizard of Oz.

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While the problem of “vision” is trivially solved by humans (even by children), computational vision remains one of the most challenging fields in computer science, especially due to the enormous complexity of the varying physical world.

Existing architectures for OCR extractions include EasyOCR, Python-tesseract, or Keras-OCR. These machine learning software tools are popularly used for Number Plate Recognition as an example.

Apart from tracking user in-app behavior, pay attention to support tickets and bug reports. That’s how you will be able to identify technical issues.

What is computer vision, and how does it work? This article provides a complete guide to Computer Vision (CV), one of the key fields of artificial intelligence (AI).

❌ Complexity: traditionally, dark launches required multiple production environments or complex routing configurations. It’s less of an issue now thanks to feature flags/feature toggles but it still can result in fairly complex code.

Pose Estimation makes computers understand the human pose. Popular architectures around Pose Estimation include OpenPose, PoseNet, DensePose, or MeTRAbs. These are useful for real-world problems including crime detection via poses or ergonomic assessments to improve organizational health.

Fake door testing is a kind of prototype testing. In this case, the prototype is very low-fidelity as you have nothing under the hood.

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Computer vision is an imperative aspect of companies using AI today. If you enjoyed this article, we suggest you read more about the topic:

In video surveillance and security, person detection is performed for intelligent perimeter monitoring. Another popular use case is deep face detection and facial recognition with above-human-level accuracy.

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The organization and setup of a computer vision system vary based on the application and use case. However, all computer vision systems contain the same typical functions:

The de facto standard tool for image processing is OpenCV, initially developed by Intel and currently used by Google, Toyota, IBM, Facebook, and so on.

As soon as a user interacts with the feature, trigger an in-app survey to ask them for their feedback. Such contextual surveys are a great source of valid insights because the experience is still fresh in users’ minds.

Computer vision aims to artificially imitate human vision by enabling computers to perceive visual stimuli meaningfully. It is, therefore, also called machine perception, or machine vision.

In recent years, new deep learning technologies achieved great breakthroughs, especially in image recognition and object detection.

Edge AI, also called Edge Intelligence or on-device ML, uses edge computing and the Internet of Things (IoT) to move machine learning from the cloud to edge devices near the data source such as cameras. With the massive, still exponentially growing amount of data generated at the edge, AI is required to analyze and understand data in real-time without compromising the privacy and security of visual data.

Today, deep learning enables machines to achieve human-level performance in image recognition tasks. For example, in deep face recognition, AI models achieve a detection accuracy (e.g., Google FaceNet achieved 99.63%) that is higher than the accuracy humans can achieve (97.53%).

In dark launches, nobody tells the users that they’re taking part in the test or that they are using a new feature. That’s why they’re called ‘dark’.

We see a trend in falling computer vision costs, driven by higher computational efficiency, decreasing hardware costs, and new technologies. As a result, more and more CV applications have become possible and economically feasible – further accelerating adoption.

Fake door testing is the least labor or resource-intensive way of testing your feature ideas. The feature doesn’t need to exist as long as you convince your users it does.

Let’s imagine you’re an Asana product manager and you want to build the Goals feature It’s a theoretical example because, to our knowledge, Asana never did such a test.

Viso Suite covers the entire lifecycle of computer vision, from image annotation and model training to visual development, one-click deployment, and scaling to hundreds of cameras. The platform provides critical capabilities such as real-time performance, distributed Edge AI, Zero-Trust Security, and Privacy-preserving AI out-of-the-box.

Also, Edge CV does not fully depend on connectivity and requires much lower bandwidth and reduced latency, especially important in video analytics. Therefore, Edge CV allows the development of private, robust, secure, and mission-critical real-world applications.

When you carry out a dark launch, the risk is limited but still significant. To reduce it, there are a few more tests you can conduct to ensure you’re developing the right features and they have a solid technical foundation.

A dark launch is an easy way to reduce the risk involved in feature releases. By enabling the feature to a small number of users, you can still test it under real-world conditions. If things go wrong, though, the damage is limited.

A dark launch is a release of production-ready software features to a small user segment. The practice is commonly used in modern software development to test new software stability and performance before launching it to all users.

Companies are rapidly introducing  CV technology across industries to solve automation problems with computers that can see. Visual AI technology is quickly advancing, making it possible to innovate and implement new ideas, projects, and applications including:

For example, video surveillance cameras in retail stores track movement patterns of customers and perform people counting or footfall analysis to identify bottlenecks, customer attention, and waiting times.

A common application of image processing is super-resolution. This technique typically transforms low-resolution images into high-resolution images. Super-resolution is a major challenge most CV engineers encounter because they often get the model information from low-quality images.

Computer vision machine learning requires a massive amount of data to train a deep learning algorithm that can accurately recognize images. For example, to train a computer to recognize a helmet, it needs to be fed large quantities of helmet images with people wearing helmets in different scenes to learn the characteristics of a helmet.

The CNN helps a machine learning/deep learning model to understand images by breaking them down into pixels that were given labels to train specific features, so-called image annotation. The AI model uses the labels to perform convolutions and make predictions about what it is “seeing” and checks the accuracy of the predictions iteratively until the predictions meet the expectation (start to come true).

Basically, you enable the feature for a small percentage of users and monitor its performance or impact on user experience. As you get insights from feedback and analytics, you iterate, release it to some more users, and so on.

Optical character recognition or optical character reader (OCR) is a technique that converts any kind of written or printed text from an image into a machine-readable format.

The characteristic layered structure of deep neural networks is the foundation of Artificial Neural Networks (ANN). Each layer adds to the knowledge of the previous layer.

✅ Reduced risk and damage control: releasing features only to a small number first means that any negative impact on user experience is limited to the small test group, and it doesn’t cause disruption for the whole user base. Also, there’s limited risk for your product reputation if things go south.

The business value of Viso Suite stems from the fact that it is the only truly end-to-end computer vision infrastructure. When implementing computer vision solutions, businesses typically start with point solutions. These point solutions only take care of a specific step in the application development and management process. This could be model training, data annotation, or security.

The latest trends combine Edge Computing with on-device Machine Learning, a method also called Edge AI. Moving AI processing from the cloud to edge devices makes it possible to run computer vision machine learning everywhere and build scalable applications.

Image classification forms the fundamental building block of Computer Vision. CV engineers often start with training a Neural Network to identify different objects in an image (Object Detection). Training a network to identify the difference between two objects in an image implies building a binary classification model. On the other hand, if it is more than two objects in an image, then it is a multi-classification problem.

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Viso Suite is the leading end to end computer vision infrastructure to build, deploy, and scale AI vision dramatically faster and better.

So, before you dark launch your feature, beta test it first with an even smaller group of users. This could be a few of your most dedicated power users who will be happy to take the feature for a spin and give you actionable feedback.

Hence, computer vision works by recognizing images or “seeing” images similar to humans, using learned features with a confidence score. Therefore, neural networks essentially simulate human decision-making, or neuron activation mechanisms, and deep learning trains the machine to do what the human brain does naturally.

Modern computer vision systems combine image processing with machine learning and deep learning techniques. Hence, developers combine different software (etc., OpenCV or OpenVINO) and AI algorithms to create a multi-step process, a computer vision pipeline.

Beta-testing is often seen as an alternative to dark launching, but there’s no reason why you can’t combine both of these approaches.

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Both dark launch and canary testing allow teams to test how new functionality affects system performance or product metrics with real users, or in a production environment as a software engineer might say.

Before you do so, however, you simply add it to your menu and trigger a tooltip to prompt user engagement. And then you sit back and watch.

Computer vision systems can perform product inspection, infrastructure monitoring, or analyze thousands of products or processes in real-time, for defection detection. Due to its speed, objectivity, continuity, accuracy, and scalability, computer vision systems can quickly surpass human capabilities.

Image preprocessing removes unnecessary information and helps the AI model learn the images’ features effectively. The goal is to improve the image features by eliminating unwanted falsification and achieving better classification performances.

In mission-critical use cases, data offloading with a centralized cloud design is usually not possible because of technical (latency, bandwidth, connectivity, redundancy) or privacy reasons (sensitive data, legality, security), or because it is too expensive (real-time, large-scale, high-resolution, bottlenecks cause cost spikes). Hence, edge computing concepts are used to overcome the limits of the cloud; the cloud is extended to multiple connected edge devices.

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Omitting the need for point solutions, Viso Suite provides a unified computer vision infrastructure. This infrastructure allows businesses to retain complete control over all building, deploying, and maintenance of their computer vision applications.

Use cases of computational vision in agriculture and farming include automated animal monitoring to detect animal welfare, and early detect diseases and anomalies.

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The latest deep learning models achieve above human-level accuracy and performance in real-world image recognition tasks such as facial recognition, object detection, and image classification.

Industrial computer vision is used in manufacturing industries on the production line for automated product inspection, object counting, process automation, and to increase workforce safety with PPE detection and mask detection.

AI vision in Logistics applies deep learning to implement AI-triggered automation and save costs by reducing human errors, predictive maintenance, and accelerating operations throughout the supply chain.

To train an algorithm for computer vision, state-of-the-art technologies leverage deep learning, a subset of machine learning. Many high-performing methods in modern computer vision software are based on a convolutional neural network (CNN).

✅ Real-world testing: thanks to a dark launch, you can validate new features and evaluate their performance with real users.

Computer vision applications are used in a wide range of industries, ranging from security and medical imaging to manufacturing, automotive, agriculture, construction, smart city, transportation, and many more. As AI technology advances and becomes more flexible and scalable, significantly more use cases become possible and economically viable.

While image classification aims to identify the labels of different objects in an image, instance segmentation tries to find the exact boundary of the objects in the image.

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❌ Technical debt: unused feature flags can make it more difficult for the development team to maintain the code and release new software features, especially if you’re using homegrown feature flag systems.

There are two types of Image Segmentation techniques: Instance segmentation and semantic segmentation. Instance segmentation differs from semantic segmentation in the sense that it returns a unique label to every instance of a particular object in the image.