In everyday data-driven life, companies are faced with the challenge of efficiently evaluating their data volumes - and thus gaining important insights for their business. The evaluation of visual elements such as images and videos proves to be particularly difficult. This is where Vision AI comes into play.
We explain how Vision AI works, how you can use it for your business and the challenges you face.
The most Important in a Nutshell
- Practical areas of application for Vision AI tools include medical imaging and document processing.
- The challenges of successfully using AI include data quality, computing power and the quality of the AI.
- Konfuzio features Vision AI, which allows you to automatically recognize and evaluate visual elements in documents.
Definition - What is Vision AI?
Vision Artificial Intelligence - or Vision AI for short - describes the use of artificial intelligence (AI) to analyze and interpret images, videos and other visual elements. In this process, vision AI uses various technology components to analyze and interpret visual data such as images and videos. One of the core components of Vision AI is computer vision. This special field of artificial intelligence focuses on giving machines the ability to process and understand visual information in a similar way to human vision.
In addition, Vision AI uses image recognition and search techniques to identify and retrieve specific objects and patterns within large visual data sets. Machine learning is used to classify elements within an image and recognize them with high accuracy. Image search complements these capabilities by quickly locating similar content in a targeted manner, which enables a wide range of applications in areas such as e-commerce and digital archives.
Excursus - Computer vision vs. vision AI in detail
The two technologies are closely linked, but they are not exactly the same:
Computer Vision
Computer vision refers to the scientific field concerned with the development of methods and algorithms to enable computers to understand visual information. It is a broader term that encompasses various technologies and methods that analyze, interpret and understand visual data. Computer vision can refer to both the scientific and the practical aspect.
Vision AI
Vision AI is a sub-area of computer vision that focuses specifically on the use of artificial intelligence (AI) in the processing of visual information. Vision AI translates the findings of computer vision into applications and software to enable computers to automatically extract patterns and information from images or videos. It includes technologies such as image recognition, object recognition and face recognition to automate visual tasks.
In concrete terms this means
Computer vision is a broader term that covers the general understanding of computers for visual information, while vision AI is more specifically focused on the use of artificial intelligence in this context. Vision AI can therefore be considered a specialized form of computer vision.
So far, Google's Vertex AI Vision development environment has enabled companies to develop their own applications for machine vision using pre-trained APIs and AutoML or to extract data from visual elements.
Applications of Vision AI
Vision AI is generally suitable wherever an AI needs to understand and extract data from visual elements. These are some of the areas of application:
Image segmentation
AI uses complex algorithms to automatically identify and separate the different segments of an image. This enables precise analysis of individual image areas and is used in medical images, surveillance systems and object recognition scenarios.
Object detection
Vision AI solutions identify and localize objects in images or videos. Companies use the technology in surveillance cameras, for example, to detect suspicious human activity (such as carrying a weapon).
Face recognition
In facial recognition, the technology plays a key role by recognizing and extracting complex features from faces. This is one of the most complex areas of application for the technology, as human faces are difficult to read due to their expression, skin color, posture and orientation. Facial recognition is used, for example, in security systems, access control and social media.
Edge detection
Vision AI is the main actor for edge detection by identifying the transitions in the form of breaks in brightness between different objects or structures in an image. This is fundamental for image processing and data extraction.
Pattern recognition
In pattern recognition, AI identifies arrangements of features or data. As the name suggests, it recognizes patterns. This is useful in image processing to detect and interpret structures in images.
Visual search
The technology enables a visual search. This means that users no longer have to search for a product or information online using text, but can simply upload an image for the search. Vision AI tools then analyze the visual features such as shapes, patterns and colors and compare them with a large database of images. In this way, they identify similarities and show users relevant matches.
The AI recognizes characteristics of images and shows users similar images in the search function of a search engine.
Optical Character Recognition
Vision AI plays a key role in the Optical Character Recognition (OCR, Optical Character Recognition) by converting a text file into Image to text and extracts information from it downstream, e.g. using rules or AI. It identifies shapes and patterns of letters and characters in an image and converts them into machine-readable text. This technology not only supports the digitization of documents and automated data searches, but also expands the possibilities when dealing with technical drawings, for example. Not only texts, but also specific symbols and structures are recognizedThis enables technical departments to use detailed information efficiently for planning and analysis.
Image classification
Vision AI software automatically classifies images into categories or assigns labels to them. To do this, Computer Vision AI uses a classification system with a database containing predefined patterns. It compares the patterns with the image elements and determines what they are. This process is important in various applications such as biomedical imaging, biometrics and video surveillance.
Document classification
Vision AI recognizes image and text content in documents and can assign the documents accordingly. This means that the technology is able to automatically sort and file documents according to predefined categories.
In practice, companies are therefore using the technology in areas such as medical imaging, monitoring and detecting safety-relevant events and autonomous vehicles.
Computer Vision AI Examples
From industry to the financial sector and insurance: Vision AI is already in use in numerous industries today. Here are 3 classic computer vision AI examples:
Vision AI for autonomous vehicles
AI technology plays a crucial role in vehicles with automated driving functions. Autonomous vehicles have multiple cameras and sensors to capture the environment in real time. Vision AI algorithms continuously analyze this data and identify traffic signs, pedestrians, other vehicles and obstacles on the road. By analyzing the visual information in real time, the vehicle is able to make precise decisions, such as adjusting speed, changing lanes or braking to avoid collisions.
Vision AI for medical imaging
In medical imaging, healthcare facilities are using AI to analyze X-rays, MRI scans and other medical images. Vision AI algorithms are designed to recognize complex patterns and anomalies in the images to assist doctors in diagnosis and treatment planning. For example, the AI identifies tumors at an early stage, detects structural abnormalities or automatically highlights certain areas for more detailed analysis.
Vision AI in document processing
In document processing, the technology supports the automated extraction of information from various types of documents in particular. Companies are faced with the challenge of processing large volumes of paper documents or digital files, such as invoices, contracts and forms.
Vision AI uses technologies such as OCR and OMR to automatically capture text and markings in images, graphics, forms and tables, for example.
The AI is thus able to extract key information such as names, addresses, invoice amounts or product codes. By automating this process, companies reduce processing time and minimize human error.
The Challenges of AI
The areas of application for Vision Artificial Intelligence show the great potential the technology has for companies. In order to achieve the best possible results, companies are faced with these challenges:
Data quality and diversity
The quality and diversity of training data are critical to the performance of Vision AI programs. If the data sets are not representative or certain groups are underrepresented, pre-trained models can make inaccurate predictions. This also limits the applicability of AI in different contexts.
Computing power
Processing large amounts of visual data requires significant computing power. Advanced custom models, such as neural networks, require powerful hardware, e.g. GPU graphics cards, and efficient algorithms to perform complex analysis in an acceptable amount of time. This is a challenge - especially for companies or applications with limited resources.
Complexity of the visual data
In contrast to structured data, visual data is highly unstructured and complex. This complexity makes analysis difficult and requires advanced and adaptive AI systems to recognize and extract relevant patterns and features.
Understanding context
Human abilities to understand context and recognize abstract concepts in images are a challenge for vision AI software. The interpretation of images requires not only the identification of objects, but also the understanding of context and abstract concepts, which is still a complex task for artificial intelligence.
Management of AI-related risks
There are various risks associated with the use of AI, including security vulnerabilities, misinterpretation and potentially unintended consequences. Managing these risks requires a comprehensive assessment, implementation of security measures and regular monitoring of the systems.
Adapt Vision AI applications to an application
AI technology uses advanced machine learning techniques such as deep neural networks to extract meaningful information from image and video content. The technology usually proceeds in these 7 steps:
1. Data collection
First, the AI collects large amounts of visual data. This can be images or videos that represent the variety of scenarios that the system will later recognize.
2. Data cleansing
The technology then cleanses the collected data. In this way, it ensures that the model is trained on high-quality and representative data. This can include the adjustment of image sizes, the removal of interference or the normalization of colors.
3. Feature extraction
The AI model uses advanced algorithms to automatically extract relevant features from the visual data. These features can include various aspects, such as edges, shapes, textures or color patterns. Feature extraction is crucial to identify the important information that is relevant for the subsequent analysis and recognition of objects or patterns.
4. Training of the model
The extracted knowledge is taught to the model. Companies train the model with labeled data so that it learns to recognize patterns and correlations between the visual features and the corresponding labels.
5. Optimization
After training, companies optimize the model to improve its performance. This can be done by adjusting hyperparameters or using special optimization algorithms.
6. Inference (application)
After training, companies are able to apply the model to new, unseen data. This step is called inference. The model analyzes visual information and makes predictions, recommendations or classifications based on its training.
7. Feedback and improvement
Feedback and errors allow companies to identify where the model needs more data to improve its performance and accuracy.
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