Computer Vision is one of the key concepts presented under the Microsoft AI-900: Microsoft Azure AI fundamentals. It forms an integral part of Microsoft Azure’s artificial intelligence (AI) services, offering capabilities that enable devices to identify and analyze their surroundings visually.
Using computer vision workloads, you can create AI models capable of recognizing and understanding pictures or videos. They can conduct object recognition, form analysis, optical character recognition (OCR), facial detection, and emotion recognition, among other tasks.
Common Computer Vision Workloads
Here, we will closely look at some of the common computer vision workloads that act in tandem with Microsoft Azure’s AI capabilities:
1. Image Classification:
This is a supervised learning task, where the model is trained to identify specific objects or scenarios in an image. With Microsoft Azure’s AI capabilities, you can build sophisticated models to perform this classification.
2. Object Detection:
Object detection takes image classification a step further, not just identifying if an object is present, but also determining its location within an image. The model identifies a bounding box around the object, highlighting its exact location and allows detection of multiple objects within the same image.
3. Optical Character Recognition (OCR):
OCR capabilities allow machines to detect and identify printed or handwritten text within images and convert them into machine-readable data. This workload is especially beneficial for document digitization processes.
4. Facial Detection and Analysis:
Facial detection involves identifying human faces, whereas facial analysis goes a step further by recognizing features such as age, emotion, or identifiable traits. It also identifies and locates landmarks such as eyes, nose, and mouth.
5. Form Recognizer:
Azure Form Recognizer is a cognitive service that uses machine learning to identify and extract key/value pairs and tables from forms. This analysis extends the OCR capabilities to better extract structured data from documents.
Implementing Computer Vision Workloads with Azure
It is worth mentioning that Microsoft Azure provides pre-built models that can be leveraged for different computer vision workloads. Here is how you can implement computer vision workloads using Azure:
- For image classification and object detection, Azure provides the Custom Vision Service, where you can upload labeled images and train a model to perform the desired task.
- For OCR tasks, Azure provides the Computer Vision API that yields information about visual features in an image and extracts printed words.
- The Face API provides several features for facial detection and analysis. Not only can it detect faces and their features in images, but it can also identify similar faces, group faces, and predict age and emotion.
- Azure Form Recognizer service is essentially used for extracting text, key/value pairs, and tables from documents.
Computer vision is significantly shaping the field of AI. This feature of Microsoft Azure AI provides an astounding ability for systems and applications to perceive and interpret their environment, opening a plethora of opportunities for businesses to work on automated tasks and processes more effectively and efficiently.
In conclusion, identifying and understanding the scope of computer vision workloads is key in preparing to take the AI-900 Microsoft Azure AI Fundamentals exam. It’s not just about understanding these concepts theoretically – exploring practical applications of these workloads using Azure AI services will provide a clear outlook on how these processes work.
Note: All the practical implementations discussed throughout the article, revolve around Microsoft Azure and require a strong understanding of Azure services along with some programming knowledge to handle and manage Azure resources effectively.
Practice Test
True or False: Computer vision is a field of artificial intelligence that processes and interprets visual data from the real world to produce numerical or symbolic information.
- Answer: True
Explanation: Computer vision is indeed an AI field that interprets visual data. It works to duplicate the abilities of human vision by electronically perceiving and understanding an image.
In computer vision, object detection is used to identify particular instances of objects in an image.
- Answer: True
Explanation: Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results, identifying the presence and location of objects within images.
True or False: The term “computer vision” only refers to image recognition systems.
- Answer: False
Explanation: Computer vision is not limited to just image recognition. It encompasses a wide range of tasks such as image processing, object detection, pattern recognition, and more.
Which of the following tasks are part of computer vision?
- A. Image classification
- B. Object detection
- C. Optical character recognition
- D. Sensor data analysis
Answer: A, B, C
Explanation: Tasks like image classification, object detection, and optical character recognition are classic use-cases for computer vision, whereas sensor data analysis generally falls under the realm of IoT.
True or False: Analyzing a security footage to detect any unusual activities is a computer vision task.
- Answer: True
Explanation: Analyzing security footage for anomalies is a common application of computer vision. It’s often referred to as motion detection or activity recognition.
Task of recognizing hand-written text is known as:
- A. Image Recognition
- B. Object Detection
- C. Character Segmentation
- D. Optical Character Recognition
Answer: D. Optical Character Recognition
Explanation: Recognizing hand-written text is known as Optical Character Recognition (OCR). It is used to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable data.
True or False: The goal of computer vision is to understand the contents of digital images such as photographs and videos.
- Answer: True
Explanation: Yes, computer vision aims to understand the contents of digital images including photographs and videos, and it may achieve this through various methods such as detecting features or classifying objects.
Facial recognition is a part of which field?
- A. Computer Vision
- B. Natural Language Processing
- C. Machine Learning
- D. None of the above
Answer: A. Computer Vision
Explanation: Facial recognition is a part of Computer Vision. It is a technique of identifying or verifying a person’s identity using their face.
Which of the following is NOT a use-case for computer vision?
- A. Detecting defects in manufactured products
- B. Translating spoken language into written text
- C. Recognizing objects in a photo
- D. Extracting information from a scanned document
Answer: B. Translating spoken language into written text
Explanation: Translating spoken language into written text falls under the domain of Natural Language Processing not Computer Vision.
True or False: Computer vision can be used in healthcare for tasks like detecting diseases in medical images.
- Answer: True
Explanation: Computer vision has substantial applications in healthcare, and detecting diseases in medical images is one of them. By training on large datasets of medical images, computer vision models can learn to recognize patterns correlating with specific diseases.
True or False: Even with advanced AI technologies, computer vision cannot recognize emotions.
- Answer: False
Explanation: Computer vision can be trained to recognize facial expressions and hence detect emotions. This is commonly used in areas such as customer service and sentiment analysis.
What are the major steps in a computer vision project workflow?
- A. Data collection, Model training, Prediction
- B. Data collection, Data manipulation, Data visualization
- C. Model training, Model manipulation, Model visualization
Answer: A. Data collection, Model training, Prediction
Explanation: The typical computer vision project includes steps like: data collection, preprocessing the data, model training, model evaluation, and prediction.
True or False: Training a computer vision model requires labeled datasets.
- Answer: True
Explanation: Labeled datasets act as a ground truth during model training. The labels associated with each image or set of images inform the model about the correct response, helping it learn and optimize its predictions.
What are the ways that computer vision can be used in the automotive industry?
- A. Assisting drivers in parking cars
- B. Autonomous vehicle technology
- C. Monitoring the vehicle’s surroundings for hazards
- D. All of the above
Answer: D. All of the above
Explanation: Computer Vision has several applications in the automotive industry such as helping drivers in parking cars, aiding in the development of autonomous vehicles, and alerting drivers to potential hazards in the vehicle’s surroundings.
Which of the following is NOT a computer vision task?
- A. Scene understanding
- B. Speech recognition
- C. Activity recognition
- D. Image segmentation
Answer: B. Speech recognition
Explanation: Speech recognition is a task in the realm of audio processing and natural language understanding, not computer vision. It uses different algorithms and techniques than those used in computer vision.
Interview Questions
What is Computer Vision in the context of Microsoft Azure AI?
Computer Vision is a service under Microsoft Azure Cognitive Services that uses advanced algorithms to process and analyze images, enabling systems to identify and classify elements within those images.
Can Computer Vision be used to analyze video content?
Yes, Computer Vision can extract insights from video as well as still images by applying similar advanced algorithms to analyze and interpret the visual content.
What are the ways to consume the Computer Vision service in Azure?
There are two ways to consume Computer Vision service in Azure. The first is via a REST API, which supports both synchronous and asynchronous operations. The second is through the SDKs that Microsoft provides in different programming languages like .NET, Java, or Python.
Can the Computer Vision API be used to read text in images?
Yes, the Read API in Azure Computer Vision Service can be used to detect and extract printed or handwritten text in images.
Which Azure service would you use for real-time video analysis?
For real-time video analysis, you would use Azure Video Analyzer.
What are some of the use cases of the Azure Computer Vision API?
Azure Computer Vision API can be used to generate image descriptions, categorize images, recognize printed and handwritten text, detect adult or racy content, identify celebrities and landmarks, and generate thumbnails for images.
What type of Azure AI service is the Computer Vision API?
The Computer Vision API is a type of Cognitive Service in Azure AI.
What is Optical Character Recognition (OCR) in Azure Computer Vision?
OCR is a feature in Azure Computer Vision that allows the extraction of text from images. It can be used to identify printed and handwritten text in several languages.
What models does Azure Computer Vision use for image classification?
Azure Computer Vision uses both pre-trained models and custom models, created by Azure Custom Vision, for image classification.
Can Azure Computer Vision recognize images of objects and classify them?
Yes, Azure Computer Vision can identify different objects and actions in an image and classify them based on pre-trained or custom models.
What is the difference between the read and the OCR operations for text extraction in Azure Computer Vision?
The read operation is suitable for a large amount of text and a range of scenarios, while the OCR operation is best for small amounts of text and single language use.
Can Azure Computer Vision API handle animated GIFs?
No, the Azure Computer Vision API does not directly handle animated GIFs. Each frame of the GIF would need to be extracted and analyzed separately.
What should be the aspect ratio for the images used in Custom Vision training?
The aspect ratio of the images used in Custom Vision training should be 4:3 or 3:4.
What kind of images cannot be analyzed using Azure Computer Vision API?
Azure Computer Vision API is unable to analyse images that are blurred, have a file size of more than 4MB and dimensions smaller than 50 x 50 pixels, or larger than 10000 x 10000 pixels.
Is it possible to train a model on Azure Custom Vision service without any labeled data?
No, it requires labeled data to train a model in Azure Custom Vision service.
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