"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
Recognition of the face as an identity is a critical aspect in today's world. Facial identification and recognition find its use in many real-life contexts, whether your identity card, passport, or any other credential of significant importance. It has become quite a popular tool these days to authenticate the identity of an individual. This technology is also being used in various sectors and industries to prevent ID fraud and identity theft. Your smartphone also has a face recognition feature to unlock it.
Quanergy Systems, the Sunnyvale, California-based lidar company, said Tuesday it has agreed to merge with special purpose acquisition fund CITIC Capital Acquisition Corp., a Chinese blank-check firm affiliated with the country's largest state-owned investment conglomerate. The deal, which puts an implied valuation on Quanergy at $1.4 billion, is expected to close in the second half of 2021. After closing, the transaction will inject the lidar company with around $278 million in pro forma net cash, including $40 million in private investment in public equity (PIPE) funding. Lidar is an essential component of most autonomous driving systems -- the notable exception being Tesla's stack, which is attempting to develop a pure vision-based system to support its pursuit of automated driving (Tesla vehicles are not autonomous today and have what is considered a Level 2 advanced driver assistance system). Quanergy is a developer of solid state silicon lidar units, which pulses a low-power laser through an optical phased array to measure the distance and shape of objects.
So many of us have used different Facebook applications to see us aging, turned into rock stars, or applied festive make-up. Such waves of facial transformations are usually accompanied by warnings not to share images of your faces -- otherwise, they will be processed and misused. But how does AI use faces in reality? Let's discuss state-of-the-art applications for face detection and recognition. First, detection and recognition are different tasks. Face detection is the crucial part of face recognition determining the number of faces on the picture or video without remembering or storing details.
BRUSSELS: Europe's two privacy watchdogs teamed up on Monday (Jun 21) to call for a ban on the use of facial recognition in public spaces, going against draft European Union rules which would allow the technology to be used for public security reasons. The European Commission in April proposed rules on artificial intelligence, including a ban on most surveillance, in a bid to set global standards for a key technology dominated by China and the United States. The proposal does allow high-risk AI applications to be used in areas such as migration and law enforcement, though it laid out strict safeguards, with the threat of fines of as much as 6per cent of a company's global turnover for breaches. The proposal needs to be negotiated with EU countries and the bloc's lawmakers before it becomes law. The two privacy agencies, the European Data Protection Board (EDPB) and European Data Protection Supervisor (EDPS), warned of the extremely high risks posed by remote biometric identification of individuals in public areas.
Training ML Models and exporting it in more optimized way for Edge device from scratch is quite challenging thing to do especially for a beginner in ML space. Interestingly Azure Cognitive Services will aid in heavy lifting half of the common problems such as Image Classification, Speech Recognition etc. So in this article, I will show you how I created a simple pipeline(kind of MLOps) that deploys the model to an Edge Device leveraging Azure IoT Modules and Azure DevOps Services. Blob Storage – For storing images for ML training 2. Logic Apps – To respond Blob storage upload events and trigger a Post REST API call to Azure Pipelines 3. Cognitive Services – For training Images and generate a optimized model specifically for edge devices. Containerized Az Devops Agents will be running inside this, orchestrated using K3s Kubernetes Distribution.
GasHis-Transformer is a model for realizing gastric histopathological image classification (GHIC), which automatically classifies microscopic images of the stomach into normal and abnormal cases in gastric cancer diagnosis, as shown in the figure. GasHis-Transformer is a multi-scale image classification model that combines the best features of Vision Transformer (ViT) and CNN, where ViT is good for global information and CNN is good for local information. GasHis-Transformer consists of two important modules, Global Information Module ( GIM) and Local Information Module ( LIM), as shown in the figure below. GasHisTransformer has high classification performance on the test data of gastric histopathology dataset, with estimate precision, recall, F1-score, and accuracy of 98.0%, 100.0%, 96.0%, and 98.0%, respectively. GasHisTransformer consists of two modules: Global Information Module (GIM) and Local Information Module (LIM).
Adrian Rosebrock, a known CV researcher, states in his "Gentle guide to deep learning object detection" that: "object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification and seeks to localize precisely an area where an object appears". One approach to build a custom object detector, as he suggests, is to choose any classifier and precede it with an algorithm to select and provide regions of an image that may contain an object. Within this method, you are free to decide whether to use a traditional ML algorithm for image classification (utilising or not CNN as a feature extractor) or train a simple neural network to handle arbitrary large datasets. Despite its proven efficiency, this two-stage object detection paradigm, known as R-CNN, still relies on heavy computations and is not suitable for real-time application. It is further said in the abovementioned post that "another approach is to treat a pre-trained classification network as a base (backbone) network in a multi-component deep learning object detection framework (such as Faster R-CNN, SSD, or YOLO)".
This article is part of "Deconstructing artificial intelligence," a series of posts that explore the details of how AI applications work (In partnership with Paperspace). Deep neural networks have gained fame for their capability to process visual information. And in the past few years, they have become a key component of many computer vision applications. Among the key problems neural networks can solve is detecting and localizing objects in images. Object detection is used in many different domains, including autonomous driving, video surveillance, and healthcare.