"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.
Chest radiography is an important diagnostic tool for chest-related diseases. Medical imaging research is currently embracing the automatic detection techniques used in computer vision. Over the past decade, Deep Learning techniques have shown an enormous breakthrough in the field of medical diagnostics. Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review that would assist practitioners in selecting the best methods from a real-time perspective, perceiving the available datasets, and understanding the currently achieved results in this domain. After summarizing the topic, the review analyzes the usability, goodness factors, and computational complexities of the algorithms that implement these techniques.
This interesting ability of the brain led the Researches to think that hey what if we can give this ability to a machine. With this the task of the machine will get much simplified, once it can recognize the objects in it's surrounding it can interact better with them and that's the whole aim of improving machines, to make them more human friendly, to make them more human-like. Well in that pursuit, there is one big hurdle. How do we make the machine to identify an object? That's what gave rise to the domain of Computer Vision that we call "Object Detection".
Cognitive automation is an extension of existing robotic process automation (RPA) technology. Machine learning enables bots to remember the best ways of completing tasks, while technology like optical character recognition increases the data formats with which bots can interact. Cognitive automation adds a layer of AI to RPA software to enhance the ability of RPA bots to complete tasks that require more knowledge and reasoning. These tasks can range from answering complex customer queries to extracting pertinent information from document scans. Some examples of mature cognitive automation use cases include intelligent document processing and intelligent virtual agents. In contrast, Modi sees intelligent automation as the automation of more rote tasks and processes by combining RPA and AI.
MIT researchers have concluded that the well-known ImageNet data set has "systematic annotation issues" and is misaligned with ground truth or direct observation when used as a benchmark data set. "Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for," the researchers write in a paper titled "From ImageNet to Image Classification: Contextualizing Progress on Benchmarks." "We believe that developing annotation pipelines that better capture the ground truth while remaining scalable is an important avenue for future research." When the Stanford University Vision Lab introduced ImageNet at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2009, it was much larger than many previously existing image data sets. The ImageNet data set contains millions of photos and was assembled over the span of more than two years. ImageNet uses the WordNet hierarchy for data labels and is widely used as a benchmark for object recognition models.
"Without data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway." Every data science task needs data. Specifically, data that's clean and understandable by the system it's being fed into. When it comes to images, a computer needs to see what human eyes see. For example, humans have the ability to identify and classify objects.
Researchers at Nanyang Technological University and University of Technology Sydney have recently developed a machine learning architecture that can recognize human gestures by analyzing images captured by stretchable strain sensors. The new architecture, presented in a paper published in Nature Electronics, is inspired by the functioning of the human brain. "Our idea originates from how the human brain processes information," Xiaodong Chen, one of the researchers who carried out the study, told TechXplore. "In the human brain, high perceptual activities, such as thinking, planning and inspiration, do not only depend on specific sensory information, but are derived from a comprehensive integration of multi-sensory information from diverse sensors. This inspired us to combine visual information and somatosensory information to implement high-precision gesture recognition."
The new model of artificial intelligence (AI) defines gestures with an accuracy of 85%. To create it, scientists have studied how the human brain works. Researchers from Nanyang and Sydney University of Technology have developed a machine learning system that can recognize hand gestures. To do this, she analyzes the images using stretch strain gauges. The architecture of artificial intelligence (AI) is described in the journal Nature Electronics, scientists were inspired by the device of the human brain.
Can artificial intelligence understand human humor? According to Fei-Fei Li, professor in the Computer Science Department at Stanford University and co-director of Stanford's Human-Centered AI Institute, the answer is: not yet."Today's What kind of sentiment does it carry? Humor requires a deep and nuanced reasoning which is not a strength of current AI."A former Google VP and one of the world's expert in the field computer vision, in the talk Li highlighted how many Israeli researchers have impacted her over the course of her career."I It will need to happen in the future," she said.In the lecture, the professor focused on different projects to shape the future of artificial intelligence guaranteeing a more ethical approach, a goal that Zebra, a healthcare company proving AI-based medical image diagnosis, also shares.Together with tremendous opportunities, Li acknowledged how the new technologies developed risk to enhance problems such as a wider gap between generations in interacting with machines, but also job displacement, bias and privacy infringements."For this reason, we believe in a different approach to AI, a human-centered approach," she pointed out, explaining that the goal is to carry out research with a concern for its human impact, with the idea of augmenting people's capabilities rather than replacing them, as well as by drawing inspiration from human intelligence.
I was trying my hand on Optical Character Recognition on newspaper images when I realised that most documents have sections and text is not necessarily across the entire horizontal space of the page. Even though Tesseract was able to recognise the text it was jumbled up. To fix this the model should be able to identify sections on the document and draw a bounding box around it an perform OCR. It was this moment when applying Yolo Object detection on such images came into mind. YOLOv3 is extremely fast and accurate.
Artificial Intelligence (AI) -- and its attendant term, 'Machine Learning' (ML) -- is described as the capability of a computer system to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. Almost all AI/ML examples in commercial as well as military use today rely on data stores that drive deep learning and natural language processing. The defining feature of an AI/ML system is its ability to learn and solve problems. There has been a gradual change in our understanding of what exactly constitutes AI. While advancements in computer hardware and more efficient software have led to the development of AI systems, hitherto computer-resource-intensive tasks, such as optical character recognition (OCR) are now considered a routine technology and, hence, no longer included in any contemporary discussion of AI/ML.