"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to'big data' enables the'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.
The department disclosed its use of the technology only this month, with Levine and Cholas-Wood detailing their work in the INFORMS Journal on Applied Analytics in an article alerting other departments how they could create similar software. Speaking about it with the news media for the first time, they told The Associated Press recently that theirs is the first police department in the country to use a pattern-recognition tool like this.
In this Oct. 31, 2018 photo, Huang Yongzhen, CEO of Watrix, demonstrates the use of his firm's gait recognition software at his company's offices in Beijing. Chinese authorities have begun deploying a new surveillance tool: "gait recognition" software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras.
Like many parents in the United States, Rob Glaser has been thinking a lot lately about how to keep his kids from getting shot in school. Specifically, he's been thinking of what he can do that doesn't involve getting into a nasty and endless battle over what he calls "the g-word." It's not that Glaser opposes gun control. A steady Democratic donor, Glaser founded the online streaming giant RealNetworks back in the 1990s as a vehicle for broadcasting left-leaning political views. It's just that any conversation about curbing gun rights in America tends to lead more to gridlock and finger-pointing than it does to action.
Machine learning has advanced radically over the past 10 years, and machine learning algorithms now achieve human-level performance or better on a number of tasks, including face recognition,31 optical character recognition,8 object recognition,29 and playing the game Go.26 Yet machine learning algorithms that exceed human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to modify their input data even subtly. Machine learning is already used for many highly important applications and will be used in even more of even greater importance in the near future. Search algorithms, automated financial trading algorithms, data analytics, autonomous vehicles, and malware detection are all critically dependent on the underlying machine learning algorithms that interpret their respective domain inputs to provide intelligent outputs that facilitate the decision-making process of users or automated systems. As machine learning is used in more contexts where malicious adversaries have an incentive to interfere with the operation of a given machine learning system, it is increasingly important to provide protections, or "robustness guarantees," against adversarial manipulation. The modern generation of machine learning services is a result of nearly 50 years of research and development in artificial intelligence--the study of computational algorithms and systems that reason about their environment to make predictions.25 A subfield of artificial intelligence, most modern machine learning, as used in production, can essentially be understood as applied function approximation; when there is some mapping from an input x to an output y that is difficult for a programmer to describe through explicit code, a machine learning algorithm can learn an approximation of the mapping by analyzing a dataset containing several examples of inputs and their corresponding outputs. Google's image-classification system, Inception, has been trained with millions of labeled images.28 It can classify images as cats, dogs, airplanes, boats, or more complex concepts on par or improving on human accuracy. Increases in the size of machine learning models and their accuracy is the result of recent advancements in machine learning algorithms,17 particularly to advance deep learning.7 One focus of the machine learning research community has been on developing models that make accurate predictions, as progress was in part measured by results on benchmark datasets. In this context, accuracy denotes the fraction of test inputs that a model processes correctly--the proportion of images that an object-recognition algorithm recognizes as belonging to the correct class, and the proportion of executables that a malware detector correctly designates as benign or malicious. The estimate of a model's accuracy varies greatly with the choice of the dataset used to compute the estimate.
An introduction to the field of computer vision and image recognition, and how Deep Learning is fueling the fire of this hot topic. Computer Vision is an interdisciplinary field that focuses on how machines or computers can emulate the way in which humans' brains and eyes work together to visually process the world around them. Research on Computer Vision can be traced back to beginning in the 1960s. The 1970's saw the foundations of computer vision algorithms used today being made; like the shift from basic digital image processing to focusing on the understanding of the 3D structure of scenes, edge extraction and line-labelling. Over the years, computer vision has developed many applications; 3D imaging, facial recognition, autonomous driving, drone technology and medical diagnostics to name a few.