"... 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
Artificial intelligence systems are being employed for a wide range of tasks from recognition systems to autonomous activities, from pattern and anomaly detection to predictive analytics and conversational systems, and many other aspects . One of the areas where AI has shown particular capability is in the area of recognition, from image recognition to speech and other aspects of pattern recognition. Source: Can AI Detect Your Emotion Just By How You Walk?
In my previous post I talked about using a portable EEG device to detect Event Related Potentials (ERP's) in the brain. Specifically, I was able to detect a Reward Positivity (RewP) signal after a puzzle was solved correctly. I did this by graphing the signal immediately after the event and comparing it with the average RewP signal from this paper. Using my human brain's visual pattern recognition, I confirmed that I was getting the same pattern. Wouldn't it be interesting to train a machine learning model to recognize the same pattern so we can monitor these events automatically.
Course Overview This is a class for computer-literate people with no programming background who wish to learn basic Python programming. The course is aimed at those who want to learn data wrangling - manipulating downloaded files to make them amenable to analysis. We concentrate on language basics such as list & string manipulation, control structures, simple data analysis packages, & introduce modules for downloading data from the web. Instructors Tony Schultz Tony Schultz Tony received his Ph.D. in Physics from the City University of New York & has taught at Sarah Lawrence College over the past decade. Tony specializes in developing machine learning & pattern recognition algorithms for processing motion capture data.
Z Advanced Computing, Inc. (ZAC), the pioneer startup on Explainable-AI (Artificial Intelligence) (XAI), is developing its Smart Home product line through a paid-pilot for Smart Appliances for BSH Home Appliances (a subsidiary of the Bosch Group, originally a joint venture between Bosch and Siemens), the largest manufacturer of home appliances in Europe and one of the largest in the world. ZAC just successfully finished its Phase 1 of the pilot program. "Our cognitive-based algorithm is more robust, resilient, consistent, and reproducible, with a higher accuracy, than Convolutional Neural Nets or GANs, which others are using now. It also requires much smaller number of training samples, compared to CNNs, which is a huge advantage," said Dr. Saied Tadayon, CTO of ZAC. "We did the entire work on a regular laptop, for both training and recognition, without any dedicated GPU. So, our computing requirement is much smaller than a typical Neural Net, which requires a dedicated GPU," continued Dr. Bijan Tadayon, CEO of ZAC.
Face and Image Recognition is not only about security and surveillance or controlling the quality of industrial production processes. The technology is proving increasingly impactful to the fashion and beauty industries, generating multiple exciting opportunities for manufacturers and consumers alike. Face and Image recognition being an AI frontrunner in terms of security, agriculture, and industrial QA, the technology's business uses beyond these three realms are still much less known. As a result, many businesses in industries other than security and surveillance, agriculture, and industrial production have barely given any thought to employing Image Recognition as a means of attaining better capabilities to raise their sights and achieve higher levels of quality and profitability. Meanwhile, the Image Recognition- inspired and - enabled opportunities, which have been cropping up of late elsewhere, can barely be ignored and should be taken note of by a much, much wider audience.
When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The model thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training without any annotations for parts of images.
Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain because the transformation of interest is not known in advance. As a consequence, not all basis functions will necessarily contribute to the final transformation which results in a non-compact representation of the transformation. For each element in the sequence, a local deformation defined by its position, shape, and weight is computed by our recurrent registration neural network.
This paper concerns the undetermined problem of estimating geometric transformation between image pairs. Recent methods introduce deep neural networks to predict the controlling parameters of hand-crafted geometric transformation models (e.g. However, the low-dimension parametric models are incapable of estimating a highly complex geometric transform with limited flexibility to model the actual geometric deformation from image pairs. To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment. Arbicon-Net is generalized from training data to predict the desired arbitrary continuous geometric transformation in a data-driven manner for unseen new pair of images.
Heterogeneous Face Recognition (HFR) is a challenging issue because of the large domain discrepancy and a lack of heterogeneous data. This paper considers HFR as a dual generation problem, and proposes a novel Dual Variational Generation (DVG) framework. It generates large-scale new paired heterogeneous images with the same identity from noise, for the sake of reducing the domain gap of HFR. Specifically, we first introduce a dual variational autoencoder to represent a joint distribution of paired heterogeneous images. Then, in order to ensure the identity consistency of the generated paired heterogeneous images, we impose a distribution alignment in the latent space and a pairwise identity preserving in the image space.
Researchers at TU Wien (Vienna) have developed an ultra-fast image sensor with a built-in neural network; the sensor can be trained to recognize certain objects. They describe their work on ultrafast machine vision in a paper in Nature. Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN). The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption.