Abstract: In an approach to integrating real-world properties into machine learning training, a real-world image is received. The real-world image is compared to a simulated image, where the comparison is performed using a discriminator network of a generative adversarial network (GAN). A generator network of the GAN is trained with results of the comparison of the real-world image to the simulated image. Responsive to determining that the real-world image is not optimal, the real-world image is iteratively tuned, using the generator network of the GAN, until it is determined that the real-world image is optimal, where the real-world image is optimal if the real-world image meets a predetermined threshold for accuracy of one or more image parameters of the simulated image versus the real-world image. The discriminator network of the GAN is trained with the real-world image.

Abstract: A method of using a learning machine to provide a biomechanical data representation of a subject based on markerless video motion capture. The learning machine is trained with both markerless video and marker-based (or other worn body sensor) data, with the marker-based or body worn sensor data being used to generate a full biomechanical model, which is the “ground truth” data. This ground truth data is combined with the markerless video data to generate a training dataset.

Abstract: A method and system is provided for estimation of sensor data confidence based on statistical analysis of different classifier and feature-set (CF) configurations. A method may include: training a classifier of a CF configuration based on a training set of nominal sensor data values; executing the classifier on the training set to generate a first set of confidence values; collecting statistics on the confidence values; calculating a confidence decision threshold based on the collected statistics; executing the classifier on an evaluation set of nominal and degraded sensor data values, to generate a second set of confidence values; deciding whether the sensor data values of the evaluation set are nominal or degraded based on a comparison of the second set of confidence values to the confidence decision threshold; and calculating a score to evaluate the trained classifier based on a verification of the decisions.

Abstract: A surrounding environment recognition device includes an image capturing unit that captures a peripheral image, and a traffic signal detecting unit, which sets a search region with respect to the peripheral image, and detects a traffic signal inside of the search region. The search region becomes smaller as the distance from the image capturing unit is farther away.

Abstract: A surrounding environment recognition device includes an image capturing unit that captures a peripheral image, and a traffic signal detecting unit, which sets a search region with respect to the peripheral image, and detects a traffic signal inside of the search region. The search region becomes smaller as the distance from the image capturing unit is farther away.

Abstract: A surrounding environment recognition device includes an image capturing unit that captures a peripheral image, and a traffic signal recognizing unit that recognizes a traffic signal from within the peripheral image. The image capturing unit captures a plurality of images of frames. The traffic signal recognizing unit recognizes the traffic signal based on a combination of the plurality of images of frames.

Abstract: A method and system is provided for estimation of sensor data confidence based on statistical analysis of different classifier and feature-set (CF) configurations. A method may include: training a classifier of a CF configuration based on a training set of nominal sensor data values; executing the classifier on the training set to generate a first set of confidence values; collecting statistics on the confidence values; calculating a confidence decision threshold based on the collected statistics; executing the classifier on an evaluation set of nominal and degraded sensor data values, to generate a second set of confidence values; deciding whether the sensor data values of the evaluation set are nominal or degraded based on a comparison of the second set of confidence values to the confidence decision threshold; and calculating a score to evaluate the trained classifier based on a verification of the decisions.

Abstract: A method and system for analysis of a viscoelastic response in a deformable material. The system includes a light source configured to provide linearly polarized light and a polariscope configured to receive said linearly polarized light and to generate an image associated with a viscoelastic response of said deformable material. The system also includes a machine vision system configured to operate on the image to locate the response on the deformable material and to classify the response as one of a plurality of predefined types of responses. A display may then be provide that is configured to provide feedback of the location of the viscoelastic response and classification of the response to a user of said system.

Abstract: A method and system for analysis of a viscoelastic response in a deformable material. The system includes a light source configured to provide linearly polarized light and a polariscope configured to receive said linearly polarized light and to generate an image associated with a viscoelastic response of said deformable material. The system also includes a machine vision system configured to operate on the image to locate the response on the deformable material and to classify the response as one of a plurality of predefined types of responses. A display may then be provide that is configured to provide feedback of the location of the viscoelastic response and classification of the response to a user of said system.