Abstract: A geometric approach may be used to detect objects on a road surface. A set of points within a region of interest between a first frame and a second frame are captured and tracked to determine a difference in location between the set of points in two frames. The first frame may be aligned with the second frame and the first pixel values of the first frame may be compared with the second pixel values of the second frame to generate a disparity image including third pixels. One or more subsets of the third pixels that have a value above a first threshold may be combined, and the third pixels may be scored and associated with disparity values for each pixel of the one or more subsets of the third pixels. A bounding shape may be generated based on the scoring.

Abstract: A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

Abstract: Systems, methods and computer readable media provide technology to perform image classification and produce visualization using a machine learning architecture. The disclosed image classification and visualization technology includes a feature extraction network to generate a feature map, a feature importance network to generate a feature importance vector, an attention map generated based on a weighted sum of the feature importance vector and the feature map, a classification output determined based on a combination of the attention map and the feature map, and a feature visualization image generated by overlaying the attention map onto an input image. Each of the feature extraction network and the feature importance network can include a neural network.

Abstract: A method, apparatus, system, and computer program product for training a gesture recognition machine learning model system. Temporal images for a set of gestures used for ground operations for an aircraft are identified by a computer system. Pixel variation data identifying movement on a per image basis from the temporal images is generated by the computer system. The temporal images and the pixel variation data form training data. A set of feature machine learning models is trained by the computer system to recognize features using the training data.

Abstract: The present invention relates to a UV curable clad resin composition for optical fiber or waveguide comprising: (A) a photopolymerizable acrylate oligomer derived from the mixture composed of a polyol copolymer, an acid anhydride, an acrylate alcohol, a condensation polymerization catalyst and a polymerization inhibitor, (B) a photopolymerizable monomer, (C) a photopolymerization initiator, and (D) a leveling or antifoaming agent. The inventive composition minimizes optical loss and has a controllable low refractive index in the range of 1.35 to 1.46.

Abstract: The present invention relates to a UV curable clad resin composition for optical fiber or waveguide comprising: (A) a photopolymerizable acrylate oligomer derived from the mixture composed of a polyol copolymer, an acid anhydride, an acrylate alcohol, a condensation polymerization catalyst and a polymerization inhibitor, HB) a photopolymerizable monomer, (C) a photopolymerization initiator, and (D) a leveling or antifoaming agent. The inventive composition minimizes optical loss and has a controllable low refractive index in the range of 1.35 to 1.46.