Abstract: An exemplary device for classifying an image includes a receiving unit that receives image data. The device also includes a hardware processor including a neural network architecture to extract a plurality of features from the image data, filter each feature extracted from the image data, concatenate the plurality of filtered features to form an image vector, evaluate the plurality of concatenated features in first and second layers of a plurality of fully connected layers of the neural network architecture based on an amount of deviation in the features determined at each fully connected layer, and generate a data signal based on an output of the plurality of fully connected layers. A transmitting unit sends the data signal to a peripheral or remote device.

Abstract: Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Abstract: In various embodiments, a map inference application automatically maps a user space. A camera is positioned within the user space. In operation, the map inference application determines a path of a first moving object within the user space based on a tracking dataset generated from images captured by the camera. Subsequently, the map inference application infers a walking space within the user space based on the path. The map inference application then generates a model of at least a portion of the user space based on the walking space. One or more movements of a second object within the user space are based on the model. Advantageously, unlike prior art solutions, the map inference application enables a model of a user space to be automatically and efficiently generated based on images from a single stationary camera.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for finding lost objects. In some implementations, a video frame is displayed. An input drawing a shape around an area of the video frame is received. A second video frame is displayed. An indication of the shape in the second video frame is displayed. An input to adjust the shape such that the shape is drawn around a second area is received.

Abstract: Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Abstract: Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Abstract: Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Abstract: Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

Abstract: An attribute recognition system has a person face detection circuitry to detect a suitable person or face for recognition of at least one attribute from persons or faces captured in frame images input from at least one camera to capture a given capture area, an identification information assignment circuitry to identify the persons or faces captured in the frame images having been subjected to the detection by the person face detection circuitry so as to assign an identification information to each identified person or face, and an attribute recognition circuitry to recognize the attribute of a person or face assigned with the identification information, only if the person or face is yet without being subjected to recognition of the attribute, and at the same time if the person or face has been detected by the person face detection circuitry as a suitable person or face for the recognition of the attribute.

Abstract: A gaze estimation method includes receiving, by a processor, input data including a current image and a previous image each including a face of a user, determining, by the processor, a gaze mode indicating a relative movement between the user and a camera that captured the current image and the previous image based on the input data, and estimating, by the processor, a gaze of the user based on the determined gaze mode, wherein the determined gaze mode is one of a plurality of gaze modes comprising a stationary mode and a motion mode.

Abstract: Disclosed herein is a machine learning application for automatically reading filled-in forms. There are multiple steps involved in using a computer to accurately read a handwritten form. First, the system identifies the form. Second, the system identifies what parts of the form are important. Third, the important parts are extracted as image data (known as shreds). Finally, fourth, the system interprets the shreds. This application is focused on steps two and three of that overall process. The disclosed techniques relate to training a machine learning system on a given series of forms such that when provided future filled-in forms within that series, the system is able to extract the portions of the filled-in form that are important/relevant.

Abstract: A device is configured to classify data. Its operation involves providing (210) data samples including one or more of: image data, radar data, acoustic data, and/or lidar data to a processing unit. The data samples include at least one test sample, including positive samples and negative samples. Each positive sample has been determined to contain data relating to at least one object to be detected including at least one pedestrian, car, vehicle, truck or bicycle. Each negative sample has been determined not to contain data relating to the at least one object to be detected. These determinations regarding the positive samples and the negative samples are provided as input data, validated by a human operator, and/or provided by the device itself through a learning algorithm. A first plurality of groups is generated (220) by the processing unit implementing an artificial neural network, wherein at least some of the first plurality of groups are assigned a weighting factor.

Abstract: The present invention provides for a new system and method for diagnosing skin cancer that provides for a more effective analysis of changes in skin tissue due to the duality of acquiring visual data and transforming such visual into an audio signal. The conversion of complicated patterns of visual information of a skin lesion by a computer aided classification analysis into diagnostic sounds results in a much higher resolution rate and increased precision of diagnosis.

Abstract: Techniques are disclosed to facilitate, in autonomous vehicles, the robust detection and classification of objects in a scene using a static sensors in conjunction with event-based sensors. A trained system architecture may be implemented, and the fusion of both sensors thus allows for the consideration of scenes with overexposure, scenes with underexposure, as well as scenes in which there is no movement. In doing so, the autonomous vehicle may detect and classify objects in conditions in which each sensor, if operating separately, would not otherwise be able to classify (or classify with high uncertainty) due to the sensing environment.

Abstract: An electronic apparatus is disclosed.

Abstract: The subject technology provides for receiving a new input stroke. The subject technology determines whether the new input stroke is associated with an existing line group based on a writing direction estimate of the existing line group. The subject technology merges the new input stroke with the existing line group in response to determining that the new input stroke is associated with the existing line group. The subject technology determines a local orientation of the existing line group including the new input stroke based on an estimate of a direction of writing and a scale of each stroke. The subject technology normalizes the existing line group including the new input stroke using the determined location orientation.

Abstract: A method for processing image using fully connected convolutional neural network and a circuit system are provided. The method is operated using fully connected convolutional neural network (CNN) and performed by the circuit system. In the method, an image with a length, a width and an aspect ratio is obtained. A reference image closest to the input image can be obtained by querying a lookup table that records multiple reference images with various sizes to be adapted to the fully connected CNN. The input image can be resized as the closest reference image. A convolution operation is then performed onto the resized image, and a feature cube is formed after multiple operations of convolution. The feature cube is transformed to one-dimensional feature values that are configured to be inputted to a fully connected layer for fully connected operation. An output value of the fully connected CNN is generated.

Abstract: The present invention provides an angle interference resistant and occlusion interference resistant fast face recognition method, comprising: first collecting a training set of images which have been detected and cropped, adjusting and expanding the training set, and conducting standardized pre-processing; inputting the same into a constructed neural network for training, and saving a parametric model; adjusting test data to a suitable size and number of channels, and also conducting standardized pre-processing; inputting the same into a prediction network to obtain feature vectors of face images; and determining whether two faces are from the same person by calculating a distance between the feature vectors of the face images.

Abstract: A method includes receiving one or more sets of wafer data, identifying one or more primitives from one or more shapes in one or more layers in the one or more sets of wafer data, classifying each of the one or more primitives as a particular primitive type, identifying one or more primitive characteristics for each of the one or more primitives, generating a primitive database of the one or more primitives, generating one or more rules based on the primitive database, receiving one or more sets of design data, applying the one or more rules to the one or more sets of design data to identify one or more critical areas, and generating one or more wafer inspection recipes including the one or more critical areas for an inspection sub-system.

Abstract: A method and for child state analysis, a vehicle, an electronic device, and a storage medium are provided. The method includes: performing face feature extraction on at least one image frame in an obtained video stream; classifying whether a person in the image is a child and at least one state of the person according to face features to obtain a first classification result of whether the person in the image is a child, and a second classification result of the at least one state of the person; outputting the first classification result and the second classification result; and/or outputting prompt information according to the first classification result and the second classification result.