Intel Corporation

Samsung Electronics Co., Ltd.

Mobileye Vision Technologies Ltd.

Lenovo (beijing) Limited

Beijing Lenovo Software Ltd.

Ping Guo

Keun Woo Park

Yimin Zhang

Qiang Wang

Lidan Zhang

Haibing Ren

Wei Hu

Kyoobin Lee

Xiangbin Wu

Xinxin Zhang

Qianying Zhu

Fei Li

Shandong Wang

Eric Hyunsurk Ryu

Baek Hwan Cho

Dongqing Zou

Wentao Mao

Guangqi Shao

Xiaotao Wang

Qing Fa Wang

Fei Fei Li

Gang Yan

Weiyu Yang

Qi She

Renjing Pei

System and techniques for vehicle operation safety model (VOSM) grade measurement are described herein. A data set of parameter measurements—defined by the VOSM—of multiple vehicles are obtained. A statistical value is then derived from a portion of the parameter measurements. A measurement from a subject vehicle is obtained that corresponds to the portion of the parameter measurements from which the statistical value was derived. The measurement is then compared to the statistical value to produce a safety grade for the subject vehicle.

Vehicle operation safety model grade measurement

Systems, methods, apparatuses, and computer program products to provide stochastic trajectory prediction using social graph networks. An operation may comprise determining a first feature vector describing destination features of a first person depicted in an image, generating a directed graph for the image based on all people depicted in the image, determining, for the first person, a second feature vector based on the directed graph and the destination features, sampling a value of a latent variable from a learned prior distribution, the latent variable to correspond to a first time interval, and generating, based on the sampled value and the feature vectors by a hierarchical long short-term memory (LSTM) executing on a processor, an output vector comprising a direction of movement and a speed of the direction of movement of the first person at a second time interval, subsequent to the first time interval.

Trajectory prediction using directed graph and destination features

System and techniques for vehicle operation safety model (VOSM) compliance measurement are described herein. A subject vehicle is tested in a vehicle following scenario against VOSM parameter compliance. The test measures the subject vehicle activity during phases of the following scenario in which a lead vehicle slows and produces log data and calculations that form the basis of a VOSM compliance measurement.

Vehicle operation safety model compliance measurement

A long-term object tracker employs a continuous learning framework to overcome drift in the tracking position of a tracked object. The continuous learning framework consists of a continuous learning module that accumulates samples of the tracked object to improve the accuracy of object tracking over extended periods of time. The continuous learning module can include a sample pre-processor to refine a location of a candidate object found during object tracking, and a cropper to crop a portion of a frame containing a tracked object as a sample and to insert the sample into a continuous learning database to support future tracking.

Continuous learning for object tracking

An example apparatus for generating synthesized images includes a receiver to receive a frame, a mask and external images. The apparatus also includes a foreground augmenter to generate augmented foregrounds in the frame based on the mask. The apparatus includes a background augmenter to generate augmented backgrounds based on the frame, the mask, and the external images. The apparatus also further includes an image synthesizer to generate a synthesized image based on the generated augmented foregrounds and the augmented backgrounds.

Training one-shot instance segmenters using synthesized images

A system, article, and method of neural network object recognition for image processing includes customizing a training database and adapting an instance segmentation neural network used to perform the customization.

Method and system of neural network object recognition for image processing

A system, article, and method of point cloud registration using overlap regions for image processing.

Method and system of point cloud registration for image processing

Methods and apparatus to match images using semantic features are disclosed. An example apparatus includes a semantic labeler to determine a semantic label for each of a first set of points of a first image and each of a second set of points of a second image; a binary robust independent element features (BRIEF) determiner to determine semantic BRIEF descriptors for a first subset of the first set of points and a second subset of the second set of points based on the semantic labels; and a point matcher to match first points of the first subset of points to second points of the second subset of points based on the semantic BRIEF descriptors.

Methods and apparatus to match images using semantic features

A method and apparatus for processing a binocular disparity image are provided. A method of determining a disparity of a binocular disparity image that includes a left eye image and a right eye image includes acquiring features of a plurality of pixels of the binocular disparity image based on an event distribution of the binocular disparity image, calculating a cost matrix of matching respective pixels between the left eye image and the right eye image based on the features, and determining a disparity of each matched pair of pixels based on the cost matrix.

Method and apparatus for processing binocular disparity image

A method and apparatus for acquiring an image disparity are provided. The method may include acquiring, from dynamic vision sensors, a first image having a first view of an object and a second image having a second view of the object; calculating a cost within a preset disparity range of an event of first image and a corresponding event of the second image; calculating an intermediate disparity of the event of the first image and an intermediate disparity of the event of the second image based on the cost; determining whether the event of the first image is a matched event based on the intermediate disparity of the event of the first image and the intermediate disparity of the event of the second image; and predicting optimal disparities of all events of the first image based on an intermediate disparity of the matched event of the first imaged.

Method and apparatus for acquiring image disparity

An object recognition apparatus and an object recognition method are provided. The object recognition method includes generating an input image based on an event flow of an object, generating a composite feature based on features extracted by a plurality of recognizers, and recognizing the object based on the composite feature.

Object recognition method and apparatus

An image deblurring method and an image deblurring apparatus are provided. The image deblurring method includes acquiring a blurred image and a dynamic vision sensor (DVS) event set that is recorded by a DVS while the blurred image is exposed, and deblurring the blurred image, based on the DVS event set.

Image deblurring method and apparatus

A method and apparatus for detecting an object using an event-based sensor is provided. An object detection method includes determining a feature vector based on target pixels and neighbor pixels included in an event image, and determining a target object corresponding to the target pixels based on the feature vector.

Method and apparatus of detecting object using event-based sensor

Embodiments of the present disclosure provide an information processing method and an electronic device. The method comprises: modulating a first audio signal associated with a first channel in a first sound source file onto a first ultrasound signal to obtain a first modulated ultrasound signal; modulating a second audio signal associated with a second channel in a second sound source file onto a second ultrasound signal to obtain a second modulated ultrasound signal; transmitting a first ultrasound wave associated with the first modulated ultrasound signal, such that the first ultrasound wave, once reflected by a first reflection plane, produces a first audible sound signal corresponding to the first audio signal; and transmitting a second ultrasound wave associated with the second modulated ultrasound signal, such that the second ultrasound wave, once reflected by a second reflection plane, produces a second audible sound signal corresponding to the second audio signal.

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Beijing, China

Ping Guo