A system for selectively amplifying audio signals may include a microphone configured to capture sounds from an environment of a user. The system may also include a processor programmed to: receive audio signals representative of the sounds captured by the microphone; cause selective conditioning of at least one audio signal received by the microphone from a region associated with the recognized individual; and cause transmission of the at least one conditioned audio signal to a hearing interface device configured to provide sound to an ear of the user.

The present disclosure provides a material identification method and a device based on laser speckle and modal fusion, an electronic device and a non-transitory computer readable storage medium. The method includes: performing data acquisition on an object by using a structured light camera to obtain a color modal image, a depth modal image and an infrared modal image; preprocessing the color modal image, the depth modal image and the infrared modal image; and inputting the color modal image, the depth modal image and the infrared modal image preprocessed into a preset depth neural network for training, to learn a material characteristic from a speckle structure and a coupling relation between color modal and depth modal, to generate a material classification model for classifying materials, and to generate a material prediction result in testing by the material classification model of the object.

The present teaching relates to a method, system, medium, and implementation of processing image data in an autonomous driving vehicle. Sensor data acquired by one or more types of sensors deployed on the vehicle are continuously received. The sensor data provide different information about surrounding of the vehicle. Based on a first data set acquired by a first sensor of a first type of the one or more types of sensors at a specific time, an object is detected, where the first data set provides a first type of information about the surrounding of the vehicle. Depth information of the object is then estimated via object centric stereo at object level based on the object detected as well as a second data set acquired by a second sensor of the first type of the one or more types of sensors at the specific time. The second data set provides the first type of information about the surrounding of the vehicle with a different perspective as compared with the first data set.

A system may include a wearable camera configured to capture images and a microphone configured to capture sounds. The system may also include a processor programmed to receive the images; identify a representation of one or more individuals in the images; receive from the microphone a first audio signal associated with a voice; determine, based on analysis of the images, that the first audio signal is not associated with a voice of any of the one or more individuals; receive from the microphone a second audio signal associated with a voice; determine, based on analysis of the images, that the second audio signal is associated with a voice of one of the one or more individuals; and cause a first amplification of the first audio signal and a second amplification of the second audio signal. The first amplification may differ from the second amplification in one aspect.

A geographic object recognition unit (120) recognizes, using image data (192) obtained by photographing in a measurement region where a geographic object exists, a type of the geographic object from an image that the image data (192) represents. A position specification unit (130) specifies, using three-dimensional point cloud data (191) indicating a three-dimensional coordinate value of each of a plurality of points in the measurement region, a position of the geographic object.

A system is comprised of one or more processors coupled to memory. The one or more processors are configured to receive image data based on an image captured using a camera of a vehicle and to utilize the image data as a basis of an input to a trained machine learning model to at least in part identify a distance of an object from the vehicle. The trained machine learning model has been trained using a training image and a correlated output of an emitting distance sensor.

Provided is a system and method for fusion recognition using an active stick filter. The system for fusion recognition using the active stick filter includes a data input unit configured to receive input information for calibration between an image and a heterogeneous sensor, a matrix calculation unit configured to calculate a correlation for projection of information of the heterogeneous sensor, a projection unit configured to project the information of the heterogeneous sensor onto an image domain using the correlation, and a two-dimensional (2D) heterogeneous sensor fusion unit configured to perform stick calibration modeling and design and apply a stick calibration filter.

A method for automatically panning a view for a commercial vehicle includes determining a plurality of estimated trailer angles. Each estimated trailer angle is determined using a distinct estimation method, and method assigns a confidence value to each estimated trailer angle in the plurality of estimated trailer angles. The method determines a weighted sum of the plurality of estimate trailer angles, and automatically pans the view based at least in part on the weighted sum and a current vehicle operation.

Embodiments provide a method for obstacle avoidance in a mobile robot. Images of objects are captured with mobile robot image sensors with a floor level perspective. Data corresponding to the images is used to train a neural network. The identified objects are classified by indicating whether the objects are a potential hazard. In one embodiment, an image sensor on a robot provides intensity and depth data for each of a plurality of pixels of the images. The intensity and depth data are fused to produce fused data. The fused data is then used to train the neural network.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.