Abstract: Provided herein is a headlamp assembly comprising a housing that encloses: a sensor that acquires data associated with a surrounding environment; a light source that illuminates a field of view comprising a portion of the surrounding environment; and one or more processors that analyze the acquired data and determine a direction, field of view, power, or an intensity of the illumination of the portion based on the analyzed data.

Abstract: An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.

Abstract: An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.

Abstract: Provided herein is a headlamp assembly comprising a housing that encloses: a sensor that acquires data associated with a surrounding environment; a light source that illuminates a field of view comprising a portion of the surrounding environment; and one or more processors that analyze the acquired data and determine a direction, field of view, power, or an intensity of the illumination of the portion based on the analyzed data.

Abstract: Provided herein is a headlamp assembly comprising a housing that encloses: a sensor that acquires data associated with a surrounding environment; a light source that illuminates a field of view comprising a portion of the surrounding environment; and one or more processors that analyze the acquired data and determine a direction, field of view, power, or an intensity of the illumination of the portion based on the analyzed data.

Abstract: An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.

Abstract: Provided herein is a headlamp assembly comprising a housing that encloses: a sensor that acquires data associated with a surrounding environment; a light source that illuminates a field of view comprising a portion of the surrounding environment; and one or more processors that analyze the acquired data and determine a direction, field of view, power, or an intensity of the illumination of the portion based on the analyzed data.

Abstract: An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.

Abstract: An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.

Abstract: A method for removing a ghost artifact from a multiple-exposure image of a scene method includes steps of generating and segmenting a difference mask, determining a lower threshold and an upper threshold, generating a refined mask, and generating a corrected image. The difference mask includes a plurality of absolute differences in luminance-values between the multiple-exposure image and a first image of the scene. The segmenting step involves segmenting the difference mask into a plurality of blocks. The lower and upper thresholds are based on statistical properties of the blocks. The method generates the refined mask by mapping each absolute difference to a respective one of a plurality refined values, of the refined mask, equal to a function of the absolute difference, the lower threshold, and the upper threshold. The corrected image is a weighted sum of the first image and the multiple-exposure image, weights being based on the refined mask.

Abstract: A method for removing a ghost artifact from a multiple-exposure image of a scene method includes steps of generating and segmenting a difference mask, determining a lower threshold and an upper threshold, generating a refined mask, and generating a corrected image. The difference mask includes a plurality of absolute differences in luminance-values between the multiple-exposure image and a first image of the scene. The segmenting step involves segmenting the difference mask into a plurality of blocks. The lower and upper thresholds are based on statistical properties of the blocks. The method generates the refined mask by mapping each absolute difference to a respective one of a plurality refined values, of the refined mask, equal to a function of the absolute difference, the lower threshold, and the upper threshold. The corrected image is a weighted sum of the first image and the multiple-exposure image, weights being based on the refined mask.

Abstract: An imaging system includes an image sensor configured to capture a sequence of images including at least one low dynamic range (LDR) image and at least one high dynamic range (HDR) image. The imaging system also includes readout circuitry. The readout circuitry is coupled to read out image data captured by the image sensor. A processor is coupled to the readout circuitry to receive image data corresponding to the at least one LDR image and image data corresponding to the at least one HDR image. The processor is configured to combine high frequency image data extracted from image data corresponding to the at least one LDR image with low frequency image data extracted from image data corresponding to the at least one HDR image to form a composite image.

Abstract: An imaging system includes an image sensor to capture a sequence of images, including a low dynamic range (LDR) image and a high dynamic range (HDR) image, and a processor coupled to the image sensor to receive the LDR image and the HDR image. The processor receives instructions to perform operations to segment the LDR image and HDR image into a plurality of segments. The processor also scans the plurality of LDR and HDR image segments to find a first image segment in the plurality of LDR image segments and a second image segment in the plurality of HDR image segments. The processor then finds interest points in the first and second image segments, and determines an alignment parameter based on matched interest points. The LDR image and the HDR image are combined in accordance with the alignment parameter.

Abstract: An imaging system includes an image sensor configured to capture a sequence of images including at least one low dynamic range (LDR) image and at least one high dynamic range (HDR) image. The imaging system also includes readout circuitry. The readout circuitry is coupled to read out image data captured by the image sensor. A processor is coupled to the readout circuitry to receive image data corresponding to the at least one LDR image and image data corresponding to the at least one HDR image. The processor is configured to combine high frequency image data extracted from image data corresponding to the at least one LDR image with low frequency image data extracted from image data corresponding to the at least one HDR image to form a composite image.