Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Generating a location-specific three-dimensional model in response to a location query can provide users with a better understanding of a location through providing better interactivity, better perspective, and better understanding of dimensionality. Generation of the models can be enabled by leveraging a three-dimensional asset database and segmentation methods. The location-specific models can provide further utility by further including situation specific simulated effects, such as simulated weather or traffic.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.

Abstract: Generating a location-specific three-dimensional model in response to a location query can provide users with a better understanding of a location through providing better interactivity, better perspective, and better understanding of dimensionality. Generation of the models can be enabled by leveraging a three-dimensional asset database and segmentation methods. The location-specific models can provide further utility by further including situation specific simulated effects, such as simulated weather or traffic.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.

Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Described examples relate to an apparatus comprising a first sensor configured to scan an area of interest during a first time period and a second sensor configured to capture a plurality of images of a field of view. The apparatus may include at least one controller configured to receive the plurality of images captured by the second sensor, compare the timestamp information associated with at least one image of the plurality of images to at least one time period of the first time period, and select a base image from the plurality of images based on the comparison.

Abstract: Described examples relate to an apparatus comprising a first sensor configured to scan an area of interest during a first time period and a second sensor configured to capture a plurality of images of a field of view. The apparatus may include at least one controller configured to receive the plurality of images captured by the second sensor, compare the timestamp information associated with at least one image of the plurality of images to at least one time period of the first time period, and select a base image from the plurality of images based on the comparison.

Abstract: The present disclosure describes systems and techniques directed to optical image stabilization movement to create a super-resolution image of a scene. The systems and techniques include a user device (102) introducing (502), through an optical image stabilization system (114), movement to one or more components of a camera system (112) of the user device (102). The user device (102) then captures (504) respective and multiple frames (306) of an image of a scene, where the respective and multiple frames (306) of the image of the scene have respective, sub-pixel offsets of the image of the scene across the multiple frames (306) as a result of the introduced movement to the one or more components of the camera system (112). The user device (102) performs (506), based on the respective, sub-pixel offsets of the image of the scene across the respective, multiple frames (306), super-resolution computations and creates (508) the super-resolution image of the scene based on the super-resolution computations.

Abstract: The present disclosure describes systems and techniques directed to optical image stabilization movement to create a super-resolution image of a scene. The systems and techniques include a user device (102) introducing (502), through an optical image stabilization system (114), movement to one or more components of a camera system (112) of the user device (102). The user device (102) then captures (504) respective and multiple frames (306) of an image of a scene, where the respective and multiple frames (306) of the image of the scene have respective, sub-pixel offsets of the image of the scene across the multiple frames (306) as a result of the introduced movement to the one or more components of the camera system (112). The user device (102) performs (506), based on the respective, sub-pixel offsets of the image of the scene across the respective, multiple frames (306), super-resolution computations and creates (508) the super-resolution image of the scene based on the super-resolution computations.

Abstract: Generating a location-specific three-dimensional model in response to a location query can provide users with a better understanding of a location through providing better interactivity, better perspective, and better understanding of dimensionality. Generation of the models can be enabled by leveraging a three-dimensional asset database and segmentation methods. The location-specific models can provide further utility by further including situation specific simulated effects, such as simulated weather or traffic.

Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Described examples relate to an apparatus comprising a first sensor configured to scan an area of interest during a first time period and a second sensor configured to capture a plurality of images of a field of view. The apparatus may include at least one controller configured to receive the plurality of images captured by the second sensor, compare the timestamp information associated with at least one image of the plurality of images to at least one time period of the first time period, and select a base image from the plurality of images based on the comparison.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.

Abstract: The present disclosure describes systems and techniques directed to optical image stabilization movement to create a super-resolution image of a scene. The systems and techniques include a user device (102) introducing (502), through an optical image stabilization system (114), movement to one or more components of a camera system (112) of the user device (102). The user device (102) then captures (504) respective and multiple frames (306) of an image of a scene, where the respective and multiple frames (306) of the image of the scene have respective, sub-pixel offsets of the image of the scene across the multiple frames (306) as a result of the introduced movement to the one or more components of the camera system (112). The user device (102) performs (506), based on the respective, sub-pixel offsets of the image of the scene across the respective, multiple frames (306), super-resolution computations and creates (508) the super-resolution image of the scene based on the super-resolution computations.

Abstract: The present disclosure describes systems and techniques for creating a super-resolution image (122) of a scene captured by a user device (102). Natural handheld motion (110) introduces, across multiple frames (204, 206, 208) of an image of a scene, sub-pixel offsets that enable the use of super-resolution computations (210) to form color planes (212, 214, 216), which are accumulated (218) and combined (220) to create a super-resolution image (122) of the scene.

Abstract: The present disclosure provides systems and methods that generate a summary storyboard from a plurality of image frames. An example computer-implemented method can include inputting a plurality of image frames into a machine-learned model and receiving as an output of the machine-learned model, object data that describes the respective locations of a plurality of objects recognized in the plurality of image frames. The method can include generating a plurality of image crops that respectively include the plurality of objects and arranging two or more of the plurality of image crops to generate a storyboard.

Abstract: The present disclosure provides systems and methods that generate a summary storyboard from a plurality of image frames. An example computer-implemented method can include inputting a plurality of image frames into a machine-learned model and receiving as an output of the machine-learned model, object data that describes the respective locations of a plurality of objects recognized in the plurality of image frames. The method can include generating a plurality of image crops that respectively include the plurality of objects and arranging two or more of the plurality of image crops to generate a storyboard.

Abstract: A method to generate an output image that improves observation of a target image viewed on a medium by an optical system is disclosed. The method includes receiving at least one target image by a processing system, receiving at least one parameter by the processing system, defining an error signal associated with the difference between calculated optical system observation of intermediate images and the at least one target image, minimizing the error signal, and generating an output image associated with the intermediate image having the minimized error.

Abstract: A method to generate an output image that improves observation of a target image viewed on a medium by an optical system is disclosed. The method includes receiving at least one target image by a processing system, receiving at least one parameter by the processing system, defining an error signal associated with the difference between calculated optical system observation of intermediate images and the at least one target image, minimizing the error signal, and generating an output image associated with the intermediate image having the minimized error.