Abstract: Apparatus and methods related to photography are provided. A computing device can receive an input image. An object detector of the computing device can determine an object region of interest of the input image that is associated with an object detected in the input image. A trained machine learning algorithm can determine an output photographic region of interest for the input image based on the object region of interest and the input image. The machine learning algorithm can be trained to identify an output photographic region of interest that is suitable for use by a photographic function for image generation. The computing device can generate an output related to the output photographic region of interest.

Abstract: Apparatus and methods related to photography are provided. A computing device can receive an input image. An object detector of the computing device can determine an object region of interest of the input image that is associated with an object detected in the input image. A trained machine learning algorithm can determine an output photographic region of interest for the input image based on the object region of interest and the input image. The machine learning algorithm can be trained to identify an output photographic region of interest that is suitable for use by a photographic function for image generation. The computing device can generate an output related to the output photographic region of interest.

Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.

Abstract: Apparatus and methods related to photography are provided. A computing device can receive an input image. An object detector of the computing device can determine an object region of interest of the input image that is associated with an object detected in the input image. A trained machine learning algorithm can determine an output photographic region of interest for the input image based on the object region of interest and the input image. The machine learning algorithm can be trained to identify an output photographic region of interest that is suitable for use by a photographic function for image generation. The computing device can generate an output related to the output photographic region of interest.

Abstract: A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.

Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.

Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.

Abstract: A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.

Abstract: Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.

Abstract: A device may operate a first image-capture system to capture first image data of a scene. While the first image-capture system is capturing the first image data, the device may operate a second image-capture system to determine an updated value for the first image setting, and send an instruction to the first image-capture system that indicates to use the updated value for the first image setting to continue to capture the first image data.

Abstract: An apparatus, method and computer program product for presenting burst images are provided. The apparatus may include a processor that may be configured to receive a plurality of burst images. Each burst image may differ from the other burst images based on a variable parameter. The processor may also be configured to provide for a presentation of a sample burst image. In this regard, the sample burst image may be one of the plurality of burst images. The processor may be further configured to receive a selected location within the presentation of the sample burst image and provide for a presentation of a plurality of burst image fragments associated with each of the plurality of burst images. In this regard, the burst image fragments may be portions of each of the burst images, where the areas of each burst image may be determined based on the selected location.

Abstract: A device may operate a first image-capture system to capture first image data of a scene. While the first image-capture system is capturing the first image data, the device may operate a second image-capture system to determine an updated value for the first image setting, and send an instruction to the first image-capture system that indicates to use the updated value for the first image setting to continue to capture the first image data.

Abstract: An apparatus, method and computer program product for presenting burst images are provided. The apparatus may include a processor that may be configured to receive a plurality of burst images. Each burst image may differ from the other burst images based on a variable parameter. The processor may also be configured to provide for a presentation of a sample burst image. In this regard, the sample burst image may be one of the plurality of burst images. The processor may be further configured to receive a selected location within the presentation of the sample burst image and provide for a presentation of a plurality of burst image fragments associated with each of the plurality of burst images. In this regard, the burst image fragments may be portions of each of the burst images, where the areas of each burst image may be determined based on the selected location.

Abstract: An apparatus for presenting burst images is provided. The apparatus may include a processor that may be configured to receive a plurality of burst images. Each burst image may differ from the other burst images based on a variable parameter, such as, but not limited to, exposure, focus, and/or time, or to the state of a target, such as the varying facial expression of a person. The processor may also be configured to provide for a presentation of a sample burst image. In this regard, the sample burst image may be one of the plurality of burst images. The processor may be further configured to receive a selected location within the presentation of the sample burst image and provide for a presentation of a plurality of burst image fragments associated with each of the plurality of burst images. In this regard, the burst image fragments may be portions of each of the burst images, where the areas of each burst image may be determined based on the selected location.

Abstract: A computing device may select a source tile from a source image. From the source tile, the computing device may select a first rectangular feature and a second rectangular feature. Based on the first and second rectangular features, the computing device may calculate a source feature vector. The computing device may also select a search area of a target image, and a target tile within the within the search area. Based on the target tile, the computing device may calculate a target feature vector. The computing device may determine that a difference between the source feature vector and the target feature vector is below an error threshold, and based on this determination, further determine a mapping between the source image and the target image. The computing device may then apply the mapping to the source image to produce a transformed source image.

Abstract: Using photographic flash for candid shots often results in an unevenly lit scene, in which objects in the back appear dark. A spatially adaptive photographic flash (100) is disclosed, in which the intensity of illumination (21, 23) varies depending on the depth and reflectivity (30, 101) of features in the scene. Adaption to changes in depth are used in a single-shot method. Adaption to changes in reflectivity are used in a multishot method. The single-shot method requires only a depth image (30), whereas the multi-shot method requires at least one color image (40) in addition to the depth data (30).

Abstract: A computing device may select a source tile from a source image. From the source tile, the computing device may select a first rectangular feature and a second rectangular feature. Based on the first and second rectangular features, the computing device may calculate a source feature vector. The computing device may also select a search area of a target image, and a target tile within the within the search area. Based on the target tile, the computing device may calculate a target feature vector. The computing device may determine that a difference between the source feature vector and the target feature vector is below an error threshold, and based on this determination, further determine a mapping between the source image and the target image. The computing device may then apply the mapping to the source image to produce a transformed source image.

Abstract: Systems and methods using polarized filtering for glare removal to improve the process of scanning documents, particularly bound documents with glossy content, are disclosed. The system may generally include a lighting source for illuminating a document, a lighting source polarizer attached to the lighting source to polarize light therefrom, a camera for capturing images of the document, and a camera polarizer attached to the camera. The camera polarizer and the lighting source polarizer may be positioned relative to each other so as to facilitate blocking light reflections causing glare off of the document resulting from the lighting source and arriving at the camera. The system may include multiple lighting sources, some or all of which may have a lighting source polarizer attached thereto.

Abstract: Using photographic flash for candid shots often results in an unevenly lit scene, in which objects in the back appear dark. A spatially adaptive photographic flash (100) is disclosed, in which the intensity of illumination (21, 23) varies depending on the depth and reflectivity (30, 101) of features in the scene. Adaption to changes in depth are used in a single-shot method. Adaption to changes in reflectivity are used in a multishot method. The single-shot method requires only a depth image (30), whereas the multi-shot method requires at least one color image (40) in addition to the depth data (30).