Abstract: A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.

Abstract: A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.

Abstract: A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.

Abstract: The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.

Abstract: The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.

Abstract: A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

Abstract: A method to determine activity in a sequence of successively acquired images of a scene, comprises: acquiring the sequence of images; for each image in the sequence of images, forming a feature block of features extracted from the image and determining image specific information including a weighting for the image; normalizing the determined weightings to form a normalized weighting for each image in the sequence of images; for each image in the sequence of images, combining the associated normalized weighting and associated feature block to form a weighted feature block; passing a combination of the weighted feature blocks through a predictive module to determine an activity in the sequence of images; and outputting a result comprising the determined activity in the sequence of images.

Abstract: The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.

Abstract: The technology relates to tuning a data translation block (DTB) including a generator model and a discriminator model. One or more processors may be configured to receive training data including an image in a second domain. The image in the second domain may be transformed into a first domain with a generator model. The transformed image may be processed to determine one or more outputs with one or more deep neural networks (DNNs) trained to process data in the first domain. An original objective function for the DTB may be updated based on the one or more outputs. The generator and discriminator models may be trained to satisfy the updated objective function.

Abstract: A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

Abstract: A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

Abstract: A method for dynamically calibrating an image capture device comprises: a) determining a distance (DCRT, DEST) to an object within a scene; b) determining a first lens actuator setting (DACINIT) for the determined distance; c) determining a second lens actuator setting (DACFOCUS) providing maximum sharpness for the object in a captured image of the scene; and d) storing the determined distance (DCRT, DEST) and the first and second lens actuator settings. These steps are repeated at a second determined distance separated from the first determined distance. A calibration correction (ERRNEARPLP, ERRFARPLP) for stored calibrated lens actuator settings (DACNEARPLP, DACFARPLP) is determined as a function of at least: respective differences between the second lens actuator setting (DACFOCUS) and the first lens actuator setting (DACINIT) for each of the first and second determined distances; and the stored calibrated lens actuator settings are adjusted according to the determined calibration corrections.

Abstract: A method of processing an image comprises: acquiring an image of a scene including an object having a recognizable feature. A lens actuator setting providing a maximum sharpness for a region of the image including the object and a lens displacement corresponding to the lens actuator setting are determined. A distance to the object based on the lens displacement is calculated. A dimension of the feature as a function of the distance to the object, the imaged object size and a focal length of a lens assembly with which the image was acquired, is determined. The determined dimension of the feature is employed instead of an assumed dimension of the feature for subsequent processing of images of the scene including the object.

Abstract: A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

Abstract: A method for acquiring an image comprises acquiring a first image frame including a region containing a subject at a first focus position; determining a first sharpness of the subject within the first image frame; identifying an imaged subject size within the first image frame; determining a second focus position based on the imaged subject size; acquiring a second image frame at the second focus position; and determining a second sharpness of the subject within the second image frame. A sharpness threshold is determined as a function of image acquisition parameters for the first and/or second image frame. Responsive to the second sharpness not exceeding the first sharpness and the sharpness threshold, camera motion parameters and/or subject motion parameters for the second image frame are determined before performing a focus sweep to determine an optimal focus position for the subject.

Abstract: A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.

Abstract: A method for dynamically calibrating an image capture device comprises: a) determining a distance (DCRT, DEST) to an object within a scene; b) determining a first lens actuator setting (DACINIT) for the determined distance; c) determining a second lens actuator setting (DACFOCUS) providing maximum sharpness for the object in a captured image of the scene; and d) storing the determined distance (DCRT, DEST) and the first and second lens actuator settings. These steps are repeated at a second determined distance separated from the first determined distance. A calibration correction (ERRNEARPLP, ERRFARPLP) for stored calibrated lens actuator settings (DACNEARPLP, DACFARPLP) is determined as a function of at least: respective differences between the second lens actuator setting (DACFOCUS) and the first lens actuator setting (DACINIT) for each of the first and second determined distances; and the stored calibrated lens actuator settings are adjusted according to the determined calibration corrections.

Abstract: A method for acquiring an image comprises acquiring a first image frame including a region containing a subject at a first focus position; determining a first sharpness of the subject within the first image frame; identifying an imaged subject size within the first image frame; determining a second focus position based on the imaged subject size; acquiring a second image frame at the second focus position; and determining a second sharpness of the subject within the second image frame. A sharpness threshold is determined as a function of image acquisition parameters for the first and/or second image frame. Responsive to the second sharpness not exceeding the first sharpness and the sharpness threshold, camera motion parameters and/or subject motion parameters for the second image frame are determined before performing a focus sweep to determine an optimal focus position for the subject.

Abstract: A method for dynamically calibrating an image capture device comprises: a) determining a distance (DCRT, DEST) to an object within a scene; b) determining a first lens actuator setting (DACINIT) for the determined distance; c) determining a second lens actuator setting (DACFOCUS) providing maximum sharpness for the object in a captured image of the scene; and d) storing the determined distance (DCRT, DEST) and the first and second lens actuator settings. These steps are repeated at a second determined distance separated from the first determined distance. A calibration correction (ERRNEARPLP, ERRFARPLP) for stored calibrated lens actuator settings (DACNEARPLP, DACFARPLP) is determined as a function of at least: respective differences between the second lens actuator setting (DACFOCUS) and the first lens actuator setting (DACINIT) for each of the first and second determined distances; and the stored calibrated lens actuator settings are adjusted according to the determined calibration corrections.

Abstract: A method for dynamically calibrating an image capture device comprises: a) determining a distance (DCRT, DEST) to an object within a scene; b) determining a first lens actuator setting (DACINIT) for the determined distance; c) determining a second lens actuator setting (DACFOCUS) providing maximum sharpness for the object in a captured image of the scene; and d) storing the determined distance (DCRT, DEST) and the first and second lens actuator settings. These steps are repeated at a second determined distance separated from the first determined distance. A calibration correction (ERRNEARPLP, ERRFARPLP) for stored calibrated lens actuator settings (DACNEARPLP, DACFARPLP) is determined as a function of at least: respective differences between the second lens actuator setting (DACFOCUS) and the first lens actuator setting (DACINIT) for each of the first and second determined distances; and the stored calibrated lens actuator settings are adjusted according to the determined calibration corrections.