Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A statistically lossless transform (SLT) can be used with data (e.g., image data) to be compressed. Prior to compression, an SLT stabilizes the variance for a read noise+Poisson process. The SLT redistributes (image) data that has been quantized with a linear uniform quantizer and essentially assures there are no gaps in the output values (i.e., all output digital values are used). The SLT re-quantizes the data with a quantization interval width that is proportional to the standard deviation of the process.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A digital video camera system that provides a video summary using a method that includes: designating a reference image containing a particular person; capturing a video sequence of the scene using the image sensor, the video sequence including a time sequence of image frames; processing the captured video sequence using a video processing path to form a digital video file; during the capturing of the video sequence, analyzing the captured image frames using a person recognition algorithm to identify a subset of the image frames that contain the particular person; forming the video summary including fewer than all of the image frames in the captured video sequence, wherein the video summary includes at least part of the identified subset of image frames containing the particular person; storing the digital video file in the storage memory; and storing a representation of the video summary in the storage memory.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for selecting a digital image having controlled sharpness characteristics from a set of candidate digital images of a common scene, each digital image having different sharpness characteristics. An image segmentation process is used to segment each of the candidate digital images into a subject region and a background region. For each candidate digital image the subject and background regions are analyzed to determine an associated subject and background sharpness levels. An output digital image is selected by comparing the determined subject and background sharpness levels to respective aim subject and background sharpness levels. In some embodiments, the aim subject and background sharpness levels are defined in accordance with a scene type classification.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A set of key video frames are selected based on the determined video frame clusters.

Abstract: The sharpness of a digital image is adjusted according to defined aim subject and background sharpness levels. An image segmentation process is used to segment an input digital image into a subject region and a background region. The subject and background regions are analyzed to determine corresponding subject and background sharpness levels. An enhanced digital image is formed wherein the sharpness of the subject region is adjusted responsive to the subject sharpness level and the aim subject sharpness level, and the sharpness of the background region is adjusted responsive to the background sharpness level and the aim background sharpness level. In some embodiments, the input digital image is analyzed to determined a scene type classification and the aim subject and background sharpness levels are defined in accordance with the determined scene type classification.

Abstract: A digital video camera system that provides a video summary using a method that includes: designating a reference image containing a particular person; capturing a video sequence of the scene using the image sensor, the video sequence including a time sequence of image frames; processing the captured video sequence using a video processing path to form a digital video file; during the capturing of the video sequence, analyzing the captured image frames using a person recognition algorithm to identify a subset of the image frames that contain the particular person; forming the video summary including fewer than all of the image frames in the captured video sequence, wherein the video summary includes at least part of the identified subset of image frames containing the particular person; storing the digital video file in the storage memory; and storing a representation of the video summary in the storage memory.

Abstract: The sharpness of a digital image is adjusted according to defined aim subject and background sharpness levels. An image segmentation process is used to segment an input digital image into a subject region and a background region. The subject and background regions are analyzed to determine corresponding subject and background sharpness levels. An enhanced digital image is formed wherein the sharpness of the subject region is adjusted responsive to the subject sharpness level and the aim subject sharpness level, and the sharpness of the background region is adjusted responsive to the background sharpness level and the aim background sharpness level. In some embodiments, the input digital image is analyzed to determined a scene type classification and the aim subject and background sharpness levels are defined in accordance with the determined scene type classification.

Abstract: A method for selecting a digital image having controlled sharpness characteristics from a set of candidate digital images of a common scene, each digital image having different sharpness characteristics. An image segmentation process is used to segment each of the candidate digital images into a subject region and a background region. For each candidate digital image the subject and background regions are analyzed to determine an associated subject and background sharpness levels. An output digital image is selected by comparing the determined subject and background sharpness levels to respective aim subject and background sharpness levels. In some embodiments, the aim subject and background sharpness levels are defined in accordance with a scene type classification.

Abstract: A digital video camera system that provides a video summary using a method that includes: specifying reference data, wherein the reference data indicates a feature of interest; capturing a video sequence of the scene using the image sensor, the video sequence including a time sequence of image frames; processing the captured video sequence using a video processing path to form a digital video file; during the capturing of the video sequence, analyzing the captured image frames using a feature recognition algorithm to identify a subset of the image frames that contain the feature of interest; forming the video summary including fewer than all of the image frames in the captured video sequence, wherein the video summary includes at least part of the identified subset of image frames containing the feature of interest; storing the digital video file in the storage memory; and storing a representation of the video summary in the storage memory.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A set of key video frames are selected based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A digital video camera system that provides a video summary using a method that includes: designating a reference image containing a particular person; capturing a video sequence of the scene using the image sensor, the video sequence including a time sequence of image frames; processing the captured video sequence using a video processing path to form a digital video file; during the capturing of the video sequence, analyzing the captured image frames using a person recognition algorithm to identify a subset of the image frames that contain the particular person; forming the video summary including fewer than all of the image frames in the captured video sequence, wherein the video summary includes at least part of the identified subset of image frames containing the particular person; storing the digital video file in the storage memory; and storing a representation of the video summary in the storage memory.

Abstract: A method for determining image capture settings for an electronic image capture device, comprising: capturing at least two preview images of a scene; analyzing the preview images to determine a combined motion velocity; determining one or more image capture settings responsive to the combined motion velocity; and capturing an archival image according to the determined image capture settings. The determination of the combined motion velocity includes: defining a plurality image regions; determining local motion velocities for each of the image regions; and combining the local motion velocities to determine the combined motion velocity.