Abstract: A method and apparatus for user interaction with a video stream includes providing a video stream capturing a live gaming field, analyzing the video stream so as to detect at least one gaming object of the live gaining field in the video stream, mapping position data related to the respective detected gaming object in the video stream into real world locations so as to prove real world location information related to the respective detected gaming object, the mapping being performed based on a known transformation relation between the positions in the video stream and real world locations, identifying a potential future game event or a combination of future game events, and at least occasionally amending the video stream with at least one interactive visual marker related to the identified potential future game event or combination of game events.

Abstract: A method and apparatus for user interaction with a video stream includes providing a video stream capturing a live gaming field, analyzing the video stream so as to detect at least one gaming object of the live gaining field in the video stream, mapping position data related to the respective detected gaming object in the video stream into real world locations so as to prove real world location information related to the respective detected gaming object, the mapping being performed based on a known transformation relation between the positions in the video stream and real world locations, identifying a potential future game event or a combination of future game events, and at least occasionally amending the video stream with at least one interactive visual marker related to the identified potential future game event or combination of game events.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image.

Abstract: Systems and methods for improving automatic selection of keeper images from a commonly captured set of images are described. A combination of image type identification and image quality metrics may be used to identify one or more images in the set as keeper images. Image type identification may be used to categorize the captured images into, for example, three or more categories. The categories may include portrait, action, or “other.” Depending on the category identified, the images may be analyzed differently to identify keeper images. For portrait images, an operation may be used to identify the best set of faces. For action images, the set may be divided into sections such that keeper images selected from each section tell the story of the action. For the “other” category, the images may be analyzed such that those having higher quality metrics for an identified region of interest are selected.

Abstract: Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image.

Abstract: Techniques are disclosed for generating landmark models based on exemplar portions of images (“patches”) that are known to include the target landmark (forming a “positive” set of landmark vectors) and those that do not (forming a “negative” set of landmark vectors). Theses sets of positive and negative landmark vectors, along with other landmark statistics, form a landmark model. When an unknown image is received, candidate landmark vectors may be generated based on the image's content (or portion thereof) and applied to the landmark model to rapidly reduce the dimensionality (complexity) of the candidate landmark vector space. Landmark models may be applied to the reduced-dimensioned candidate landmark vectors to identify the most likely point corresponding to the target landmark.

Abstract: A technique for combining multiple individual feature detectors to identify a combined feature in a digital image is disclosed. A combined feature detection rule may specify multiple individual feature detectors with which an image is to be analyzed. The multiple individual feature detectors may identify constituent parts of the combined feature and/or may identify features based on different image properties. An analysis of the image with the specified feature detectors may result in the identification of multiple candidate regions (i.e., regions within which the detectors identify their respective features). The combined feature detection rule may operate directly on the multiple candidate regions to adjust the spatial properties of the candidate regions and group the adjusted candidate regions into candidate region groups, it may then be determined if one or more of the candidate region groups is representative of a presence of the combined feature in the image.

Abstract: Systems, methods, and computer readable media for determining and applying face recognition parameter sets are described. In general, techniques are disclosed for identifying and constructing a unique combination of facial recognition discriminators into a “face feature vector” that has been found to be more robust (e.g., stable to image noise, a person's pose, and scene illumination) and accurate (e.g., provide high recognition rates) than prior art techniques. More particularly, a face feature vector may be generated by the combination of shape descriptors (e.g., as generated by two-dimensional and three-dimensional shape models) and texture descriptors (e.g., as generated by global and local texture models).

Abstract: Systems, methods, and computer readable media for determining and applying face recognition parameter sets are described. In general, techniques are disclosed for identifying and constructing a unique combination of facial recognition discriminators into a “face feature vector” that has been found to be more robust (e.g., stable to image noise, a person's pose, and scene illumination) and accurate (e.g., provide high recognition rates) than prior art techniques. More particularly, a face feature vector may be generated by the combination of shape descriptors (e.g., as generated by two-dimensional and three-dimensional shape models) and texture descriptors (e.g., as generated by global and local texture models).