Apple Inc.

Michael Rousson

Jan Erik Solem

Todd Stephen Sachs

Justin S Titi

Marco Zuliani

Claus Molgaard

Elliott B Harris

Brett M Keating

Karl Hsu

Vincent Y Wong

Jerome Piovano

Jeff Brasket

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.

Automated selection of keeper images from a burst photo captured set

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.

Object landmark detection in images

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 &#x201c;negative&#x201d; 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.

Landmark detection in digital images

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).

Face feature vector construction

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Palo Alto, CA, United States of America

Michael Rousson