Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. A profile model and a shape model may be applied to an object in an image to determine locations of feature points for each object component. Input may be received to move one of the feature points to a fixed location. Other ones of the feature points may be automatically adjusted to different locations based on the moved feature point.

Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. An object in an input image may be detected. A profile model may be applied to determine feature point locations for each object component of the detected object. Applying the profile model may include globally optimizing the feature points for each object component to find a global energy minimum. A component-based shape model may be applied to update the respective feature point locations for each object component.

Abstract: Methods and systems, including computer program products, implementing techniques for reviewing and editing word processing documents. The techniques include receiving from a reviewer a plurality of comments about a word processing document. The comments include a first and second comment. The techniques further include associating the comments with the word processing document; and assigning the comments to one or more editors. The first comment is assigned to a first editor, and the second comment is assigned to a second editor.

Abstract: Various embodiments of methods and apparatus for face fitting are disclosed. In one embodiment, sets of two-dimensional local feature points on a face in each image of a set of images are identified. The set of images includes a sequence of frames a video stream. A three-dimensional face model for the face in the each image is generated as a combination of a set of predefined three-dimensional face models. In some embodiments, the generating includes reducing an error between a projection of vertices of the set of predefined three-dimensional face models and the two-dimensional local feature points of the each image, and constraining facial expression of the three-dimensional face model to change smoothly from image to image in the sequence of video frames.

Abstract: Three dimensional models corresponding to a target image and a reference image are selected based on a set of feature points defining facial features in the target image and the reference image. The set of feature points defining the facial features in the target image and the reference image are associated with corresponding 3-dimensional models. A 3D motion flow between the 3-dimensional models is computed. The 3D motion flow is projected onto a 2D image plane to create a 2D optical field flow. The target image and the reference image are warped using the 2D optical field flow. A selected feature from the reference image is copied to the target image.

Abstract: Various embodiments of a system and methods for labeling images are described. An image labeling system may receive multiple images. A subset of the images may be labeled to identify image content or elements, such as faces. The system may display some of the labeled image elements in different portions of a display area. Unlabeled image elements may be displayed in the same display area. The display size and position of each unlabeled image element may be dependent on similarities between the unlabeled image element and the displayed, labeled image elements. The system may receive input which indicates that one or more of the displayed, unlabeled image elements should receive the same label as one of the displayed, labeled image elements. Similarities between image elements may be re-analyzed, dependent on the assigned labels, and the display of unlabeled images may be updated.

Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. A profile model and a shape model may be applied to an object in an image to determine locations of feature points for each object component. Input may be received to move one of the feature points to a fixed location. Other ones of the feature points may be automatically adjusted to different locations based on the moved feature point.

Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. An object in an input image may be detected. A profile model may be applied to determine feature point locations for each object component of the detected object. Applying the profile model may include globally optimizing the feature points for each object component to find a global energy minimum. A component-based shape model may be applied to update the respective feature point locations for each object component.

Abstract: Various embodiments of a system and methods for using contextual features to improve face recognition in digital images are described. A face recognition system may semi-automatically label faces and/or search for faces in images. Faces may be detected in the images and visual and non-visual features may be determined for each face. The determined features may be facial and/or contextual features. Features may also be determined from labels assigned to the faces. Pair-wise comparisons of the faces, using the determined features, may determine distances between pairs of faces. Some features may be used to determine the relevance of other features. Dependent on the calculated distances, similar faces may be grouped and suggested labels for faces may be provided. The system may be scalable, processing faces in groups to limit the use of system resources to a certain amount, regardless of the size of a digital image collection.

Abstract: A set of feature points for defining features of a face is detected in a target image and a reference image. The target image is warped to accommodate a selected feature from the reference image using the set of feature points and a set of three-dimensional models. The reference image is warped to a pose of the target image using the set of feature points and the set of three-dimensional models. The selected feature is copied from the reference image to the target image.

Abstract: A set of feature points for defining features of a face is detected in a target image and a reference image. The target image is warped to accommodate a selected feature from the reference image using the set of feature points and a set of three-dimensional models. The reference image is warped to a pose of the target image using the set of feature points and the set of three-dimensional models. The selected feature is copied from the reference image to the target image.

Abstract: Various embodiments of a system and methods for labeling images are described. An image labeling system may receive multiple images. A subset of the images may be labeled to identify image content or elements, such as faces. The system may display some of the labeled image elements in different portions of a display area. Unlabeled image elements may be displayed in the same display area. The display size and position of each unlabeled image element may be dependent on similarities between the unlabeled image element and the displayed, labeled image elements. The system may receive input which indicates that one or more of the displayed, unlabeled image elements should receive the same label as one of the displayed, labeled image elements. Similarities between image elements may be re-analyzed, dependent on the assigned labels, and the display of unlabeled images may be updated.

Abstract: Three dimensional models corresponding to a target image and a reference image are selected based on a set of feature points defining facial features in the target image and the reference image. The set of feature points defining the facial features in the target image and the reference image are associated with corresponding 3-dimensional models. A 3D motion flow between the 3-dimensional models is computed. The 3D motion flow is projected onto a 2D image plane to create a 2D optical field flow. The target image and the reference image are warped using the 2D optical field flow. A selected feature from the reference image is copied to the target image.

Abstract: Various embodiments of a system and methods for using contextual features to improve face recognition in digital images are described. A face recognition system may semi-automatically label faces and/or search for faces in images. Faces may be detected in the images and visual and non-visual features may be determined for each face. The determined features may be facial and/or contextual features. Features may also be determined from labels assigned to the faces. Pair-wise comparisons of the faces, using the determined features, may determine distances between pairs of faces. Some features may be used to determine the relevance of other features. Dependent on the calculated distances, similar faces may be grouped and suggested labels for faces may be provided. The system may be scalable, processing faces in groups to limit the use of system resources to a certain amount, regardless of the size of a digital image collection.

Abstract: Various embodiments of a system and method for labeling images are described. An image labeling system may label multiple images, where each of the images may be labeled to identify image content or image elements, such as backgrounds or faces. The system may display some of the labeled image elements in different portions of a display area. Unlabeled image elements may be displayed in the same display area. The display size and position of each unlabeled image element may be dependent on similarities between the unlabeled image element and the displayed, labeled image elements. The system may also receive gesture input in order to determine a corresponding labeling task to perform.

Abstract: Various embodiments of methods and apparatus for face fitting are disclosed. In one embodiment, sets of two-dimensional local feature points on a face in each image of a set of images are identified. The set of images includes a sequence of frames a video stream. A three-dimensional face model for the face in the each image is generated as a combination of a set of predefined three-dimensional face models. In some embodiments, the generating includes reducing an error between a projection of vertices of the set of predefined three-dimensional face models and the two-dimensional local feature points of the each image, and constraining facial expression of the three-dimensional face model to change smoothly from image to image in the sequence of video frames.

Abstract: Methods and systems, including computer program products, implementing techniques for reviewing and editing word processing documents. The techniques include receiving from a reviewer a first comment about a word processing document receiving from the reviewer a second comment about the word processing document, sending the first comment to one or more editors and independently sending the second comment to one or more editors. The first comment can be sent before the second comment is received.

Abstract: Methods and systems, including computer program products, implementing techniques for reviewing and editing word processing documents. The techniques include receiving from a reviewer one or more comments about a word processing document, receiving from an editor one or more changes to the word processing document, and classifying each change under one of the comments. The techniques can further include selecting a subset of the comments and presenting the word processing document so that the changes that are classified under the selected comments are distinguishable from the changes that are not classified under the selected comments.

Abstract: Systems and techniques for autocompleting form fields based on observed values. In general, in one implementation, the techniques include observing values entered in form field objects, generating likelihood assessments for possible values to be entered in a current form field object based on the observed values, the likelihood assessments indicating relative probability of the possible values being entered in the current form field object, and predicting a value for the current form field object based on the generated likelihood assessments. The techniques also include generating at least two likelihood assessments for each of the possible values, and aggregating the at least two likelihood assessments for each of the possible values. Generation of likelihood assessments can involve determining semantic similarity between form fields, including comparing field names with a semantic category, and generation of likelihood assessments can also involve comparing entered value ordering information.

Abstract: One embodiment of the present invention provides a system for detecting an occurrence of an object in an image. During operation, the system selects a subwindow to be evaluated based upon a currently estimated likelihood of the object being detected within the subwindow. The system then performs an evaluation step on the subwindow to determine if the object exists in the subwindow, wherein performing the evaluation step involves updating the currently estimated likelihood of the object being detected in the subwindow. If evaluation of the subwindow is not complete after performing the evaluation step, the system stores information associated with the subwindow to facilitate subsequent evaluation of the subwindow.