Abstract: A method of recognizing the environment of an image from an image and position information associated with the image includes acquiring the image and its associated position information; using the position information to acquire an aerial image correlated to the position information; identifying the environment of the image from the acquired aerial image; and storing the environment of the image in association with the image for subsequent use.

Abstract: A method for scene classification of a digital image includes extracting pre-determined camera metadata tags from the digital image. The method also includes obtaining estimates of image class based on the extracted metadata tags. In addition, the method includes obtaining estimates of image class based on image and producing a final estimate of image class based on a combination of metadata-based estimates and image content-based estimates.

Abstract: A method for detecting and correcting the orientation of a radiograph. The method generally includes the steps of accessing the radiograph; locating a region of interest in the radiograph; and using the located region of interest and a classifier to detect the orientation of the radiograph. In a preferred embodiment, the radiograph is preprocessed.

Abstract: In a method, computer program, and system a dental target, such as a tooth, is located within a digital dental image. A reference object that was placed in the patient's mouth is segmented within a digital dental image to provide a segmented reference. The reference object has a predetermined size dimension. A window is segmented in the dental image at the position of the dental target. The segmented reference defines the relative size and location of the window, prior to the segmenting of the window.

Abstract: In a method, computer program, and system a dental target, such as a tooth, is located within a digital dental image. A reference object that was placed in the patient's mouth is segmented within a digital dental image to provide a segmented reference. The reference object has a predetermined size dimension. A window is segmented in the dental image at the position of the dental target. The segmented reference defines the relative size and location of the window, prior to the segmenting of the window.

Abstract: Video communication systems and methods for communicating between an individual in a local environment, and a remote viewer in a remote environment are provided. The system has an image display device; at least one image capture device which acquires video images for fields of view of a local environment, and any individuals therein; an audio system having an audio emission device and an audio capture device; a computer, which includes a contextual interface having a gaze adapting process, and image processor; and a communication controller which transmits and receives video images of the local environment and the remote environment, and data regarding video scene characteristics thereof across a network between the local environment and the remote environment; wherein the gaze adapting process identifies video scene characteristics of the local environment indicative of eye gaze image capture and altering the video images when the characteristics are indicative.

Abstract: A method for segmenting an anatomical structure having portions occluded in a digital image wherein the digital image is produced by a projection-based medical imaging modality. The method includes the steps of: outlining an unoccluded portion of the anatomical structure in a training image; augmenting the occluded portion of the anatomical structure according to a pre-determined atlas of the anatomical structure; generating an augmented training example using the outlined and augmented portions; repeating the steps of outlining, augmenting, and generating for a plurality of training images to train a statistical model of the structure; accessing the digital image; and segmenting the anatomical structure in the accessed digital image according to the statistical model.

Abstract: A method of detecting sky in a digital color image having pixels is disclosed. The method includes identifying pixels from the digital color image representing an initial sky region; developing a model based on the identified sky pixels, wherein such model is a mathematical function that has inputs of pixel position and outputs of color; and using the model to operate on the digital color image to classify additional pixels not included in the initial sky region as sky.

Abstract: A system and method for automatically altering an image having pixels is disclosed. An image is segmented into regions and various features of the regions are extracted and used to calculate a confidence factor, or belief value, that a particular region is a main subject of the image. The belief values are assembled into a belief map that is used to define how pixel values are to be altered. Various color and appearance values of the pixel are altered in a predetermined way to emphasize the main subject of the image. The emphasis is applied in a binary fashion or as in a gradient of values. Either the main subject can be emphasized, or the secondary or background subjects can be de-emphasized. The system can be implement on a variety of computer systems.

Abstract: A method of classifying regions in a digital image or video captured by an image capture device includes providing a geographic location determining device associated with the image capture device; using the location determining device to measure the image capture location at substantially the time that the digital image or video was captured; and classifying regions in the captured digital image or video into one or more classes based on the image-capture location.

Abstract: A method for improving scene classification of a digital image comprising the steps of: (a) providing an image; (b) systematically recomposing the image to generate an expanded set of images; and (c) using a classifier and the expanded set of images to determine a scene classification for the image, whereby the expanded set of images provides at least one of an improved classifier and an improved classification result.

Abstract: In a method and digital camera, an initial set of evaluation images are captured. A plurality of characteristics of the initial set of evaluation images are assessed to provide a first assessment. The characteristics include subject motion between the initial set of evaluation images. When the subject motion is in excess of a predetermined threshold, a final capture state of the camera is set responsive to the first assessment. When the subject motion is less than the predetermined threshold, the evaluation images are analyzed to provide analysis results and the final capture state of the camera is set responsive to the first assessment and the analysis results.

Abstract: A method of producing a video thumbnail for previewing a video file representing a digital video in a file browser includes extracting a plurality of key frames from the video file; producing a video thumbnail using an encoded representation of the extracted key frames; and displaying the video thumbnail through the file browser.

Abstract: In a method and digital camera, initial evaluation images of a scene are captured with the camera. A change in scene brightness between two or more of the evaluation images is computed. When the scene brightness change is outside a predetermined brightness range, a metric of scene-to-capture mismatch in the evaluation images is determined. When the brightness change and the metric are both outside predetermined ranges, the camera is shifted to a second capture state prior to the capturing of one or more additional evaluation images. In either case, a final camera state is determined using available evaluation images and one or more final images are captured with the camera in the final camera state.

Abstract: A method for replacing a face in a first digital image with a face from a second digital image including automatically detecting one or more faces in the first digital image; identifying at least one target face from the detected faces that needs to be replaced; automatically detecting one or more faces in the second digital image; identifying at least one source face from the second image detected faces for replacing the target face; using features from the target and source faces to perform facial geometry and appearance corrections to the source face; and replacing the target face with the corrected source face and blending the corrected source face into the first digital image.

Abstract: In a method and digital camera, an initial pair of evaluation images are captured. A plurality of characteristics of the initial pair of evaluation images are assessed to provide a first assessment. The characteristics include subject motion between the initial pair of evaluation images. When the subject motion is in excess of a predetermined threshold, a final capture state of the camera is set responsive to the first assessment. When the subject motion is less than the predetermined threshold, the evaluation images are analyzed to provide analysis results and the final capture state of the camera is set responsive to the first assessment and the analysis results.

Abstract: In a method and system for detecting digital image compositing, a first artifact is detected in an uncompressed digital image. The first artifact is characteristic of an earlier compression of the digital image with a predetermined compression algorithm. An interpolation marker in the image is evaluated to provide interpolation results. During the evaluating, a second artifact of the compression algorithm is counteracted. The image is classified responsive to the interpolation results. The first artifact can be a blocking artifact and the second artifact and interpolation marker can be different periodicities in the image.

Abstract: A method for detecting an object in a digital image including the steps of performing color quantization on a model image including the object and on a search image that potentially includes the object, generating a plurality of search windows, computing spatial-color joint probability functions of each model and search image, where the color co-occurrence edge histogram is chosen to be the spatial-color joint probability function, assessing the similarity of each search image to the model, and designating search windows as containing the target object.

Abstract: A method of analyzing a digital video clip captured by a camera to determine candidate frames for subsequent key frame selection including providing a camera motion sensor in the camera so that information is provided during image capture regarding camera motion including translation of the scene or camera, or scaling of the scene; forming a plurality of video segments based on the global motion estimate and labeling each segment in accordance with a predetermined series of camera motion classes; extracting key frame candidates from the labeled segments and computing a confidence score for each candidate by using rules corresponding to each camera motion class and a rule corresponding to object motion.

Abstract: A method of analyzing a digital video clip to determine candidate frames for subsequent key frame selection including performing a global motion estimate on the video clip that indicates translation of the scene or camera, or scaling of the scene; forming a plurality of video segments based on the global motion estimate and labeling each segment in accordance with a predetermined series of camera motion classes; extracting key frame candidates from the labeled segments and computing a confidence score for each candidate by using rules corresponding to each camera motion class and a rule corresponding to object motion.