Abstract: A method for modifying an input digital image having input dimensions defined by a number of input rows and input columns to form an output digital image where the number of rows or columns is reduced by one, comprising an image energy map determined from the input image; determining a seam path responsive to the image energy map; imposing constraints on the seam path; and removing pixels along the seam path to modify the input digital image.

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 of analyzing a digital video clip to determine candidate frames for subsequent key frame selection, includes: performing a global motion estimate on the video clip that indicates translation of a scene or camera, or scaling of the scene; and 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. The method further includes 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 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 of assigning semantic labels to images in a particular collection, includes acquiring seed labels for a subset of images; propagating the seed labels to other images according to a similarity metric; and storing the semantic labels, including both seed labels and propagated labels with the corresponding images.

Abstract: A method for extracting an object out of each image in a group of digital images that contain the object, includes providing a group of digital images, each containing the object with a background; selecting a seed image from the group of digital images, and displaying the seed image to a user; the user providing at least one marking for the seed image, which corresponds to a subset of pixels in the seed image that indicates whether the set of pixels belongs to a part of object of interest or a part of the background; producing from the seed image and the marking a statistical model that can be used for separating the object of interest from the background in the group of images; and applying the statistical model to each image in the group of digital images to produce a cutout of the object of interest from each digital image.

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 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: 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: A method for determining a candidate lesion region within an ultrasound image. The method includes the steps of: accessing a digital ultrasound image of anatomical tissue; segmenting spatially contiguous pixels in the ultrasound image into a plurality of regions in accordance with substantially similar intensity values and spatial smoothness constraints; and selecting, from the plurality of regions, one or more candidate lesion regions having an intensity value lower than a pre-determined intensity value. In one arrangement, the one or more candidate lesion region is classified into at least one of the following classes: benign, malignant, or unknown.

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 of computing at least one photogenic route from a starting location to a destination location, including; computing photogenic values for images in a large collection representing a geographic region that includes the starting location and the destination location; computing a photogenic index for each route segment based on computed photogenic values of images taken along the route segment; computing at least one photogenic route from the starting location to the destination location and presenting the route(s) to a user

Abstract: A method of recognizing an event depicted in an image from the image and a location information associated with the image is disclosed. The method includes acquiring the image and its associated location information; using the location information to acquire an aerial image(s) correlated to the location information; identifying the event using both the image and the acquired aerial image(s); and storing the event in association with the image for subsequent use.

Abstract: A method of categorizing a social relationship between individuals in a collection of images to suggest a possible course of action, includes searching the collection to identify individuals and determining their genders and their age ranges; using the gender, and age ranges of the identifies individuals to infer at least one social relationship between them; and using at least one inferred social relationship to suggest a possible course of action.

Abstract: A method for improving scene classification of a sequence of digital images is disclosed herein. Such a method may include providing a sequence of images captured in temporal succession; (b) classifying each of the images individually based on information contained in the image alone to generate a first image classification; and (c) imposing a pre-determined temporal context model on the sequence of images to generate a final image classification for each image in the sequence.

Abstract: A method of processing a digital image, including the steps of: assigning belief values to pixels in the digital image, wherein the belief values are proportional to a belief that the pixels are associated with human skin; calculating a brightness adjustment amount based on a statistic of the pixels with belief values exceeding a threshold and the assigned belief values of the pixels; and applying the brightness adjustment amount to the digital image to produce a processed digital image having improved overall brightness.

Abstract: Classification of a group of temporally related images is disclosed, wherein a capture-location sequence is identified from the group of temporally related images. The capture-location-sequence information, which is associated collectively with the capture-location sequence, is compared with each of a plurality of sets of predetermined capture-location-sequence characteristics. Each set is associated with a predetermined classification. An identified classification associated with the group of temporally related images is identified based at least upon results from the comparing step; and the identified classification is stored in a processor-accessible memory system.

Abstract: A method for multitone processing an N level digital image to produce an M level digital image wherein M<N, includes the steps of: determining M reconstruction levels based on the gray level distribution of the N level image; and applying multilevel dithering to the N level digital image using the M reconstruction levels to produce the M level digital image.

Abstract: A method for recognizing the projection view of radiographs comprising the steps of correcting the orientation of the input radiograph, locating a region of interesting in the radiograph, recognizing the projection view of the radiograph.

Abstract: A method of categorizing the relationship of individuals from a collection of photos taken in multiple sets of events includes: searching the photo collection to identify individuals in the photo collection, determining the gender and age range of the identified individuals; and using the identified individuals, their occurrences and co-occurrences, their gender, and their ages to infer social relationships between the individuals based on a set of rules.