Abstract: Foreground and background regions of a digital image of a scene are distinguished from each other automatically. Foreground objects are identified in a binary image map that distinguishes between foreground pixels and background pixels. From the foreground objects, a primary foreground object is identified. Within the identified primary foreground object, a head region of the primary foreground object is located. Within the head region, patterns of foreground pixels and background pixels that are indicative of a head crown region are identified. Within the head crown region, pixels identified as background pixels that actually show portions of the primary foreground object are converted to foreground pixels, thus improving the accuracy of the binary image map.

Abstract: Sub-regions within one or more face images are identified within a digital image, and enhanced by applying an artificial glint symmetrically and/or synchronously to image data corresponding to sub-regions of eyes within the face image. An enhanced face image is generated including an enhanced version of the face that includes certain original pixels in combination with pixels corresponding to the one or more eye regions of the face with the artificial glint.

Abstract: A technique involves distinguishing between foreground and background regions of a digital image of a scene. First and second images are captured of nominally a same scene. The first image is a relatively high resolution image taken with the foreground more in focus than the background, while the second image is a relatively low resolution reference image taken with the background more in focus than the foreground. Regions of the captured images are assigned as foreground or background. In accordance with the assigning, one or more processed images are rendered based on the first image or the second image, or both.

Abstract: Sub-regions within a face image are identified to be enhanced by applying a localized smoothing kernel to luminance data corresponding to the sub-regions of the face image. An enhanced face image is generated including an enhanced version of the face that includes certain original pixels in combination with pixels corresponding to the one or more enhanced sub-regions of the face.

Abstract: Sub-regions within a face image are identified to be enhanced by applying a localized smoothing kernel to luminance data corresponding to the sub-regions of the face image. An enhanced face image is generated including an enhanced version of the face that includes certain original pixels in combination with pixels corresponding to the one or more enhanced sub-regions of the face.

Abstract: An image acquisition device includes a flash and optical system for capturing digital images. A face tracker identifies face regions within a series of one or more relatively low resolution reference images, and predicts face regions within a main digital image. A face analyzer determines one or more partial face regions within the one or more face regions each including at least one eye. A red-eye filter modifies an area within the main digital image indicative of a red-eye phenomenon based on an analysis of one or more partial face regions within the one or more face regions identified and predicted by the face tracker.

Abstract: A method for detecting a face in a mid-shot digital image of a person comprises capturing first and second mid-shot digital images of nominally the same scene using different capture settings such that the foreground is differently differentiated from the background in each image, and comparing the first and second images to determine the foreground region of the images. A portion of the foreground region likely to correspond to a face is estimated based upon the geometry of the foreground region.

Abstract: A method for digital image eye artifact detection and correction include identifying one or more candidate red-eye defect regions in an acquired image. For one or more candidate red-eye regions, a seed pixels and/or a region of pixels having a high intensity value in the vicinity of the candidate red-eye region is identified. The shape, roundness or other eye-related characteristic of a combined hybrid region including the candidate red-eye region and the region of high intensity pixels is analyzed. Based on the analysis of the eye-related characteristic of the combined hybrid region, it is determined whether to apply flash artifact correction, including red eye correction of the candidate red-eye region and/or correction of the region of high intensity pixels.

Abstract: Sub-regions within a face image are identified to be enhanced by applying a localized smoothing kernel to luminance data corresponding to the sub-regions of the face image. An enhanced face image is generated including an enhanced version of the face that includes certain original pixels in combination with pixels corresponding to the one or more enhanced sub-regions of the face.

Abstract: A method for providing improved foreground/background separation in a digital image of a scene is disclosed. The method comprises providing a first map comprising one or more regions provisionally defined as one of foreground or background within the digital image; and providing a subject profile corresponding to a region of interest of the digital image. The provisionally defined regions are compared with the subject profile to determine if any of the regions intersect with the profile region. The definition of one or more of the regions in the map is changed based on the comparison.

Abstract: A method for providing improved foreground/background separation in a digital image of a scene is disclosed. The method comprises providing a first map comprising one or more regions provisionally defined as one of foreground or background within the digital image; and providing a subject profile corresponding to a region of interest of the digital image. The provisionally defined regions are compared with the subject profile to determine if any of the regions intersect with the profile region. The definition of one or more of the regions in the map is changed based on the comparison.

Abstract: Sub-regions within a face image are identified to be enhanced by applying a localized smoothing kernel to luminance data corresponding to the sub-regions of the face image. An enhanced face image is generated including an enhanced version of the face that includes certain original pixels in combination with pixels corresponding to the one or more enhanced sub-regions of the face.

Abstract: A color filter enhancement method for a portable digital image acquisition device includes digitally exposing color pixels of a color sensor array for a first digital exposure duration and digitally exposing white pixels of a color sensor array for a second digital exposure time shorter than the first digital exposure duration. A color filter enhanced digital image is generated using data from both the color pixels exposed for the first digital exposure duration and the white pixels exposed for the second digital exposure duration.

Abstract: A method for red-eye detection in an acquired digital image includes acquiring a first image, and analyzing one or more partial face regions within the first image. One or more characteristics of the first image are determined. One or more corrective processes are identified including red eye correction that can be beneficially applied to the first image according to the one or more characteristics. The one or more corrective processes are applied to the first image.

Abstract: A method for red-eye detection in an acquired digital image includes acquiring a first image, and analyzing one or more partial face regions within the first image. One or more characteristics of the first image are determined. One or more corrective processes are identified including red eye correction that can be beneficially applied to the first image according to the one or more characteristics. The one or more corrective processes are applied to the first image.

Abstract: A digital image acquisition system having no photographic film comprises an apparatus for capturing digital images and a flash unit for providing illumination during image capture. The system has a portrait mode for generating an image of a foreground object against a blurred background, the portrait mode being operable to capture first, second and third images (A, B and C) of nominally the same scene. One of the first and second images (A, B) is taken with flash and the other is taken without flash, and the third image (C) is blurred compared to the first and second images. The portrait mode is further operable to determine foreground and background regions of the scene using the first and second images (A, B), and to substitute the blurred background of the third image (C) for the background of an in-focus image of the scene. In one embodiment the in-focus image is one of the first and second images. In another embodiment the in-focus image is a fourth image.

Abstract: A technique for detecting large and small non-red eye flash defects in an image is disclosed. The method comprises selecting pixels of the image which have a luminance above a threshold value and labeling neighboring selected pixels as luminous regions. A number of geometrical filters are applied to the luminous regions to remove false candidate luminous regions.

Abstract: A digital image acquisition device is for acquiring digital images including one or more preview images. A face detector analyzes the one or more preview images to ascertain information relating to candidate face regions therein. A speed-optimized filter produces a first set of candidate red-eye regions based on the candidate face region information provided by the face detector.

Abstract: A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus-specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component.

Abstract: A method for digital image eye artifact detection and correction include identifying one or more candidate red-eye defect regions in an acquired image. For one or more candidate red-eye regions, a seed pixels and/or a region of pixels having a high intensity value in the vicinity of the candidate red-eye region is identified. The shape, roundness or other eye-related characteristic of a combined hybrid region including the candidate red-eye region and the region of high intensity pixels is analyzed. Based on the analysis of the eye-related characteristic of the combined hybrid region, it is determined whether to apply flash artifact correction, including red eye correction of the candidate red-eye region and/or correction of the region of high intensity pixels.