Abstract: A technique is provided for generating sharp, well-exposed, color images from low-light images. A series of under-exposed images is acquired. A mean image is computed and a sum image is generated each based on the series of under-exposed images. Chrominance variables of pixels of the mean image are mapped to chrominance variables of pixels of the sum image. Chrominance values of pixels within the series of under-exposed images are replaced with chrominance values of the sum image. A set of sharp, well-exposed, color images is generated based on the series of under-exposed images with replaced chrominance values.

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 method and apparatus for providing image processing. For one embodiment of the invention, a digital image is acquired. One or more relatively large candidate red eye defect regions are detected in at least a portion of the image. Face detection is applied to at least a portion of the image to eliminate non-face regions and one or more relatively small candidate red eye defect regions are identified in at least a portion of the image not including the eliminated non-face regions.

Abstract: A method of tracking a face in a reference image stream using a digital image acquisition device includes acquiring a full resolution main image and an image stream of relatively low resolution reference images each including one or more face regions. One or more face regions are identified within two or more of the reference images. A relative movement is determined between the two or more reference images. A size and location are determined of the one or more face regions within each of the two or more reference images. Concentrated face detection is applied to at least a portion of the full resolution main image in a predicted location for candidate face regions having a predicted size as a function of the determined relative movement and the size and location of the one or more face regions within the reference images, to provide a set of candidate face regions for the main image.

Abstract: Classifier chains are used to determine quickly and accurately if a window or sub-window of an image contains a right face, a left face, a full face, or does not contain a face. After acquiring a digital image, an integral image is calculated based on the acquired digital image. Left-face classifiers are applied to the integral image to determine the probability that the window contains a left face. Right-face classifiers are applied to the integral image to determine the probability that the window contains a right face. If the probability of the window containing a right face and a left face are both greater than threshold values, then it is determined that the window contains a full face. Alternatively, if only one of the probabilities exceeds a threshold value, then it may be determined that the window contains only a left face or a right face.

Abstract: A method of generating one or more new spatial and chromatic variation digital images uses an original digitally-acquired image which including a face or portions of a face. A group of pixels that correspond to a face within the original digitally-acquired image is identified. A portion of the original image is selected to include the group of pixels. Values of pixels of one or more new images based on the selected portion are automatically generated, or an option to generate them is provided, in a manner which always includes the face within the one or more new images. Such method may be implemented to automatically establish the correct orientation and color balance of an image. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

Abstract: Two or more digital images of a same scene are captured with different exposure levels. Image pairs are generated that have a same exposure level from the image data of differently exposed images by adjusting the exposure of at least one of the pair of images. A ghosting map is generated based on differences between the pair of images adjusted to the same exposure. A blurred ghosting map is generated and alpha blending is performed.

Abstract: A method of generating one or more new spatial and chromatic variation digital images uses an original digitally-acquired image which including a face or portions of a face. A group of pixels that correspond to a face within the original digitally-acquired image is identified. A portion of the original image is selected to include the group of pixels. Values of pixels of one or more new images based on the selected portion are automatically generated, or an option to generate them is provided, in a manner which always includes the face within the one or more new images. Such method may be implemented to automatically establish the correct orientation and color balance of an image. Such method can be implemented as an automated method or a semi automatic method to guide users in viewing, capturing or printing of images.

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: A measure of frame-to-frame rotation is determined. A global XY alignment of a pair of image frames is performed. At least one section of each of the X and Y integral projection vectors is determined, where aligned global vectors demonstrate a significant localized difference. Based on X and Y locations of the at least one section of the X and Y integral projection vectors, location, relative velocity and/or approximate area of at least one moving object within the sequence of image frames is/are determined.

Abstract: A measure of frame-to-frame rotation is determined. A global XY alignment of a pair of frames is performed. Local XY alignments in at least two matching corner regions of the pair of images are determined after the global XY alignment. Based on differences between the local XY alignments, a global rotation is determined between the pair of frames.

Abstract: An image processing technique includes acquiring a main image of a scene and determining one or more facial regions in the main image. The facial regions are analysed to determine if any of the facial regions includes a defect. A sequence of relatively low resolution images nominally of the same scene is also acquired. One or more sets of low resolution facial regions in the sequence of low resolution images are determined and analysed for defects. Defect free facial regions of a set are combined to provide a high quality defect free facial region. At least a portion of any defective facial regions of the main image are corrected with image information from a corresponding high quality defect free facial region.

Abstract: In an embodiment, a device comprises a plurality of elements, including logical elements, wherein the elements are configured to perform the operations of: in a neighborhood of pixels surrounding and including a particular pixel, applying a filter to multiple groups of pixels in the neighborhood to generate a set of filtered values; generating, based at least in part upon the set of filtered values, one or more sets of gradient values; based at least in part upon the one or more sets of gradient values, computing a first metric for an image environment in which the particular pixel is situated; determining a second metric for the image environment in which the particular pixel is situated, wherein the second metric distinguishes between a detail environment; and based at least in part upon the first metric and the second metric, computing a gradient improvement (GI) metric for the particular pixel.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation, an input pixel from the first version of the image is forward mapped to the second version of the image to determine a set of candidate pixels that may be affected by the input pixel. Each candidate pixel is then backward mapped to the first version of the image to determine whether they are actually affected by the input pixel. For each candidate pixel that is actually affected by the input pixel, a pixel value is determined for that candidate pixel based at least in part upon the pixel value of the input pixel. By using this forward and backward mapping technique, forward interpolation can be implemented quickly and efficiently.

Abstract: Classifier chains are used to determine quickly and accurately if a window or sub-window of an image contains a right face, a left face, a full face, or does not contain a face. After acquiring a digital image, an integral image is calculated based on the acquired digital image. Left-face classifiers are applied to the integral image to determine the probability that the window contains a left face. Right-face classifiers are applied to the integral image to determine the probability that the window contains a right face. If the probability of the window containing a right face and a left face are both greater than threshold values, then it is determined that the window contains a full face. Alternatively, if only one of the probabilities exceeds a threshold value, then it may be determined that the window contains only a left face or a right face.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: Providing for a fixed focus optical system exhibiting extended depth of field is provided herein. By way of example, a compact and fast optical system that yields an asymmetric modulation transfer function (MTF) is disclosed. In some aspects, the asymmetric MTF results in extended depth of field for near field objects. Such a response can be particularly beneficial for small handheld cameras or camera modules having high resolution. According to some disclosed aspects, the resolution can be about 8 mega pixels. Additionally, the optical system can comprise four lenses in one aspect and five lenses in another, while remaining below about 5.3 mm total track length (TTL) for the respective systems. In at least one application, the disclosed optical systems can be employed for a high resolution compact camera, for instance in conjunction with an electronic computing device, communication device, display device, surveillance equipment, or the like.

Abstract: A processor-based system operating according to digitally-embedded programming instructions performs a method including identifying a group of pixels corresponding to a face region within digital image data acquired by an image acquisition device. A set of face analysis parameter values is extracted from said face region, including a faceprint associated with the face region. First and second reference faceprints are determined for a person using reference images captured respectively in predetermined face-portrait conditions and using ambient conditions. The faceprints are analyzed to determine a baseline faceprint and a range of variability from the baseline associated with the person. Results of the analyzing are stored and used in subsequent recognition of the person in a subsequent image acquired under ambient conditions.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.