Abstract: A method is provided for automatically providing a digital image rating of photo retouching. The method includes the step of receiving at a computer, including a processor, a first set of pixel data of an original image and a second set of pixel data of a retouched image. The method also includes using the processor to determine a plurality of geometric statistics and a plurality of photometric statistics from the first and second sets of pixel data. The method further includes the step of using the processor to quantify a rating of the retouched image based upon the geometric statistics and photometric statistics to indicate deviation of the retouched image from the original image. A system is also provided to perform the steps.

Abstract: Evaluating an image is disclosed. A plurality of attributes of the image is analyzed. A determination is made that a portion of the attributes of the image imperfectly matches a reference attribute signature corresponding to a device. It is distinguished whether the imperfect match likely corresponds to a modification of the image.

Abstract: Evaluating an image is disclosed. A plurality of attributes of the image is analyzed. A determination is made that a portion of the attributes of the image imperfectly matches a reference attribute signature corresponding to a device. It is distinguished whether the imperfect match likely corresponds to a modification of the image.

Abstract: Detecting a consistency of image components is disclosed. A first constraint associated with a possible convergence area of a first component of an image is received. A second constraint associated with a possible convergence area of a second component of the image is received. The first constraint and the second constraint are used to provide a result associated with whether the first component and the second component are consistent.

Abstract: A method is provided for automatically providing a digital image rating of photo retouching. The method includes the step of receiving at a computer, including a processor, a first set of pixel data of an original image and a second set of pixel data of a retouched image. The method also includes using the processor to determine a plurality of geometric statistics and a plurality of photometric statistics from the first and second sets of pixel data. The method further includes the step of using the processor to quantify a rating of the retouched image based upon the geometric statistics and photometric statistics to indicate deviation of the retouched image from the original image. A system is also provided to perform the steps.

Abstract: A system and method for detecting dust (18) on an image sensor (20) from a single captured image (14) of an actual scene (12) includes a control system (22) that evaluates at least one of a hue channel (466), a value channel (470), and a saturation channel (468) of the captured image (14) to determine if there is dust (18) on the image sensor (20). For example, the control system (22) can evaluate the hue channel (466) and the value channel (470) of the captured image (14) to determine if there is dust (18) on the image sensor (20). With information from the hue channel (466) and the value channel (470), the control system (22) can compute a computed probability (572) of dust (18) for a plurality of pixels (362) of the captured image (14).

Abstract: The present invention is directed to a method for detecting or predicting (302, 602) whether a test image is blurred. In one embodiment, the method includes extracting a training statistical signature (366) that is based on a plurality of data features (362, 364) from a training image set (14, 16), the training image set (14, 16) including a sharp image (14) and a blurry image (16); training a classifier (368) to discriminate between the sharp image (14) and the blurry image (16) based on the training statistical signature; and applying (302, 602) the trained classifier to a test image that is not included in the training image set (14, 16) to predict whether the test image is sharp (18) or blurry (20). The step of extracting can include measuring one or more statistical moments (576, 776) for various levels (L0-L5), estimating a covariance (577, 777) between adjacent levels (L0-L5), and/or extracting various metadata features (364, 664) from the images (14, 16).

Abstract: The present invention is directed to a method for detecting or predicting (302, 602) whether a test image is blurred. In one embodiment, the method includes extracting a training statistical signature (366) that is based on a plurality of data features (362, 364) from a training image set (14, 16), the training image set (14, 16) including a sharp image (14) and a blurry image (16); training a classifier (368) to discriminate between the sharp image (14) and the blurry image (16) based on the training statistical signature; and applying (302, 602) the trained classifier to a test image that is not included in the training image set (14, 16) to predict whether the test image is sharp (18) or blurry (20). The step of extracting can include measuring one or more statistical moments (576, 776) for various levels (L0-L5), estimating a covariance (577, 777) between adjacent levels (L0-L5), and/or extracting various metadata features (364, 664) from the images (14, 16).

Abstract: A system and method for detecting dust (18) on an image sensor (20) from a single captured image (14) of an actual scene (12) includes a control system (22) that evaluates at least one of a hue channel (466), a value channel (470), and a saturation channel (468) of the captured image (14) to determine if there is dust (18) on the image sensor (20). For example, the control system (22) can evaluate the hue channel (466) and the value channel (470) of the captured image (14) to determine if there is dust (18) on the image sensor (20). With information from the hue channel (466) and the value channel (470), the control system (22) can compute a computed probability (572) of dust (18) for a plurality of pixels (362) of the captured image (14).

Abstract: A system, method and computer program product is provided for analyzing spectral images of a micrcirculatory system to measure the volume and concentration of a blood vessel. The images are analyzed to identify vessel structure, measure the light absorption and develop a contrast gradient plus (KGP) estimate. The KGP estimate is used to predict blood characteristics, such as hemoglobin concentration and hematocrit. The KGP is estimated in three distinct phases. First, the images are screened to measure a mean image intensity and motion blur. Second, each image is analyzed to identify background curvate, and create vessel, background and diameter masks. To identify background curvate, the images are analyzed to detect shadows caused by larger blood vessels in the background image. The diameter and area of the vessels are also calculated.

Abstract: A single lens range sensor comprises a camera 27 having a pair of plano-convex lenses 28a and 28b, an optical mask 30, an imaging sensor 32, a digitizer 34 and a computer 36. The optical mask 30 may be implemented with a liquid crystal array having spatially varying opacity which can be switched (e.g., by the computer 36) between masks derived from a differentiable mask function and its derivative. Alternatively, the mask 30 may be composed of two printed masks which are mechanically switched to obtain two images based on the differentiable mask function and its derivative. The imaging sensor 32 may be implemented with a CCD array. The invention avoids the correspondence problems of the two camera and moving camera approaches, and is simple to calibrate.