Abstract: The subject application is directed to a system and method for image enhancement. Image data is received that contains a rectangular array of pixels encoded in a multidimensional color space. The image data is scaled to a scaled rectangular array having a smaller number of pixels such that both a selected rectangular dimension and a complimentary dimension are scaled to preselected dimension values. Region-of-interest data is received for an isolated region in the image data. A mask matrix, having a rectangular array of elements corresponding to pixels of the scaled rectangular array, is stored. The mask matrix, populated using region-of-interest data, has first and second values associated with each entry, with the first value, but not the second value, corresponding to a region of interest in the scaled rectangular array. Received image data is adjusted according to image data values based on pixel values associated with first value elements.

Abstract: The subject application is directed to a system and method for backlit face image correction. Image data is first received that includes at least one facial region that defines an image human face. At least one facial region is detected via a processor that operates in accordance with software and an associated data storage. Skin tone characteristics of the at least one facial region are then detected. Pixel count data and histogram data are then calculated from the received image data. A plateau is then detected in a function of the pixel count data relative to the histogram data. A correction factor, based upon a property of the detected plateau, is then calculated. Pixel values of the at least one facial region are adjusted in accordance with the calculated correction factor. Thereafter, a corrected image is output that includes adjusted pixel values corresponding to at least one facial region.

Abstract: Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Systems and methods of detecting and correcting peteye are described. In one aspect a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values.

Abstract: There is disclosed a method of processing a digital image, which includes assigning an image quality classification to the image on the basis of an analysis of at least a portion of the image to detect whether one or more defects exist in the image.

Abstract: A method for determining an indication of how well-focused an image is. An image is divided into a plurality of blocks. At least one local measure is determined for at least one block of the plurality of blocks. At least one global figure-of-merit is determined for the image based on the local measure. An indication of focus is determined based on the global figure-of-merit.

Abstract: The subject application is directed to a system and method for detecting facial areas from an image having a relatively light background. Image data is received including at least one candidate facial region. A candidate facial region is isolated from the image data as is a subportion of an interior of the candidate facial region. A size of the subportion is compared relative to a preselected threshold size value, a luminance value of the subportion is compared with a preselected darkness threshold value, and a facial recognition signal is generated corresponding to a detected facial region in accordance with an output of the size comparison and the luminance comparison.

Abstract: The subject application is directed to a system and method for facial tone indexing. Image data comprised of a plurality of pixels encoded in at least a three dimensional component space is received and at least one candidate facial region is isolated in the received image data. The received image data is translated into a Lightness, Chroma, and Hue color space. Histogram data corresponding to the Hue of pixels is calculated in the at least one candidate facial region by discarding the low-chroma pixels first. The Hue index on which the Hue histogram peaks is identified as the Facial Tone Index. The Facial Tone Index indicates where the Hue concentration is and therefore is a single number representing the flesh tone of a face.

Abstract: The subject application is directed to a system and method for image facial detection employing skin tones. Image data comprised of a plurality of pixels encoded in at least a three dimensional component space is received and sub-sampled region data is generated from the received image data. A percentage of pixels having a low chroma value below a threshold value is then calculated and tested against a predetermined percentage threshold value. Each pixel is then classified in accordance with a skin tone model. Skin tone map data is then generated based on classification and a skin tone mask is output form the map data.

Abstract: Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Systems and methods of detecting and correcting peteye are described. In one aspect a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values.

Abstract: The subject application is directed to backlit image adjustment. First, image data is received that includes an image portion defined by an associated backlit region, from which a tonal curve is then isolated. At least one anchor point on the isolated tonal curve is selected based upon backlighting characteristics in the received image data. A sectional bulging operation is applied on the isolated tonal curve in accordance with the selected anchor point.

Abstract: In one aspect, a metadata-based classification result is obtained based on metadata associated with an image. A histogram-based classification result is determined based on a histogram of intensity values derived from the image. The image is classified into a scene type class based on the metadata-based classification result and the histogram-based classification result. In another aspect, a first condition is applied on a first one of metadata associated with an image corresponding to a measure of amount of light received in capturing the image to obtain a first metadata-based classification result. A second condition is applied on a second one of the metadata corresponding to a measure of brightness of the image to obtain a second metadata-based classification result. The image is classified into a scene type class based on the first and second metadata-based classification results.

Abstract: The subject application is directed to a system and method for validation of face detection in electronic images. Image data is first received along with at least one image portion that includes a possible facial depiction. Eye position data, nose position data, and mouth position data are also received. A reference point at a central location of the at least one image portion is then isolated. A width of the image portion is then isolated, and a facial region is isolated in accordance with the eye, nose, and mouth position data. The eye distance is then determined from the received eye position data. The isolated facial region data is then tested against the reference point and eye distance is tested against a width of the image portion. An output is generated corresponding to the accuracy of isolated facial region in accordance with the tests.

Abstract: The subject application is directed to a system and method for global darkness image enhancement. Image data encoded in a multidimensional color space is first acquired. Thereafter, histogram data is calculated according to the acquired image data. A ramp zone associated with the calculated histogram data is then detected. A black level associated with the acquired image data is then selectively stretched in accordance with the detected ramp zone so as to generate enhanced image data. The enhanced image data is then output to an associated data storage or an associated display.

Abstract: The subject application is directed to a system and method for brightness adjustment of electronic images. Image data including a plurality of pixels is first received, with each pixel having an associated image value. Histogram data corresponding to a histogram of at least one component value corresponding to each of the pixels is then calculated. An image correction value is then calculated according to at least one characteristic of the calculated histogram data. A determination is then made regarding the application of the calculated image correction value from the calculated histogram data. Corrected image data is thereafter generated based upon the application of the calculated image correction value to each pixel.

Abstract: An exemplary method for providing an optimized digital image comprises obtaining digital image data by a digital image capture device, based on the digital image data, automatically determining at least two setting types, and a plurality of setting values therefor, for capturing a plurality of digital images, automatically capturing the plurality of digital images using the digital image capture device, each image being captured with at least one setting value different than another image, and providing an optimized digital image based on the plurality of digital images. In one exemplary embodiment, the optimized image is selected from the plurality of digital images. In another exemplary embodiment, the optimized image is synthesized based on the plurality of digital images.

Abstract: The subject application is directed to a system and method for artistic scene image detection. First, image data is received that is encoded in a multi-dimensional color space. From the received image data, histogram data is then calculated. Dominant spike regions in the calculated histogram data are then identified. An N-sum value is then calculated from the identified dominant spike regions in the calculated histogram data. Testing of the calculated N-sum value against a predetermined threshold value then occurs. The received image data is thereafter classified as an artistic scene, a tinted artistic scene, or a sepia tone range artistic scene according to the testing results.

Abstract: The subject application is directed to a system and method for isolating near achromatic pixels of a digital image. First, image data encoded as a plurality of pixels is received, with each pixel having component values in a multi-dimensional color space. From the received image data, chroma data is extracted. Histogram data is then calculated from the extracted chroma data. An adapted threshold value is then determined based upon the calculated histogram data. Component values of the plurality of pixels are then compared to the determined adapted threshold value. Isolation of pixels as near achromatic then occurs in accordance with the output of the comparison of the adapted threshold value to the component values.

Abstract: The subject application is directed to a system and method for image fog scene detection. Electronic image data is first received and divided into image regions, with each region consisting of a plurality of pixels. Next, a comparison matrix is generated corresponding to each image region based upon a comparison of minimum intensity values associated with corresponding pixels to a threshold value. An entry of at least one of the comparison matrices is then tested for a preselected value. The received electronic image data is then identified as inclusive of a fog scene based upon the output of the test on the entry of the comparison matrix. Based upon the identification, fog scene detection data representing an identified fog scene is then generated.

Abstract: Systems and methods of detecting and correcting redeye in an image are described. In one aspect, the input image is sub-sampled to generate a thumbnail image, redeye pixel areas are detected in the thumbnail image. In another aspect, an input image having lines of pixels with original color values is processed. One or more redeye pixel areas corresponding to respective areas in the input image are detected. Each pixel in the input image corresponding to the detected redeye pixel areas is classified as a redeye pixel or a non-redeye pixel on a line-by-line basis without reference to pixels in adjacent lines. The original color values of pixels in the input image classified as redeye pixels are corrected.

Abstract: The subject application is directed to a system and method for image enhancement. Image data, encoded as a plurality of pixels in YCbCr space, is first received, with Y representing a luma component, Cb representing a blue chroma component, and Cr representing a red chroma component. A maximum Y value is selected from the received image data. HSV image data is received corresponding to the YCbCr image data, with H representing a hue component, S representing a saturation component, and V representing a brightness component. A pixel is then selected from the HSV image data having a minimum S value. The S (saturation) value and the V (brightness) value of the selected pixel are then tested against a first and a second threshold level. Thereafter, a Max-RGB algorithm is selectively applied to the RGB image data in accordance with the output of the testing.