Hewlett-packard Development Company, L.p.

Kabushiki Kaisha Toshiba

Toshiba Tec Kabushiki Kaisha

Hewlett-packard Company

Other

Jonathan Yen

Daniel R Tretter

Huitao Luo

Doron Shaked

William C Kress

Suk Hwan Lim

Peng Wu

Avraham Levy

Avi Levy

Chit Wei Saw

Qian Lin

Niranjan Damera-Venkata

Pere Obrador

Tony T Quach

Yihong Xu

Debargha Mukherjee

Ron Kimmel

Ramin Samadani

Izhak Baharav

Jian Fan

Ping Wah Wong

Renato Keshet

Clayton Atkins

Cormac Herley

Mei Chen

Terry P Mahoney

Angela Kay Hanson

Gerald Q Maguire, Jr

Truong-Thao Nguyen

Luca Chiarabini

Robert Poe

Niaz R Pavel

Timothy Harrison

Systems and methods of detecting and correcting redeye in an image are described. In one aspect, pixels of the input image are segmented based on projections of color values of the pixels onto two-dimensional thresholding planes. Candidate redeye pixel areas are identified in the input image based on the segmented pixels of the input image.

Detecting and correcting redeye in an image

Systems and methods for backlit scene detection are described. The method includes performing a pre-test on an electronic image to determine that the electronic image does not include a disqualifying characteristic. Next, the method includes performing a type test on the electronic image to determine an image type of the electronic image from a first set of predetermined backlit image types. Next the method includes performing a post test on the electronic image to determine that the image darkness density does not exceed a predetermined density. Finally, the method includes performing a second post test on the electronic image to determine that the electronic image is one of a second set of predetermined backlit image types or that the edge percentage of the electronic image exceeds a predetermined percentage.

Backlit scene type detection

Image tone adjustment systems and methods are disclosed. The system of the present invention includes an input to accept an input image. A curve generator is also disclosed that is used to generate a tone reproduction curve including a contrast stretch of the input image, a white stretch of the input image and a black stretch of the input image. Backlit correction is also applied by the tone adjustment corrector to thereby generate a tone adjusted image.

Digital image tone adjustment

A digital image search and retrieval system includes a query parser and a response composer. The query parser is configured to parse a query obtained from a client, the query including at least one metadata criterion, search a metadata database for one or more digital images satisfying the metadata criterion, and search a digital image database for the one or more digital images. The response composer is configured to compose a response including the one or more digital images from the digital image database and their corresponding metadata from the metadata database, and enable the client to render the response in accordance with one or more user preferences.

Digital image search and retrieval system

The subject application is directed to a system and method for detecting backlit images. Encoded color image data is first received into a computer having a processor and associated data storage. Histogram data is then calculated from the received encoded color image data, and a mid-tone range in normalized histogram data is then detected. A zone of normalized pixel counts within the mid-tone range is then selected. Data representing an entry point and an exit point of the normalized histogram data relative to the selected zone is then generated. A plateau area is detected in the histogram data in the selected zone between the entry point and the exit point. Thereafter, a backlit image detection signal is generated indicating whether a backlit image portion is present in the color image data according to the plateau detection.

System and method for backlit image detection

A photo book system and method with associated multimedia is disclosed. One embodiment of the present invention provides a photo book including an image and an electronically readable code associated with the image. The electronically readable code is used for retrieving a multimedia file that corresponds to the image.

Photo book system and method having retrievable multimedia using an electronically readable code

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.

System and method for validation of face detection in electronic images

Systems and methods of classifying pixels into a nonhuman animal integument class are described. In one aspect, values of features in a discriminant space are determined for respective pixels of the input image. The pixels are classified into a nonhuman animal integument class based on the respective feature values and a mapping that segments the discriminant space into one or more regions of feature values representing pixels associated with a nonhuman animal integument characteristic and one or more regions of feature values representing pixels unassociated with the nonhuman animal integument characteristic.

Nonhuman animal integument pixel classification

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.

System and method for image fog scene detection

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.

System and method for image white balance adjustment

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.

System and method for image facial area detection employing skin tones

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.

Image management

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.

Method and system for determining an indication of focus of an image

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.

Detecting and correcting peteye

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.

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San Jose, CA, United States of America

Jonathan Yen