Abstract: In one embodiment, there is provided a method for an Optical Character Recognition (OCR) system. The method comprises: recognizing an input character based on a plurality of classifiers, wherein each classifier generates an output by comparing the input character with a plurality of trained patterns; grouping the plurality of classifiers based on a classifier grouping criterion; and combining the output of each of the plurality of classifiers based on the grouping.

Abstract: A method, system and computer program product for detecting presence of an object in an image are disclosed. According to an embodiment, a method for detecting a presence of an object in an image comprises: receiving multiple training image samples; determining a set of adaptive features for each training image sample, the set of adaptive features matching the local structure of each training image sample; integrating the sets of adaptive features of the multiple training image samples to generate an adaptive feature pool; determining a general feature based on the adaptive feature pool; and examining the image using a classifier determined based on the general feature to detect the presence of the object.

Abstract: A background model learning system for lighting change adaptation for video surveillance is provided. The system includes a background model estimation unit that estimates a background model for a scene of interest; a foreground map construction unit that constructs a reference foreground map of the time instance; and a lighting change processing unit that revises the reference foreground map by reducing false foreground regions resulting from lighting changes. The revised foreground map is then sent back to both the background model estimation unit and the lighting change processing unit as feedbacks for model learning rate tuning in background model estimation and map integration in lighting change processing, respectively, for the next time instance.

Abstract: Provided is an apparatus and method for detecting body parts, the method including identifying a group of sub-images relevant to a body part in an image to be detected, assigning a reliability coefficient for the body part to the sub-images in the group of sub-images based on a basic vision feature of the sub-images and an extension feature of the sub-images to neighboring regions, and detecting a location of the body part by overlaying sub-images having reliability coefficients higher than a threshold value.

Abstract: Systems and methods for improving visual object recognition by analyzing query images are disclosed. In one example, a visual object recognition module may determine query images matching objects of a training corpus utilized by the module. Matched query images may be added to the training corpus as training images of a matched object to expand the recognition of the object by the module. In another example, relevant candidate image corpora from a pool of image data may be automatically selected by matching the candidate image corpora against user query images. Selected image corpora may be added to a training corpus to improve recognition coverage. In yet another example, objects unknown to a visual object recognition module may be discovered by clustering query images. Clusters of similar query images may be annotated and added into a training corpus to improve recognition coverage.

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 computerized rating tool is described that assists a user in efficiently and consistently assigning expert ratings (i.e., labels) to a large collection of training images representing samples of a given product. The rating tool provides mechanisms for visualizing the training images in an intuitive and configurable fashion, including clustering and ordering the training images. In some embodiments, the rating tool provides an easy-to-use interface for exploring multiple types of defects represented in the data and efficiently assigning expert ratings. In other embodiments, the computer automatically assigns ratings (i.e., labels) to the individual clusters containing the large collection of digital images representing the samples.

Abstract: An information processing apparatus includes a feature amount extraction unit extracting a feature amount of each frame of an image, a maximum likelihood state series estimation unit estimating maximum likelihood state series using the feature amount, a highlight label generation unit generating highlight label series with respect to the attention detector learning content, and a learning unit learning the highlight detector that is the state transition probability model using learning label series that is a pair of the maximum likelihood state series obtained from the attention detector learning content and the highlight label series.

Abstract: A white list inside or outside determining apparatus includes: a first feature data extracting unit which extracts first feature data from an image by using a first transformation formula created based on preliminary learning images; a second feature data extracting unit which extracts second feature data from an image by using a second transformation formula created from the preliminary learning images and application learning images; a first matching unit which performs matching between a registration image and a collation image by using the first transformation formula; and a second matching unit which performs matching between a registration image and a collation image by using the second transformation formula. Weights of a matching result of the first matching unit and a matching result of the second matching unit are changed according to the number of preliminary learning images and the number of application learning images.

Abstract: Embodiments of methods, apparatuses, devices and systems associated with one or more representative images are disclosed.

Abstract: An image processing device includes: an image probability model generation unit calculating a feature amount in units of local regions as division regions of a captured image of an imaging apparatus and generating an image probability model configured by the calculated feature amount, the image probability model indicating the generation probability of each noiseless pixel value; a memory storing a noise probability model generated from imaging element-dependent noise characteristic information, the noise probability model indicating the conditional probability of a given noised pixel value being generated in a case where a given noiseless pixel value is generated; and a Bayesian estimation unit generating a noise reduced image in which the noise of the captured image is reduced through a Bayesian estimation process in which the image probability model and the noise probability model are applied.

Abstract: Systems and methods for metric learning include iteratively determining feature groups of images based on its derivative norm. Corresponding metrics of the feature groups are learned by gradient descent based on an expected loss. The corresponding metrics are combined to provide an intermediate metric matrix as a sparse representation of the images. A loss function of all metric parameters corresponding to features of the intermediate metric matrix are optimized, using a processor, to learn a final metric matrix. Eigenvalues of the final metric matrix are projected onto a simplex.

Abstract: A method Pan-sharpens a single panchromatic (Pan) image and a single multispectral (MS) image. A wavelet transform is applied to the Pan image and the MS image to obtain a wavelet transformed Pan image and a wavelet transformed MS image. Features, in the form of vectors, are extracted from the wavelet transformed Pan image and the wavelet transformed MS image. The features are separated into features without missing values and features with missing values. A dictionary is learned from features without missing values and used to predict the values for the features with the missing values. After the predicting, the features of the low frequency wavelet coefficients and the high frequency coefficients to form a fused wavelet coefficient map, and an inverse wavelet transform is applied to the fused wavelet coefficient map to obtain a fused MS image.

Abstract: The present invention is to provide a head detecting method for detecting the head in an image correctly at high speed.

Abstract: Architecture for comparing images by building an initial map from the average color and an inserted blackened area. Accordingly, a map can be built that is more information-rich and smaller, thereby making the system more efficient. The architecture employs a Kohonen neural network (or self-organizing map (SOM)) by guiding the learning of the SOM using characteristics of the images such as average color and a central area. A strong component of the average color of the image and the central area at the approximate center of the image are added to the uninitialized SOM, which allows related colors to converge toward the central area of the image. When input, the SOM organizes the color content of the image on a map, which can be used to compare the image with other images.

Abstract: A method, apparatus and computer program product are provided to process a compressed image, such as a normalized DICOM image. A method may receive a compressed image having pixels that each have by a pixel value having a predefined number of bits in a grayscale format. The method may decompress the image by mapping the pixel value into two channels of a multi-channel output buffer, such as a red-green-blue (RGB) output buffer, such that each channel has fewer than the predefined number of bits. The method may render a scene of the image based upon camera coordinates and, for a rendered pixel, sample a plurality of RGB values, convert the plurality of RGB values to the grayscale format having the predefined number of bits, perform an interpolation of the RGB values following conversion to the grayscale format and perform a window level operation on the result of the interpolation.

Abstract: Methods for identifying and quantifying recurrent and deterministic patterns in digital images are provided. The methods, which are based on Recurrence Quantification Analysis (RQA), generate similarity or dissimilarity distance matrices for digital images that may be used to calculate a variety of quantitative characteristics for the images. Also provided are methods for identifying and imaging spatial distributions of time variable signals generated from dynamic systems. In these methods a time variable signal is recorded for a plurality of area or volume elements into which a dynamic system has been sectioned and RQA is used to calculate one or more RQA variables for each of the area or volume elements, which may then be used to generate a two or three dimensional image displaying the spatial distribution of the RQA variables across the system.

Abstract: An image-capturing apparatus for capturing an image by using a solid-state image-capturing device may include a face detector configured to detect a face of a human being on the basis of an image signal in a period until an image signal obtained by image capturing is recorded on a recording medium; an expression evaluation section configured to evaluate the expression of the detected face and to compute an expression evaluation value indicating the degree to which the detected face is close to a specific expression in relation to expressions other than the specific expression; and a notification section configured to notify notification information corresponding to the computed expression evaluation value to an image-captured person.

Abstract: An image-capturing apparatus for capturing an image by using a solid-state image-capturing device may include a face detector configured to detect a face of a human being on the basis of an image signal in a period until an image signal obtained by image capturing is recorded on a recording medium; an expression evaluation section configured to evaluate the expression of the detected face and to compute an expression evaluation value indicating the degree to which the detected face is close to a specific expression in relation to expressions other than the specific expression; and a notification section configured to notify notification information corresponding to the computed expression evaluation value to an image-captured person.

Abstract: An image-capturing apparatus for capturing an image by using a solid-state image-capturing device may include a face detector configured to detect a face of a human being on the basis of an image signal in a period until an image signal obtained by image capturing is recorded on a recording medium; an expression evaluation section configured to evaluate the expression of the detected face and to compute an expression evaluation value indicating the degree to which the detected face is close to a specific expression in relation to expressions other than the specific expression; and a notification section configured to notify notification information corresponding to the computed expression evaluation value to an image-captured person.

Abstract: Methods and systems for automatic detection of landmarks in digital images and annotation of those images are disclosed. A method for detecting and annotating landmarks in digital images includes the steps of automatically assigning a tag descriptive of a landmark to one or more images in a plurality of text-associated digital images to generate a set of landmark-tagged images, learning an appearance model for the landmark from the set of landmark-tagged images, and detecting the landmark in a new digital image using the appearance model. The method can also include a step of annotating the new image with the tag descriptive of the landmark.

Abstract: A tire dressing system includes an imaging device that captures an image of a vehicle tire or portion thereof and at least one application device that applies tire dressing to the tire based on the captured image. The system determines one or more tire parameters based on the captured image. The tire dressing system configures the at least one application device based on the tire parameter(s) such that the application device substantially restricts application of tire dressing to within the boundaries of the tire sidewalls. By determining the tire parameters, the tire dressing system may apply the tire dressing to the tire sidewalls while reducing or minimizing application of tire dressing to the body or rims of the vehicle.

Abstract: Systems and methods for implementing a multi-label image recognition framework for classifying digital images are provided. The provided multi-label image recognition framework utilizes an iterative, multiple analysis path approach to model training and image classification tasks. A first iteration of the multi-label image recognition framework generates confidence maps for each label, which are shared by the multiple analysis paths to update the confidence maps in subsequent iterations. The provided multi-label image recognition framework permits model training and image classification tasks to be performed more accurately than conventional single-label image recognition frameworks.

Abstract: The present invention is a method and system to provide a face-based automatic ethnicity recognition system that utilizes ethnicity-sensitive image features and probabilistic graphical models to represent ethnic classes. The ethnicity-sensitive image features are derived from groups of image features so that each grouping of the image features contributes to more accurate recognition of the ethnic class. The ethnicity-sensitive image features can be derived from image filters that are matched to different colors, sizes, and shapes of facial features—such as eyes, mouth, or complexion. The ethnicity-sensitive image features serve as observable quantities in the ethnic class-dependent probabilistic graphical models, where each probabilistic graphical model represents one ethnic class. A given input facial image is corrected for pose and lighting, and ethnicity-sensitive image features are extracted.

Abstract: An apparatus, system, and method for image processing are disclosed, each of which obtains a mark from image data, detects additional information in the mark, determines whether the mark is detected in the mark to generate a determination result, and controls processing performed by an image processing apparatus with respect to the image data based on the determination result.

Abstract: A correlation value calculation circuit calculates respective correlation values of each pixel for color image or for gray image in four directions. A selection circuit determines respective correlation values (Cv, Ch, CdA and CdB) in the four directions on the basis of a chroma evaluation value. A first correlation judgment circuit determines a correlation direction of a specified pixel from the correlation values (Cv, Ch, CdA and CdB). A first interpolation circuit performs color interpolation on the basis of the determined correlation direction. A color-interpolated image is enlarged by an enlargement circuit. A correlation direction interpolation circuit performs enlargement and interpolation of the correlation direction determined by the first correlation judgment circuit. A filter performs a filtering process for each of pixels of the enlarged image by using the interpolated correlation direction for enlargement.

Abstract: A test pattern containing plurality of patches recorded using small dot patterns and large dot patterns is printed. The dot sizes arranged in the patches are different between patches. Then the test patterns are read. The detection rate and average density of additional information embedded in each large dot pattern are obtained. A large dot pattern whose detection rate and average density fall within a reference range and are closest to ideal values is determined. The average density of each small dot pattern is obtained. A small dot pattern whose average density falls within a reference range and is closest to the average density of the determined large dot pattern is determined. A copy-forgery-inhibited pattern image is generated using these dot patterns and combined with a print target image.

Abstract: A novel, linear modeling method to model a face recognition algorithm based on the match scores produced by the algorithm. Starting with a distance matrix representing the pair-wise match scores between face images, an iterative stress minimization algorithm is used to obtain an embedding of the distance matrix in a low-dimensional space. A linear transformation used to project new face images into the model space is divided into two sub-transformations: a rigid transformation of face images obtained through principal component analysis of face images and a non-rigid transformation responsible for preserving pair-wise distance relationships between face images. Also provided is a linear indexing method using the linear modeling method to perform the binning or algorithm-specific indexing task with little overhead.

Abstract: An intrusion prevention/detection system filter (IPS filter) performance evaluation is provided. The performance evaluation is performed at both the security center and at the customer sites to derive a base confidence score and local confidence scores. Existence of new vulnerability is disclosed and its attributes are used in the generation of new IPS filter or updates. The generated IPS filter is first tested to determine its base confidence score from test confidence attributes prior to deploying it to a customer site. A deep security manager and deep security agent, at the customer site, collect local confidence attributes that are used for determining the local confidence score. The local confidence score and the base confidence score are aggregated to form a global confidence score. The local and global confidence scores are then compared to deployment thresholds to determine whether the IPS filter should be deployed in prevention or detection mode or sent back to the security center for improvement.

Abstract: A plurality of images inputted in an image signal input portion are divided into a plurality of regions by an image dividing portion, and a feature value in each of the plurality of regions is calculated by a feature value calculation portion and divided into a plurality of subsets by a subset generation portion. On the other hand, a cluster classifying portion classifies a plurality of clusters generated in a feature space into any one of a plurality of classes on the basis of the feature value and occurrence frequency of the feature value. And a classification criterion calculation portion calculates a criterion of classification for classifying images included in one subset on the basis of a distribution state of the feature value in the feature space of each of the images included in the one subset.

Abstract: A process for performing white balancing of an image is performed by subdividing an image into a plurality of subframes, and then analyzing each subframe to determine if that subframe is predominantly monochromatic other than gray. If so, that subframe is excluded from the computation of the gain adjustments in the white balancing operation. As a result, the white balance process is performed using only the multicolored and/or gray subframes, thus allowing the overall white-balance of the image to be shifted only when a change in the color average is due to a change in the spectra of illumination, and not a presence of large monochromatic areas in the image.

Abstract: A method for classification of samples comprising providing a trained statistical model based upon a set of initial samples. Receiving a set of first samples and training a first statistical model base upon the first set of samples, where the first statistical model is of the same class as the trained statistical model. Receiving a set of second samples and training a second statistical model base upon the second set of samples, where the second statistical model is of the same class as the trained statistical model. The trained statistical model, the first statistical model, and the second statistical model, being independent of each other and collectively used to classify another sample.

Abstract: An image-based pattern recognizer and a method and apparatus for making such a pattern recognizer are disclosed. By employing positional coding, the meaning of any feature present in an image can be defined implicitly in space. The pattern recognizer can be a neural network including a plurality of stages of observers. The observers are configured to cooperate to identify the presence of features in the input image and to recognize a pattern in the input image based on the features. Each of the observers includes a plurality of neurons. The input image includes a plurality of units, and each of the observers is configured to generate a separate output set that includes zero or more coordinates of such units.

Abstract: A method is provided for automatically identifying image views in a three-dimensional dataset comprises accessing with a processor a three-dimensional dataset comprising a plurality of image frames and fitting with the processor at least one deformable model to at least one structure within each of the image frames. The method further comprises identifying with the processor at least one feature point within each of the image frames based on the at least one deformable model and displaying on a display at least one image view based on the at least one feature point.

Abstract: An image-capturing apparatus for capturing an image by using a solid-state image-capturing device may include a face detector configured to detect a face of a human being on the basis of an image signal in a period until an image signal obtained by image capturing is recorded on a recording medium; an expression evaluation section configured to evaluate the expression of the detected face and to compute an expression evaluation value indicating the degree to which the detected face is close to a specific expression in relation to expressions other than the specific expression; and a notification section configured to notify notification information corresponding to the computed expression evaluation value to an image-captured person.

Abstract: The invention relates to a method and apparatus for characterizing the imperfections of the skin. The apparatus comprises: a) a digital camera allowing the taking of at least one digital image of at least one determined skin zone, the said image being defined by a multiplicity of pixels, that is transmitted to a digital image processing device; b) means of splitting the digital image into three color planes: red, green, blue, termed R, G, B, with the aid of the said image processing device; c) means of extracting a single of these planes; d) means of calculating at least one of the following parameters on the basis of this plane: the mean grey level of the image; the surface area of the imperfections of the skin of the said image, some of which are related to aging, such as wrinkles, lines, spots, dark circles, pigmentary imperfections, slackening or loosening zones; the variance of the grey levels over the set of pixels of the image as parameter representative of the homogeneity of the skin.

Abstract: An image-capturing apparatus for capturing an image by using a solid-state image-capturing device may include a face detector configured to detect a face of a human being on the basis of an image signal in a period until an image signal obtained by image capturing is recorded on a recording medium; an expression evaluation section configured to evaluate the expression of the detected face and to compute an expression evaluation value indicating the degree to which the detected face is close to a specific expression in relation to expressions other than the specific expression; and a notification section configured to notify notification information corresponding to the computed expression evaluation value to an image-captured person.

Abstract: An input image is received, represented by a matrix D having a first number of dimensions. Each of the first number of dimensions may represent or correspond to a portion of the image. A metric objective may be identified. A dimensional reduction on the matrix D may then be performed that optimize the metric objective, so that a matrix d of a second number of dimensions is identified to represent the input image, where the second number of dimensions is less than the first number of dimensions.

Abstract: An image-based pattern recognizer and a method and apparatus for making such a pattern recognizer are disclosed. By employing positional coding, the meaning of any feature present in an image can be defined implicitly in space. The pattern recognizer can be a neural network including a plurality of stages of observers. The observers are configured to cooperate to identify the presence of features in the input image and to recognize a pattern in the input image based on the features. Each of the observers includes a plurality of neurons. The input image includes a plurality of units, and each of the observers is configured to generate a separate output set that includes zero or more coordinates of such units.

Abstract: Input MRI brain images are positioned so as to correct a spatial deviation, gray matter tissues are extracted from these images to effect a first image smoothing, the thus-obtained images are subjected to anatomical standardization, a second image smoothing is effected, the gray level is corrected, brain images after correction are statistically compared with MRI brain images of normal cases, thereby providing the diagnosis result. In this instance, the brain images are automatically checked for input images regarding the resolution dot density and the like, the result of gray matter tissue extraction and the result of anatomical standardization, by which specifications of input images and the like can be confirmed objectively and automatically to make a diagnosis automatically by image processing. Further, an ROI-based analysis is made to provide the analysis result as the diagnosis result.

Abstract: Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the detected foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

Abstract: This disclosure describes various exemplary method and computer program products for transductive multi-label classification in detecting video concepts for information retrieval. This disclosure describes utilizing a hidden Markov random field formulation to detect labels for concepts in a video content and modeling a multi-label interdependence between the labels by a pairwise Markov random field. The process groups the labels into several parts to speed up a labeling inference and calculates a conditional probability score for the labels, the conditional probability scores are ordered for ranking in a video retrieval evaluation.

Abstract: A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Said features may include the 3-jet of said pixels.

Abstract: Systems and methods for automated pattern recognition and object detection. The method can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The system includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data.

Abstract: A method of face categorization and annotation of a face image library includes automatically cropping a face within an acquired digital image or removing one or more non-facial items from the digital image, or both, and thereby generating a full-size face image. The full-size face image is stored with other indicia identifying a person corresponding to the face in a face image library of an embedded device such as a mobile camera phone or other handheld camera device.

Abstract: Image enhancement techniques are described to enhance an image in accordance with a set of training images. In an implementation, an image color tone map is generated for a facial region included in an image. The image color tone map may be normalized to a color tone map for a set of training images so that the image color tone map matches the map for the training images. The normalized color tone map may be applied to the image to enhance the in-question image. In further implementations, the procedure may be updated when the average color intensity in non-facial regions differs from an accumulated mean by a threshold amount.

Abstract: A method of compression of videotelephony images characterized by: creating (10) a learning base containing images; centering the learning base about zero; determining component images by principal component analysis (12); and keeping a number of significant principal components (14).

Abstract: An information processing apparatus includes a statistical analysis processing device configured to perform statistical analysis processing, an acquisition device configured to acquire samples to be processed by the statistical analysis processing device, a classification device configured to classify the samples acquired by the acquisition device, and a selection device configured to select from the samples classified by the classification device learning samples to be used in the statistical analysis processing by the statistical analysis processing device.

Abstract: An image-based pattern recognizer and a method and apparatus for making such a pattern recognizer are disclosed. By employing positional coding, the meaning of any feature present in an image can be defined implicitly in space. The pattern recognizer can be a neural network including a plurality of stages of observers. The observers are configured to cooperate to identify the presence of features in the input image and to recognize a pattern in the input image based on the features. Each of the observers includes a plurality of neurons. The input image includes a plurality of units, and each of the observers is configured to generate a separate output set that includes zero or more coordinates of such units.

Abstract: A method for detecting and localizing multiple anatomical landmarks in medical images, including: receiving an input requesting identification of a plurality of anatomical landmarks in a medical image; applying a multi-landmark detector to the medical image to identify a plurality of candidate locations for each of the anatomical landmarks; for each of the anatomical landmarks, applying a landmark-specific detector to each of its candidate locations, wherein the landmark-specific detector assigns a score to each of the candidate locations, and wherein candidate locations having a score below a predetermined threshold are removed; applying spatial statistics to groups of the remaining candidate locations to determine, for each of the anatomical landmarks, the candidate location that most accurately identifies the anatomical landmark; and for each of the anatomical landmarks, outputting the candidate location that most accurately identifies the anatomical landmark.