Abstract: Process, and related apparatus, that exploits psycho-physiological aspects involved in generation and perception of handwriting for directly inferring from the trace on the paper (or any other means on which the author writes by hand) the interpretation of writing, i.e. the sequence of characters that the trace is intended to represent.

Abstract: An apparatus for determining a position of an object relative to a representation of an image to be represented includes a position pattern generator for generating a position pattern subdivided into a plurality of pattern portions, each of the pattern portions having an unambiguous bit pattern of a plurality of bit patterns, and the bit patterns being Gray-coded in a generalized manner; a combination unit for combining the position pattern with the at least one image to be represented and for providing a corresponding combined image; an optical sensor for optically sensing an image section of the combined image, being correlated with the object position; a filter for extracting at least one bit pattern corresponding to a pattern portion of the position pattern, from the image section and for providing at least one corresponding extracted pattern portion; and a determiner for determining the object position based on the extracted bit pattern.

Abstract: In the field of eye tracking, greater accuracy or resolution in monitoring movement of the eye can be gained by digitizing the eye, and tracking the movement of a landmark with fixed size and a fixed location relative to the eye's local coordinate system. The edge of the iris can be used as such a fixed landmark. Through the location and or establishment of at least a portion of the outer edge of the iris and/or an iris center point, both large and small scale eye movements, including but not limited to micro-tremors, can be traced with a higher degree of accuracy. This will aid in the diagnosis of diseases, assessing state of consciousness, and defining brainstem death.

Abstract: An image processing device includes: a face detector which detects a face region including a face of a person from an input image signal; a determination portion which determines, in the input image signal of the face region, an addition amount of a pixel value to be added to the input image signal of the face region based on a size of a difference in a pixel value between adjacent pixels; and a signal corrector which adds the addition amount to the pixel value of the input image signal of the face region.

Abstract: A server is configured to identify items of visual content; assign the identified items to clusters; assign the identified items, of a particular cluster, to layers; generate a document that includes information regarding the identified items arranged according to the clusters and the layers; and provide the document. The document may include, for the particular cluster, a first item, corresponding to one of the identified items assigned to a first layer, and second items, corresponding to multiple ones of the identified items assigned to a second layer. The size of the first item may be greater than a size of each of the second items. The first item may be presented on top of the second items, within the document, to at least partially cover the second items, where at least one of the second items is at least partially visible within the document.

Abstract: A mobile device with camera automatically captures an image of a check from a video sequence. A computing application assesses quality metrics of a frame of the video and, if acceptable, initiates capture of the check in that frame without user selection. Metrics include an aspect ratio of the check, image quality of the routing transit symbols that delineate a routing transit number on a MICR line of the check, distances between the routing transit symbols and to an edge of the check, recognition of digits of the routing transit number, checksum of the routing transit numbers, and image sharpness. Other embodiments note cropping of the check from the background of the image, properly orienting the check for viewing, and providing color coded visual feedback to users about the quality of the image frame about the check, to name a few.

Abstract: Apparatus and methods for inspecting a specimen are disclosed. An inspection tool is used at one or more operating modes to obtain images of a plurality of training regions of a specimen, and the training regions are identified as defect-free. Three or more basis training images are derived from the images of the training regions. A classifier is formed based on the three or more basis training images. The inspection system is used at the one or more operating modes to obtain images of a plurality of test regions of a specimen. Three or more basis test images are derived from to the test regions. The classifier is applied to the three or more basis test images to find defects in the test regions.

Abstract: A system and method for improved classification. A first classifier is trained using a first process running on at least one computing device using a first set of training images relating to a class of images. A set of additional images are selected using the first classifier from a source of additional images accessible to the computing device. The first set of training images and the set of additional images are merged using the computing device to create a second set of training images. A second classifier is trained using a second process running on the computing device using the second set of training images. A set of unclassified images are classified using the second classifier thereby creating a set of classified images. The first classifier and the second classifier employ different classification methods.

Abstract: A method for automatically selecting test parameters for an image inspection system using a computer, includes digitizing a reference image or a scanned printed image to determine target values, categorizing the image elements that are present, determining tolerances for the target values based on the categorization, calculating the inspection sensitivity based on target values and the respective tolerances thereof, setting the parameters of the image inspection system based on the inspection sensitivity, and configuring the image inspection using these parameters. An image inspection system for implementing the method is also provided.

Abstract: The invention relates to a method and an apparatus for evaluating results of gaze detection, wherein these results are present or are obtained in the form of information which defines, for each of a multiplicity of successive times, a viewing direction detected at this time and a focal point identified thereby in a scene image assigned to this time.

Abstract: The present disclosure relates to a method for analyzing tissue specimens. In one implementation the method involves obtaining a tissue sample and exposing the sample to one or more fluorophores as contrast agents to enhance contrast of subcellular compartments of the tissue sample. The tissue sample is illuminated by an ultraviolet (UV) light having a wavelength between about 200 nm to about 400 nm, with the wavelength being selected to result in penetration to only a specified depth below a surface of the tissue sample. Inter-image operations between images acquired under different imaging parameters allow for improvement of the image quality via removal of unwanted image components. A microscope may be used to image the tissue sample and provide the image to an image acquisition system that makes use of a camera. The image acquisition system may create a corresponding image that is transmitted to a display system for processing and display.

Abstract: A method to automatically classify video content retrieved over the Internet and, in particular, to determine whether a candidate video is an instance of a source video. The method begins by sampling static image frames from each of the candidate video and the source video. Based on the sampled static image frames, a set of static image frames in the candidate video that match a corresponding set of static image frames in the source video are then identified. Metadata associated with the sets of matching static image frames are then compared to determine how closely a temporal order of the set of static image frames in the candidate video matches the temporal order of the set of static image frames in the source video. Based on a measure of temporal order match, a classification is then applied to the candidate video to complete the method.

Abstract: An authentication apparatus includes a prism and an imaging unit. The prism comprises: a contact surface, an imaging surface, a first reflection surface opposed to the imaging surface, contacted with the contact surface to make an angle to be lower than an optimum angle to an incident light from a concave portion of the living body, and to reflect an incident light from a convex portion of the living body on the imaging surface and a second reflection surface opposed to the imaging surface, contacted with the first reflection surface, and to form a reflection body that reflects the incident light from the concave portion of the living body and the incident light from the convex portion of the living body on the imaging surface. The imaging unit images the light which is reflected by the first reflection surface and the light, which is reflected by the second reflection surface.

Abstract: An image conversion apparatus extracts from among pixel rows parallel to an object pixel row, a first pixel row overlapping an aperture shape; and further extracts a second pixel row having the number of pixels successively overlapping the aperture shape equal to or greater than that of the first pixel row. The image conversion apparatus calculates a value of a second pixel in the case of converting an original image, by referring to a storage unit that stores a value of a first pixel in the case of converting the original image. For example, the image conversion apparatus defines an average of the value of the first pixel in the case of converting the original image, a value of a diffusion source pixel (IN[5][2]), and a diffusion source pixel (IN[5][3]) as the value of the second pixel in the case of converting the original image.

Abstract: A method for biometrical identification of a user includes receiving, as an input, biometrical properties of a user, and encoding the biometrical properties. The encoded biometrical properties are checked with corresponding provided biometrical user information, Based on a result of the check, the user is identified and a response of the identification is outputted. Prior to encoding, the biometrical properties are encrypted, The encoded biometrical properties are provided as a biometrical challenge. The biometrical challenge is decrypted for performing the check. A verification of the response is provided for.

Abstract: An image analyzing device, an image analyzing method, and a recording medium storing an image analyzing program are provided. Each of the image analyzing device, the image analyzing method, and the recording medium storing the image analyzing program recognizes an area where a target is displayed based on a feature value of an input image, generates space recognition information to recognize spatial properties of each portion of the input image, divides the image into a plurality of similar areas according to similarity in feature value of the input image, the similar area having a similar feature value, obtains specified attribute data of the spatial properties to be referred to, from image areas around the recognized area where the target is displayed, recognizes the spatial properties according to the space recognition information, and determines whether a result of recognition is appropriate at the portion where the target is displayed.

Abstract: A system, apparatus, method, and computer-readable medium for optimizing classifiers are disclosed. The optimization process can include receiving one or more training examples. The optimization process can further include assigning a loss parameter to each training example. The optimization process can further include optimizing each loss parameter of each training sample based on a sample variance of each training example using a non-linear function. The optimization process can further include estimating a classifier from the one or more weighted training samples. The optimization process can further include assigning a loss parameter to the classifier based on a number of training examples that the classifier correctly classified and a number of training examples that the classifier incorrectly classified. The optimization process can further include adding the weighted classifier to an overall classifier.

Abstract: A system and method for active contour segmentation. An image of a first and at least a second structure, a first position for the first structure, and at least a second position for the at least second structure are received, the first and the at least second positions are set as a first and an at least second initial contour, and the first and the at least second initial contours are concurrently and iteratively deformed to respectively expand into a first and at least a second expanded contour matching a shape of the first and the at least second structure by applying constraints to each point of the first and the at least second initial contour, comprising a constraint for preventing the first and at least one of the at least second initial contour from intersecting one another upon being deformed, and the constraints are updated after each iteration.

Abstract: A method includes accessing image data representing tissue and identifying one or more features of the tissue indicated by the image data. A model is selected for the tissue based on the one or more identified features. The image data is segmented and, using the model, one or more anatomical landmarks of the tissue indicated by the segmented image data are identified.

Abstract: Disclosed are a method and apparatus for adjusting a set of luminance values associated with a set of visual elements in a current frame (310) of a video sequence for object detection (370). The method determines (410,430,450), for each of a plurality of scenarios (SO, SI, S2), a set of adjusted luminance values based on a corresponding luminance compensation value, and accumulates (460), for each scenario, a set of brightness counts and darkness counts of the current frame based on the set of adjusted luminance values. A metric (470) is calculated for each scenario based on the set of brightness counts and darkness counts and one of scenarios is selected based on the calculated metric. The method then selects (350) the adjusted luminance value associated with the selected scenario as an adjusted set of luminance values associated with the current frame of the video sequence.