Abstract: Provided are a feature impression axis determination system, a feature impression axis determination method, and a program thereof capable of determining a feature axis indicating features of a plurality of images. A plurality of images is input, and a standard impression value of each of the plurality of images is determined with respect to at least two standard impression axes. The determined standard impression values are plotted in a standard impression region. In the standard region defined by the two standard impression axes, an axis on which a variance of the standard impression values becomes a maximum is determined as a feature impression axis indicating features of the plurality of input images. The determined feature impression axis is displayed in the standard region.

Abstract: Various techniques are disclosed to generate a contrast-enhanced combined image based on a thermal image and high spatial frequency content extracted from a visual light image, said images depicting the same scene. In one example, a method comprises: determining a blending measure indicating the quality of at least one of said images; combining luminance components of pixel values comprised in the thermal image and of pixel values representing high spatial frequency content comprised in the visual light image based on the blending measure; and generating a contrast-enhanced combined image based on the thermal image and the combined luminance components.

Abstract: A method for detecting user liveness is provided that includes selecting first and second images from a sequence of images. The first and second images are captured under different illumination conditions. The method further includes locating feature calculation windows in corresponding positions on the first and second images. Each window includes a first area and a second area. Moreover, the method includes calculating, by a computing device, a feature value for each window position based on pixels, within the windows located at the position, from the first and second images. Furthermore, the method includes calculating a feature vector from the feature values, calculating a confidence score from the feature vector, and determining the sequence of images includes images of a live user when the confidence score is equal to or greater than the threshold score.

Abstract: This specific person detection system: identifies, from among the persons recorded in a specific person recording unit, a person who most closely matches a feature value extracted from image data; calculates the degree to which feature values of a plurality of persons extracted from other image data match the identified person; and outputs, as an identification result, information about a person who has a feature value closely matching the identified person, and who is associated with angle information that most closely matches angle information associated with the identified person.

Abstract: A method for dividing a blister strip having a plurality of blister-packaged drugs apportioned into separate sections of the blister strip is provided. A blister strip with a substrate having a plurality of depressions for drug portions is provided and an image of the substrate is generated by a 3D-sensor device. A control device evaluates the image with image analysis. Based on the analysis results, an arrangement of the depressions is determined and control signals for a separating device are generated. The blister strip is then divided into a plurality of blister strip parts based on the control signals. A blister strip dividing device is also provided.

Abstract: A method and apparatus for training a character detector based on weak supervision, a character detection system and a computer readable storage medium are provided, wherein the method includes: inputting coarse-grained annotation information of a to-be-processed object, wherein the coarse-grained annotation information including a whole bounding outline of a word, text bar or line of the to-be-processed objected; dividing the whole bounding outline of the coarse-grained annotation information, to obtain a coarse bounding box of a character of the to-be-processed object; obtaining a predicted bounding box of the character of the to-be-processed object through a neural network model from the coarse-grained annotation information; and determining a fine bounding box of the character of the to-be-processed object as character-based annotation of the to-be-processed object, according to the coarse bounding box and the predicted bounding box.

Abstract: In the image processing device, the image processing method and the recording medium, the image analyzer carries out image analysis on an image. The tag information assignor assigns the image with tag information corresponding to objects present in the image based on the result of the image analysis. The first assignment ratio calculator calculates an assignment ratio of common tag information assigned to images owned by users as the first assignment ratio. The second assignment ratio calculator calculates an assignment ratio of the common tag information assigned to images owned by each user as the second assignment ratio. And the tag ranking determiner ranks the common tag information assigned to images owned by the user based on the difference between the first assignment ratio and the second assignment ratio of the common tag information.

Abstract: Sampling data is disclosed. A region of a map to be rendered is determined. It is determined that a first subregion is included in the region. Data points associated with subregions in a neighborhood of the first subregion are analyzed to determine a regional density of points. Data points associated with the first subregion are sampled based on the regional density of points.

Abstract: Use of pupillary response to visible light for iris authentication is disclosed. One example involves (a) capturing an initial image of an eye including an inner circular boundary between a pupil region and an iris region and an outer circular boundary between the iris region and a sclera region, (b) determining a first size measurement indicative of the inner circular boundary, (c) responsive to at least the first size measurement, modulating one or more visible light sources to output visible light toward the eye of the user, (d) capturing a subsequent image of the eye of the user during a period of pupillary response, (e) obtaining an iris data record of the user, and (f) comparing the iris data record of the user to one or more registered iris data records to authenticate the user.

Abstract: The A pattern recognition apparatus using domain adaptation 10 comprises an estimation unit 11. The estimation unit 11 estimates PLDA (Probabilistic Linear Discriminant Analysis) parameters and transformation parameters from features of a first domain data and a second domain data so as to maximize/minimize an objective function with respect to the features.

Abstract: Disclosed are methods for object tracking. In an example, the method comprises: determining a region of interest (ROI) in a first frame of a video sequences; feeding the determined ROI forward through a first CNN (convolutional network) to obtain a plurality of first feature maps in a higher layer of the CNN and a plurality of second feature maps in a lower layer of the first CNN; selecting a plurality of feature maps from the first and second feature maps, respectively; predicting, based on the selected first and second feature maps, two target heat maps indicating a target location for said objects in the current frame, respectively; and estimating, based on the two predicated target heat maps, a final target location for the object in the current frame.

Abstract: A life determination device comprises: an image acquiring unit that acquires an image showing a result of machining by cutting on a target of the machining by cutting; a feature quantity acquiring unit that acquires a feature quantity indicating the quality of the result of the machining by cutting from the image acquired by the image acquiring unit; and a life determining unit that determines the life of a cutting tool having been used for the machining by cutting on the target based on the feature quantity acquired by the feature quantity acquiring unit.

Abstract: Methods, systems, and media for color palette extraction for videos are provided. In some embodiments, the method comprises: identifying, at a server, a frame of a video content item; clustering pixels of the frame of the video content item based on a color of each of the pixels into a group of clusters; for each of a plurality of clusters in the group of clusters, determining an average color for the cluster; selecting a cluster of the plurality of clusters based on the average color of the cluster; determining a color palette corresponding to the frame of the video content item for one or more user interface elements in which the video content item is to be presented based on the average color of the selected cluster; and transmitting information indicating the color palette to a user device in response to a request to present the video content item.

Abstract: A method for dividing a blister strip having a plurality of blister-packaged drugs apportioned into separate sections of the blister strip is provided. A blister strip with a substrate having a plurality of depressions for drug portions is provided and an image of the substrate is generated by a 3D-sensor device. A control device evaluates the image with image analysis. Based on the analysis results, an arrangement of the depressions is determined and control signals for a separating device are generated. The blister strip is then divided into a plurality of blister strip parts based on the control signals. A blister strip dividing device is also provided.

Abstract: Various examples are directed to methods and systems for object recognition in an image. A computer vision system may receive a patch comprising a plurality of pixels arranged in a grid. The computer vision system may determine a plurality of columns and a plurality of rows in the patch. The plurality of columns may be based at least in part on a column target sum and the plurality of rows may be based at least in part on a row target sum.

Abstract: Methods and systems for detecting defects on a specimen are provided. One system includes a storage medium configured for storing images for a physical version of a specimen generated by an inspection system. At least two dies are formed on the specimen with different values of one or more parameters of a fabrication process performed on the specimen. The system also includes computer subsystem(s) configured for comparing portions of the stored images generated at locations on the specimen at which patterns having the same as-designed characteristics are formed with at least two of the different values. The portions of the stored images that are compared are not constrained by locations of the dies on the specimen, locations of the patterns within the dies, or locations of the patterns on the specimen. The computer subsystem(s) are also configured for detecting defects at the locations based on results of the comparing.

Abstract: A system, device, and method for inspecting the cosmetic and operational features of electronic devices, including computing and telecommunications devices. The cosmetic inspection system includes an image capture unit for capturing the images of the electronic devices, and a user interface for processing the captured images and providing relevant information to the user of the system. Images of the external components such as external casing materials or touch screens of electronic devices are captured and the cosmetic inspection system uses baseline images to make determinations to identify defective components of the electronic devices. Based on these determinations, the system may conclude which, if any, replacement components of the devices are needed to restore the electronic device. In one embodiment, a user of the system may then be provided with information through a user interface about defective components and options for ordering replacement components.

Abstract: A system and method of image classification may include linking between a plurality of predetermined objects and categories, assigning a set of attributes for each object until relationships between all categories are determined, storing at least one classified image for each object in an object database, receiving an unclassified image, comparing the received unclassified image to the at least one classified image, determining at least one attribute of the received unclassified image, based on the comparison and based on the assigned attributes for each object, assigning a classification of the received unclassified image, with correlation between the at least one determined attribute of the received unclassified image, and relationships between the categories.

Abstract: Methods and systems for combining information present in measured images of semiconductor wafers with additional measurements of particular structures within the measured images are presented herein. In one aspect, an image-based signal response metrology (SRM) model is trained based on measured images and corresponding reference measurements of particular structures within each image. The trained, image-based SRM model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers. In another aspect, a measurement signal synthesis model is trained based on measured images and corresponding measurement signals generated by measurements of particular structures within each image by a non-imaging measurement technique.

Abstract: A method of jointly classifying a plurality of objects in an image using a feature type selected from a plurality of feature types determines classification information for each of the plurality of objects in the image by applying a predetermined joint classifier to at least one feature of a first type. The feature is generated from the image using a first feature extractor, the classification information being based on a probability of each of a plurality of possible classifications. The method estimates, for each of the feature types, an improvement in an accuracy of classification for each of the plurality of objects. The method selects features of a further type, from the plurality of feature types, according to the estimated improvement in the accuracy of the classification of each of the objects, and classifies the plurality of objects in the image using the selected features of the further type.