Abstract: Systems and methods are disclosed herein for automatically identifying a query object within a visual medium. The technique generally involves receiving as input to a neural network a query object and a visual medium including the query object. The technique also involves generating, by the neural network, representations of the query object and the visual medium defining features of the query object and the visual medium. The technique also involves generating, by the neural network, a heat map using the representations. The heat map identifies a location of pixels corresponding to the query object within the visual medium and is usable to generate an updated visual medium highlighting the query object.

Abstract: According to an embodiment, a facial recognition system includes an image sensor, a facial recognition processing unit, a heart rate detection unit, and an authentication unit. The image sensor detects incident light by an infrared pixel, and outputs detected infrared information. The facial recognition processing unit recognizes the face of a person based on image information signal captured by the image sensor. The heart rate detection unit reads the heart rate of the person from pulses in a blood vessel in the face based on the infrared information. The authentication unit receives facial recognition signal outputted from the facial recognition processing unit. The authentication unit authenticates the person as an identical person when the heart rate is inputted, and authenticates the person as a photograph when the heart rate is not inputted.

Abstract: A fingerprint identification method is provided. The fingerprint identification method includes following steps: obtaining an object image and storing a plurality of pixel data of the object image in a first color model format, where the pixel data include a plurality of first pixel values; converting the pixel data into a second color model format and obtaining a plurality of second pixel values based on the converted pixel data and a first gain value; calculating a plurality of third pixel values based on the first pixel values and the second pixel values; calculating a first standard deviation based on the third pixel values; and determining whether the first standard deviation being greater than a first preset threshold value, if the first standard deviation being greater than the first preset threshold value, recognizing the object image as a fingerprint image of a true finger.

Abstract: The visual similarity between fonts is determined using visual descriptors of character images in the fonts. A model used to generate the visual descriptors may include a set of letterforms, keypoint locations on each letterform, and detail shapes at zero, one, or more detail areas on the letterform. In some instances, the model may also set forth one or more geometric measurements. Based on the model, a visual descriptor may be generated for a character image from a font by identifying a letterform of the character image, identifying keypoint locations on the character image, and identifying a detail shape at any detail areas on the character image. Additionally, the visual descriptor may include any geometric measurement defined by the model. The visual similarity between two fonts may be determined as a function of the differences between pairs of visual descriptors for the fonts that correspond with the same letterform.

Abstract: Embodiments of the present disclosure relate to non-sequential document comparison. A first plurality of segments in a first document and a second plurality of segments in a second document are obtained. In response to a first segment from the first plurality of segments being associated with a second segment from the second plurality of segments, a third segment from the first plurality of segments is associated with a fourth segment from the second plurality of segments.

Abstract: The present invention generally relates to a method for forming a fingerprint using a fingerprint sensing system, and specifically to the possibility of allowing the formation of a fingerprint when only separated portions of the fingerprint is available. The invention also relates to the corresponding fingerprint sensing system and to a computer program product.

Abstract: An information processing system includes a verification unit and an execution unit. The verification unit verifies biometric information acquired from users against preregistered reference information. The execution unit executes a process based on the number of times biometric information has been verified on a user basis in a predetermined period by the verification unit.

Abstract: Systems and methods are provided for selecting feature points within an image. A plurality of candidate feature points are identified in the image. A plurality of feature points are selected for each of the plurality of candidate feature points, a plurality of sets of representative pixels. For each set of representative pixels, a representative value is determined as one of a maximum chromaticity value and a minimum chromaticity value from the set of representative pixels. A score is determined for each candidate feature point from the representative values for the plurality of sets of representative pixels associated with the candidate feature point. The feature points are selected according to the determined scores for the plurality of candidate feature points.

Abstract: Interior scan and exterior photos are jointly visualized in medical imaging. One or more cameras are mounted to the internal medical scanner, allowing a photograph to be taken while or in temporal proximity to when the internal region of the patient is scanned. An image associating the exterior photographs with the interior region of the patient is presented to assist in diagnosis. Due to the spatial and temporal proximity, the image may be a visualization formed by combining the photograph and interior scan data.

Abstract: An apparatus for and a method of correcting an image for an image artifact. An initial image is corrected by an image artifact corrector (190). The so corrected sample correction image is compared with the initial image to obtain information on the corrective action. The corrective action is then adaptively reapplied by a controller (140) to obtain an improved corrected image thereby ensuring previously present artifacts are removed and creation of new artifacts are avoided.

Abstract: A system comprises a picture and metadata captured by a content capture system; a recognizable characteristic datastore configured to store recognizable characteristics of different users; a module configured to identify a time and a location associated with the picture based on the metadata, and to identify one or more potential target systems within a predetermined range of the location at the time; a characteristic recognition module configured to retrieve the recognizable characteristics of one or more potential users associated with the potential target systems, and evaluate whether the picture includes one or more representations of at least one actual target user from the potential users based on the recognizable characteristics of the potential users; a distortion module configured to distort a feature of the representations of the least one actual target user in response to the determination; a communication module configured to communicate the distorted picture to a computer network.

Abstract: A method is provided for providing automated testing of an Optical Character Recognition (OCR) system. An automated testing framework may convert original text files to images of various formats and resolutions. The images may comprise various fonts and layouts according to a document type. The images may be processed by the OCR system to generate a converted text file. Converted text files may be compared to original text files and an OCR accuracy score may be calculated.

Abstract: The information presentation device has an image acquisition unit, a parameter deriving unit, an abnormality detection unit, and the presentation unit. The image acquisition unit acquires a first image which is captured by a first imaging device and a second image which is captured by a second imaging device. The parameter deriving unit derives parameters for deriving three-dimensional coordinates from the first image and the second image. The abnormality detection unit determines presence or absence of an abnormality in a relative positional relationship or a relative relationship of pose between the first imaging device and the second imaging device according to the derived parameters. In a case where an abnormality is determined, the presentation unit presents warning information including an instruction that there is an abnormality.

Abstract: A parking map generated based on determining a plurality of object clusters by associating pixels from an image with points from a point cloud. At least a portion of the plurality of object clusters can be classified into one of a plurality of object classifications including at least a vehicle object classification. A bounding box for one or more of the plurality of object clusters classified as the vehicle object classification can be generated. The bounding box can be included as a parking space on a parking map based on a location associated with the image and/or point cloud.

Abstract: A method includes obtaining a plurality of images, including geolocation information and time information for each image. The method also includes identifying a group of related images from the plurality of images from a common geographical area and a common time period based on the geolocation information and the time information for each of the plurality of images, and identifying an event that occurred within the common time period and within the common geographical area by searching a repository of event information. The method also includes storing the group of related images in an image collection, and assigning a title to the image collection, wherein the title is based at least in part on a title of the event.

Abstract: Systems and methods are disclosed for deep learning and classifying images of objects by receiving images of objects for training or classification of the objects; producing fine-grained labels of the objects; providing object images to a multi-class convolutional neural network (CNN) having a softmax layer and a final fully connected layer to explicitly model bipartite-graph labels (BGLs); and optimizing the CNN with global back-propagation.

Abstract: An image measurement device acquires a first measurement region and a second measurement region on an image, acquires distance information corresponding to the first measurement region and distance information corresponding to the second measurement region, and computes a distance between the two regions. In a case where the first measurement region is displaced on the image, the second measurement region is displaced on a contour or a plane on the image, and a distance between the first measurement region after the displacement and the second measurement region after the displacement is computed. Thereby, distance measurement is able to be performed over a wide range without a complicated operation in the image measurement device for measuring a distance between two regions by using an image.

Abstract: A face detection and tracking method of a robotic device. The method includes obtaining a video frame from a camera of the robotic device; performing a face detection process on the video frame to detect one or more faces in the video frame and, after the face detection process, identifying the detected one or more faces in the video frame. The method also includes performing a vision-based tracking process to track the identified one or more faces using a combination of a feature points tracker and a correlation tracker and, after the vision-based tracking process, performing a detection and tracking fusion process and providing desired target prediction of the identified one or more faces.

Abstract: A system and method for performing spoof detection are disclosed. The method includes: receiving an input image of a biometric; generating a first filtered image by applying a first convolution to the input image based on a first convolution kernel; generating a second filtered image by applying a second convolution to the input image based on a second convolution kernel; computing a gradient residual image by subtracting, for each pixel location of the first and second filtered images, a pixel value in the second filtered image from a corresponding pixel value in the first filtered image; applying a density estimation procedure to the gradient residual image to identify areas of varied density; and, determining whether the input image is a replica of the biometric based on results of the density estimation procedure.

Abstract: A method of processing a set of images of a scene is disclosed. The set is represented by a plurality of picture-elements defining an image plane, and the images are characterized by different light intensity spatial distributions but the same viewpoint relative to the scene. The method comprises: for each of at least some picture-elements, independently selecting a direction parallel to the image plane, constructing a weighted vector field based on the selected direction, and calculating a height of a surface in the scene perpendicular to the image plane based on the constructed weighted vector field. The method further comprises reconstructing a three-dimensional spatial representation of the surface based on the heights; and transmitting the spatial representation to a computer readable medium.