Abstract: Disclosed herein are an apparatus and method for registering face poses for face recognition. The apparatus includes a face detection unit for detecting the face of a user from an image including the face of the user; a pose recognition unit for recognizing the face pose of the face of the user based on the degree of rotation of the face of the user; a registration interface unit for providing an interface for showing information about whether the face poses of the face of the user are registered; and a face registration unit for registering the face pose of the face of the user when the face pose of the face of the user is recognized as an unregistered face pose based on the interface.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from images. In particular, the novel present invention provides a unique platform for analyzing, classifying, extracting, and processing information from images using deep learning image detection models with the use of convolutional neural networks. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from images that can be securely distributed to multiple entities and third parties while maintaining the ability to deter unauthorized copying of the platform via embedded steganographic watermarking introduced during the encoding process and only removable using an additional step between the encoding and decoding processes.

Abstract: Inspection apparatus includes an imaging module, which is configured to capture images of defects at different, respective locations on a sample. A processor is coupled to process the images so as to automatically assign respective classifications to the defects, and to autonomously control the imaging module to continue capturing the images responsively to the assigned classifications.

Abstract: According to one or more embodiments, a method, a computer program product, and a computer system for preventing intrusion during video recording or streaming are provided. According to the method, at first, a camera of a device may capture a scene into a video, then the device may store frame-series images of the video into a cache. The device may then detect whether there is at least one intruder in the scene, and then in response to the at least one intruder being detected in the scene, the device may eliminate the at least one intruder from the cached frame-series images.

Abstract: The disclosure is directed to systems and methods for image capturing technologies and, more particularly, to a slice scan imaging system and respective processes to achieve high quality images. The method can be implemented in a computing device, which includes: capturing multiple lines of an image in a single slice; capturing multiple slices; stitching together the multiple slices by aligning common features of the images of a previous slice with a successive slice; and blending together the stitched together multiple slices.

Abstract: In one embodiment, the apparatus includes a decoder. The decoder is configured to obtain first and second motion vectors of a block other than the current block. The other block neighbors the current block at one of a left, top and top right position. The decoder is configured to determine first and second motion vectors of the current block using the first and second motion vectors of the other block such that the first motion vector of the current block has a same direction as the first motion vector of the other block and the second motion vector of the current block has a same direction as the second motion vector of the other block.

Abstract: In a boundary line recognition apparatus, based on luminance levels of an image captured by a camera, candidate edge points of a boundary line sectioning a travel road are extracted, and a candidate line of the boundary line is extracted. An apparent width of the candidate line on an image is calculated, from a width of the candidate line in a horizontal direction of the image and an angle of the candidate line relative to a vertical direction of the image. A probability of a candidate line being a boundary line is calculated to be higher, as a degree of the candidate line having characteristics as a boundary line is higher. The calculated probabilities are integrated in respect of a plurality of characteristics to recognize a boundary line. The characteristics include a ratio of the calculated apparent width to an image blur degree is larger than a predetermined value.

Abstract: A method that generally relates to comparing a current dental setup with a series of preset dental setups. More particularly a method where this comparison is used to determine which aligner should be used from a series of dental aligners for proceeding with an orthodontic treatment. Each aligner in the series of previously manufactured aligners is provided for moving the at least one tooth in the patient's teeth from one dental setup to a subsequent dental setup.

Abstract: A learning method performed by an image recognition device is provided which includes a convolutional neural network. The learning method includes performing first learning by the convolutional neural network by using a first image group captured at a first frame rate, the first image group being input from outside; and performing, after performing the first learning, second learning by using a second image group that is captured at a second frame rate that is lower than the first frame rate, the second image group being is input from outside, and the number of images in the second image group being less than that in the first image group.

Abstract: Determining spatial coordinates of a 3D reconstruction includes obtaining, from a first camera system, a first image comprising a first real object, obtaining, from a second camera system, a second image comprising a second real object associated with known geometric properties, wherein the first camera system and the second camera system have a known spatial relationship, and determining a scale of the face based on the second image and the known geometric properties of the at least part of the second real object. Determining the spatial coordinates of the 3D reconstruction also includes determining a pose of the first and second camera systems, and determining the spatial coordinates based on the pose of the first camera system and the scale of the at least part of the second real object.

Abstract: A technology which enables identifying, via a computer, a vessel in a third image. The third image is obtained from a subtraction of a second image from a first image. The second image and the first image are aligned within an imaging space. The first image is post-contrast. The second image is pre-contrast. The technology enables determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image. The technology enables obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value. The technology enables applying, via the computer, a filter onto the fourth image. The technology enables generating, via the computer, a filter mask based on the fourth image.

Abstract: A system and a process are described for computing the cost of usable and consumable materials for painting motor vehicles, from the analysis of deformations in the motor vehicles, the process comprising the following steps: loading image data relevant for a three-dimensional image of a damaged vehicle in a vehicle images memory; in the images memory of the damaged vehicles, recalling the image data of at least one three-dimensional image of a sample vehicle from a database of images of sample vehicles; automatically comparing the three-dimensional image of the damaged vehicle with the corresponding three-dimensional image of the sample vehicle identifying, through an automatic comparison between the two images: damage position or deformation and detecting the distorted regions; selecting through graphical tools for delimiting or pointing out the damaged or distorted regions identified from the automatic comparison on at least one of the two images; computing perimeter, area and/or volume from the damaged or

Abstract: The systems, methods, and computer program products disclosed herein pertain to image analysis techniques that may exploit computer vision, supervised and unsupervised machine learning, and deep neural network models in order to detect change and determine spatio-temporal attention regions of relevant change in multi-spectral and multi-resolution satellite images. In some embodiments, the automatic detection of changes in regions over large scales of satellite imagery data may be further used to generate alerts in the context of a monitoring and alerting system. In some embodiments, the input to the system may be provided through an analytics web interface or an application programming interface (API), and the output may also be served through the web interface or API.

Abstract: An image processing apparatus comprises: an acquisition unit that acquires images of a plurality of consecutive frames; a first composite unit that performs lighten composite on the images; a second composite unit that performs darken composite on the images by two consecutive frames, and subtracts an image of a frame before or after the images of the two frames subjected to the darken composite from a result of the darken composite to generate a subtraction image; a third composite unit that performs lighten composite on subtraction images obtained by the second composite unit; and an addition unit that adds a first image obtained by performing the lighten composite on the images by the first composite unit and a second image obtained by performing lighten composite on the subtraction images.

Abstract: A defect inspection method and a defect inspection system are provided. In the method, a plurality of candidate defect images are retrieved from inspection images obtained by at least one optical inspection tool performing hot scans on at least one wafer and a plurality of attributes are extracted from the inspection images. A random forest classifier including a plurality of decision trees for classifying the candidate defect images is created, wherein the decision trees are built with different subset of the attributes and the candidate defect images. A plurality of candidate defect images are retrieved from the optical inspection tool in runtime and applied to the decision trees, and classified into nuisance images and real defect images according to votes of the decision trees in which the nuisance images are filtered out. The real defect images with the votes over a confidence value are sampled for microscopic review.

Abstract: Disclosed are methods and systems that include obtaining at least one image of a dendritic structure, analyzing the at least one image to identify one or more features associated with the dendritic structure, and determining a numerical value associated with the dendritic structure based on the one or more features.

Abstract: A method for learning a neural network performed by a computing means wherein the neural network receives a marine image and outputs information about a type and a distance of at least one object included in the marine image is provided. The method comprises: obtaining a marine image including a sea and an obstacle; obtaining a labelling data generated based on the marine image; obtaining an output data by using a neural network, wherein the neural network receives the marine image and outputs the output data; calculating an error value by using the labelling data and the output data; and updating the neural network based on the error value; wherein the labelling data and the output data are determined by a combination of information about a type and a distance of an object.

Abstract: A method and apparatus for recognizing a character area in an image are provided. A specific embodiment of the method comprises: acquiring color values and position information of pixel points in a to-be-recognized image; clustering the pixel points based on the color values of the pixel points, the color values of the pixel points in each pixel point category being identical or similar; determining, for each category of clustered pixel points, outlines of connected regions including the pixel points in each category of pixel points, to obtain an outline set; and merging, based on the color values and the position information of the outlines in the outline set, the outlines, to obtain character areas in the image. The embodiment has improved the accuracy of recognizing character lines in recognizing characters in an image.

Abstract: A system and method allows a light colored image of an object such as a check to be detected and captured on a light colored background for uploading to a server for processing. Detection involves detecting edges of objects on the image, drawing a rectangle around the detected edges, testing for an aspect ratio of the rectangle within an approved range, testing for the rectangle being outside of a margin of the image and being a certain percentage of the image, and testing for blur within a tolerable range.

Abstract: In an image processing method that converts image data into output data by performing tone mapping, an edge of an image represented by the image data is determined, a first tone mapping operation is performed on first image data included in the image data, where the first image data represent a first portion of the image not including the edge, whether a gray level of second image data is within a predetermined gray range is determined, where the second image data represent a second portion of the image including the edge, a second tone mapping operation is performed on the second image data when the gray level of the second image data is within the gray range, and the first tone mapping operation is performed on the second image data when the gray level of the second image data is not within the gray range.