Abstract: Embodiments for determining an orientation of a scanned image. In an exemplary embodiment, a method comprises receiving image data of an image of one or more objects. Each object of the one or more objects in the image includes a plurality of points on a surface of the object. The method further comprises generating a plurality of subsets of points of the plurality of points and fitting a parametric model to more than one subset of the plurality of subsets to generate a plurality of parametric models. Further, the method identifies a parametric model of the plurality of parametric models that includes the largest number of points and orients the image based on the parametric model that includes the largest number of points.

Abstract: This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.

Abstract: A foreign matter inspection system that is less likely to misidentify a crack as a foreign matter during an image inspection in which a vibration is applied to a powder contained in a bag-like container. The foreign matter inspection system includes a vibration device configured to apply a vibration to a container, a photography device configured to optically photograph the inside of the container through a transparent area, and a determination device configured to determine whether a foreign matter is present inside the container based on an image of the container photographed by the photography device. The vibration device alternately applies weak vibrations W and strong vibrations S to the container. The determination device determines whether the foreign matter is present inside the container based on the image of the container photographed by the photography device when the vibration device applies the weak vibrations W.

Abstract: A difference detection device includes a difference detection unit configured to, based on association among a first image and a second image captured at different times and illustrating a substantially identical space and encoding information of each of the first image and the second image, detect difference between a third image and a fourth image captured at different times and illustrating a substantially identical space, and the encoding information is information acquired from data including the first image encoded and data including the second image encoded, before inverse transform processing is executed in decoding processing executed on each of the first image and the second image.

Abstract: An image acquisition unit acquires a plurality of projection images corresponding to a plurality of radiation source positions in a case of tomosynthesis imaging. A reconstruction unit reconstructs all or a part of the plurality of projection images to generate a tomographic image on each of a plurality of tomographic planes of a subject. A feature point detecting unit detects at least one feature point from a plurality of the tomographic images. A positional shift amount derivation unit derives a positional shift amount between the plurality of projection images with the feature point as a reference on a corresponding tomographic plane corresponding to the tomographic image in which the feature point is detected. The reconstruction unit reconstructs the plurality of projection images by correcting the positional shift amount to generate a corrected tomographic image.

Abstract: An image processing method, an image processing device, a training method and a computer-readable storage medium. The image processing method includes: extracting a characteristic vector in an image to be recognized; based on the characteristic vector of the image to be recognized, acquiring a predicted score value of the image to be recognized; and based on the predicted score value, determining a category of an image information of the image to be recognized; wherein the image to be recognized is a face image, and the image information is a facial expression.

Abstract: A vehicle and control method of the vehicle are provided. The vehicle includes a camera provided on the vehicle and configured to capture an image of an object outside the vehicle, a controller configured to determine a photographing position required for facial recognition from the captured image, a guide configured to guide the photographing position, and a display configured to display a result of the facial recognition.

Abstract: The system for the qualitative evaluation of human organs includes: a camera (101) for capturing an image of the organ, the organ being in the donor's body or already collected or placed in a hypothermic, normothermic and/or subnormothermic graft infusion machine at the time of image capture; an image processor (103, 104) configured to extract at least a portion of the organ image from the captured image and an estimator (103, 104) for estimating, from the extracted image, the health of the organ. In some embodiments, the device also includes a means of introducing into the donor's body at least one optical window of the image capture means as well as a light source to illuminate the donor's organ, while maintaining the sterility of the surgical field. In some embodiments, the image processor involves applying a clipping mask to the captured image.

Abstract: A method includes receiving patient health data; determining a score using a trained machine learning model; determining a threshold value using an adaptive threshold tuning learning model; comparing the score to the threshold value; and generating an alarm. A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the processor, cause the computing system to: receive patient health data; determine a score using a trained machine learning model; determine a threshold value using an adaptive threshold tuning learning model; compare the score to the threshold value; and generate an alarm. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive patient health data; determine a score using a trained machine learning model; determine a threshold value using an adaptive threshold tuning learning model; compare the score to the threshold value; and generate an alarm.

Abstract: A context-based object classifying model is applied to a set of object location representations (12, 14), derived from an object detection applied to a frame (10) of a video stream, to obtain a context-adapted classification probability for each object location representation (12, 14). Each object location representation (12, 14) defines a region of the frame (10) and each context-adapted classification probability represents a likelihood that the region comprises an object (11, 13). The model is generated based on object location representations from previous frames of the video stream. It is determined whether the region defined by the object location representation (12, 14) comprises an object (11, 13) based on the context-adapted classification probability and a detection probability. The detection probability is derived from the object detection and represents a likelihood that the region defined by the object location representation (12, 14) comprises an object (11, 13).

Abstract: The present teaching relates to method, system, medium, and implementations for detecting liveness. When an image is received with visual information claimed to represent a palm of a person, a region of interests (ROI) in the image that corresponds to the palm is identified. Each of a plurality of fake palm detectors individually generates an individual decision on whether the ROI corresponds to a specific type of fake palm that the fake palm detector is to detect. Such individual decisions from the plurality of fake palm detectors are combined to derive a liveness detection decision with respect to the ROI.

Abstract: Techniques are described for converting a 2D map into a 3D mesh. The 2D map of the environment is generated using data captured by a 2D scanner. Further, a set of features is identified from a subset of panoramic images of the environment that are captured by a camera. Further, the panoramic images from the subset are aligned with the 2D map using the features that are extracted. Further, 3D coordinates of the features are determined using 2D coordinates from the 2D map and a third coordinate based on a pose of the camera. The 3D mesh is generated using the 3D coordinates of the features.

Abstract: An information processing apparatus (10) according to the present disclosure includes: an object recognition unit (11) that outputs, by using a first modal signal and a first modal recognition model corresponding to the first modal signal, an inference result regarding the first modal signal; a training data processing unit (12) that generates first modal training data regarding the first modal signal by using the inference result, and updates second modal training data regarding a second modal signal that is different from the first modal signal by using the first modal training data; and a recognition model update unit (13) that updates a second modal recognition model corresponding to the second modal signal by using the second modal training data.

Abstract: An automated line clearance inspection system will enable fast and accurate inspection of packaging equipment lines to reduce or prevent product intermixing. The system includes a set of image capturing devices that are controlled via a central processing unit whereby end run images are compared with control images to determine if a line is cleared.

Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.

Abstract: According to one embodiment, a biometric authentication device includes a sensor and an illumination device. The sensor includes a first base material, a plurality of photoelectric conversion elements and a first control unit. The illumination device includes a second base material, a plurality of light emitting elements and a second control unit. Upon lighting the light emitting element at a predetermined position in the groups of the light emitting elements by the second control unit, the first control unit controls the operation of the photoelectric conversion elements so as to acquire a signal from the photoelectric conversion elements at predetermined positions of the groups of the photoelectric conversion elements.

Abstract: A method for training an automatic learning machine (3.12), comprising the following steps: defining, in a database, at least one first class of image matrices (3.11) and one second class of image matrices (3.11), the first class of image matrices being associated with said first measured characteristic value; the second class of image matrices (3.11) being associated with said second measured characteristic value; classifying said image matrices in said classes as a function of the respective measured characteristic value for the mixture of plant elements from which each of said images has been obtained; modifying, by said calculator (3.15), in response to the reception of said overall classification error (3.18), at least one of its calculation elements, so that the second probabilities of inclusion of the image matrices in said at least two classes are closer to the true inclusion of the at least three image matrices in the at least two classes than the first inclusion probabilities.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect surfaces and reflections in such surfaces. Some implementations involve providing a CGR environment that includes virtual content that replaces the appearance of a user or the user's device in a mirror or other surface providing a reflection. For example, a CGR environment may be modified to include a reflection of the user that does not include the device that the user is holding or wearing. In another example, the CGR environment is modified so that virtual content, such as a newer version of the electronic device or a virtual wand, replaces the electronic device in the reflection. In another example, the CGR environment is modified so that virtual content, such as a user avatar, replaces the user in the reflection.

Abstract: A system and computer-implemented method are provided for annotation of image data. A user is enabled to iteratively annotate the image data. An iteration of said iterative annotation comprises generating labels for a current image data part based on user-verified labels of a previous image data part, and enabling the user to verify and correct said generated labels to obtain user-verified labels for the current image data part. The labels for the current image data part are generated by combining respective outputs of a label propagation algorithm and a machine-learned classifier trained on user-verified labels and image data and applied to image data of the current image data part. The machine-learned classifier is retrained using the user-verified labels and the image data of the current image data part to obtain a retrained machine-learned classifier.

Abstract: An object of the present disclosure is to reduce the number of pages while guaranteeing the consistency of the story of an album. One embodiment of the present invention is a non-transitory computer readable storage medium storing a program for causing a computer to function as: a selection unit configured to select, based on analysis results of each of a plurality of model images used in a model album and analysis results of each image in a candidate image group that is used in an album, similar images similar to the model images from among the candidate image group; a creation unit configured to create a layout in which the similar images selected by the selection unit are arranged in templates of the model album; and a reducing unit configured to reduce a number of pages of the layout.