Abstract: A detection device includes an acquirer that acquires a video of each of a plurality of bearings of a structure including the plurality of bearings, an extractor that extracts a dynamic feature corresponding to a plurality of degrees of freedom of each of the plurality of bearings based on the video, and an identifier that identifies, among the plurality of bearings, a bearing whose dynamic feature fails to match a dynamic feature of one or more other bearings of the plurality of bearings.

Abstract: A single-instruction, multiple data processor performs object detection in an image by testing for a plurality of object features in a plurality of image regions, the processor comprising: a set of computation units operable to execute a plurality of classifier sequences in parallel, each classifier sequence comprising a plurality of classifier routines, and each classifier routine comprising identical instructions to the other classifier routines in each of the plurality of classifier sequences; wherein each computation unit is configured to independently maintain data identifying an image region and a feature under test on that computation unit, and each classifier routine is arranged to access the data, test the identified feature against the identified image region and update the data such that the computation units are operable to concurrently test different features against different image regions.

Abstract: Some embodiments are directed to an image director of a patient monitoring system to obtain calibration images of a calibration sheet or other calibration object at various orientations and locations. The images are then stored and processed to calculate camera parameters defining the location and orientation of the image detector and identifying internal characteristics of the image detector, and the information are stored. The patient monitoring system can be re-calibrated by using the image detector to obtain an additional image of a calibration sheet or calibration object. The additional image and the stored camera parameters are then used to detect any apparent change in the internal characteristics of the image detector (10)(S6-4).

Abstract: The described technology is a technique related to an indoor localization method using deep learning. The indoor localization method comprises: opening a 3D tour comprising a plurality of panoramic images; receiving a first perspective image captured by a camera provided in the user device; calculating global features for the first perspective image and each of the plurality of panoramic images included in the 3D tour; selecting a most similar panoramic image to the first perspective image by using the calculated global features; computing an indoor location corresponding to a location of the camera on the 3D tour by using feature points included in the selected panoramic image and the first perspective image; and providing the computed indoor location to the user device.

Abstract: Disclosed are methods, apparatus and systems for real-time gesture recognition. One exemplary method for the real-time identification of a gesture communicated by a subject includes receiving, by a first thread of the one or more multi-threaded processors, a first set of image frames associated with the gesture, the first set of image frames captured during a first time interval, performing, by the first thread, pose estimation on each frame of the first set of image frames including eliminating background information from each frame to obtain one or more areas of interest, storing information representative of the one or more areas of interest in a shared memory accessible to the one or more multi-threaded processors, and performing, by a second thread of the one or more multi-threaded processors, a gesture recognition operation on a second set of image frames associated with the gesture.

Abstract: A system is configured to receive an indication that an apparatus in a first assembled state should comprise a component with a first digital fingerprint and a component with a second digital fingerprint. The system is configured to receive video footage of apparatuses in the first assembled state. The system is configured to isolate an image of an apparatus in the first assembled state. The system is configured to split the image into a frame comprising a first component and a frame comprising a second component. The system is configured to identify feature points and to determine that the first set of feature points matches the first digital fingerprint and that the second set of feature points matches the second digital fingerprint. The system is configured to update a component database.

Abstract: The invention relates to computer-implemented method for the 3D reconstruction of a ground surface area by stereophotogrammetry, comprising the steps of: determining corrected Rational Polynomial Camera, RPC, models by performing bundle adjustment (BA) of original RPC models each provided with an image of a set of images of the ground surface area acquired by a remote imaging sensor and each associated to a corresponding original projection function ({Pm}) from a 3D object space to a 2D image space, wherein determining the corrected RPC models comprises determining corrected projection functions ({Pmcor}) from the 3D object space to the 2D image space; and determining (PC) a 3D point cloud representative (3DPC) of the ground surface area by triangulation, based on the corrected RPC models, of stereo correspondences within images of the set of images.

Abstract: In one or more implementations, the apparatus, systems and methods disclosed herein are directed to configuring a color measurement device to output color measurements that match the expected output of a different color measurement device. In a particular implementation, a method is provided for matching the color measurements made by a color measurement device to the color measurements made by a target color measurement device by implementing a single step color calibration and conversion process using an Artificial Neural Network (ANN). By way of non-limiting example, the raw counts from the color measurement device is converted to a specific color space, such as L*a*b, directly through an ANN. Such ANN is trained to ensure the output of the color measurement from the color measurement device will match with the output of the color measurement from a target color measurement device.

Abstract: A computer-based system and method for taking clarity measurements of a gem, and a computer-readable medium having computer-executable instructions, are provided and include receiving a pixilated image of a gem and identifying pixels representing an inclusion. The method and medium further include determining characteristics of the inclusion as a function of the pixels representing the inclusion, and providing a clarity grade based upon the determined characteristics. Also provided is a method for mapping a gem, and a computer-readable medium having computer-executable instructions, which include receiving a pixilated image of a gem having facet edges, and identifying pixels representing the facet edges. The method and medium further include generating a diagram of the gem, such that the diagram is a function of the pixels representing the facet edges, and superimposing the diagram onto the pixilated image.

Abstract: The APPARATUSES, METHODS AND SYSTEMS FOR A VIDEO REMOTE DEPOSIT CAPTURE PLATFORM (hereinafter “RDC-Video”) provides a platform for remote deposit by obtaining images of a check from streaming video captured by a video camera, wherein the RDC-Video transforms captured check images and/or entered check deposit information inputs via RDC-Video components into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the RDC-Video may capture images of the check and send them to a financial institution for deposit processing.

Abstract: An on-device training-based user recognition method includes performing on-device training on a feature extractor based on reference data corresponding to generalized users and user data, determining a registration feature vector based on an output from the feature extractor in response to the input of the user data, determining a test feature vector based on an output from the feature extractor in response to an input of test data, and performing user recognition on a test user based on a result of comparing the registration feature vector to the test feature vector.

Abstract: A graphics processor can include a processing cluster array including a plurality of processing clusters coupled with the plurality of memory controllers, each processing cluster of the plurality of processing clusters including a plurality of streaming multiprocessors, the processing cluster array configured for partitioning into a plurality of partitions. The plurality of partitions include a first partition including a first plurality of streaming multiprocessors configured to perform operations for a first neural network, The operations for the first neural network are isolated to the first partition. The plurality of partitions also include a second partition including a second plurality of streaming multiprocessors configured to perform operations for a second neural network. The operations for the second neural network are isolated to the second partition and protected from operations performed for the first neural network.

Abstract: The APPARATUSES, METHODS AND SYSTEMS FOR A VIDEO REMOTE DEPOSIT CAPTURE PLATFORM (hereinafter “RDC-Video”) provides a platform for remote deposit by obtaining images of a check from streaming video captured by a video camera, wherein the RDC-Video transforms captured check images and/or entered check deposit information inputs via RDC-Video components into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the RDC-Video may capture images of the check and send them to a financial institution for deposit processing.

Abstract: A defect detection apparatus includes a defect detection unit and a calculation unit. The defect detection unit detects a defect that appears on an external appearance of a structure through imaging processing from external appearance image data that is generated by imaging the external appearance of the structure with a surveying instrument. The calculation unit calculates defect data relating to the defect detected by the defect detection unit by using coordinate data that is correlated with the external appearance image data.

Abstract: An object tracking system that includes a sensor that is configured to capture frames of at least a portion of a global plane for a space. The system further includes a position sensor that is configured to output (x,y) coordinates corresponding with the physical location of the sensor within the space. The system is configured to receive an (x,y) coordinate within the space for the sensor from the position sensor. The (x,y) coordinate corresponds with a new physical location of the sensor within the space. The system is further configured to determine translation coefficients for the sensor based on a difference between the (x,y) coordinate and a previous (x,y) coordinate for the sensor. The system is further configured to update a homography associated with the sensor by applying the translation coefficients to the homography and to store the updated homography.

Abstract: In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Abstract: The invention relates to a method for detecting the heating activity in objects or materials, based on thermal only or thermal and visual data images. The method is based on a physics-based model of the underlying heating phenomena within materials or objects, while taking into account disturbing factors of natural phenomena such as weather conditions and day and night cycle. The invention also relates to a corresponding system for detecting the heating activity in objects or materials.

Abstract: An information processing apparatus according to the present invention includes: a first acquisition unit that acquires biometric information of a person; a second acquisition unit that acquires identification information of the baggage possessed by the person; and a creating unit that creates link information for associating the biometric information with the identification information.

Abstract: Apparatus and associated methods relate to a method of non-contact motion detection. A one-dimensional optical sensor detects motion of a target or objects on a conveyor belt through a continuous measurement of targets or objects and a real-time comparison of the pixel images captured by the one-dimensional optical sensor. In an illustrative embodiment, a one-dimensional sensor may be configured to determine motion of objects based on changes to the captured intensities of pixel images over time. The sensor may continually capture photoelectric pixel images and compare a current pixel image with a previous pixel image to determine a frame differential image value. The frame differential image value is evaluated against a predetermined threshold over a predetermined time period. Based on the evaluation, a signal is output indicating whether the objects on the conveyor belt are moving or jammed.

Abstract: A mechanism is described for facilitating real-time multi-view detection of objects in multi-camera environments, according to one embodiment. A method of embodiments, as described herein, includes mapping first lines associated with objects to a ground plane; and forming clusters of second lines corresponding to the first lines such that an intersection point in a cluster represents a position of an object on the ground plane.