Abstract: Provided is a method for blending of agricultural product utilizing hyperspectral imaging. At least one region along a sample of agricultural product is scanned using at least one light source of different wavelengths. Hyperspectral images are generated from the at least one region. A spectral fingerprint for the sample of agricultural product is formed from the hyperspectral images. A plurality of samples of agricultural product is blended based on the spectral fingerprints of the samples according to parameters determined by executing a blending algorithm.

Abstract: An autonomous ship bottom inspection method by a ROV(s) based on a ship 3D model in STL format is provided. The ship 3D model is obtained and a surface thereof is spliced by triangular facets. Body 3D coordinate points of the ship 3D model are obtained and then expanded according to a safety distance of ROV and ship to obtain inspection track points of the ROV. The ship 3D model is divided into regions, and the inspection track points in each region are performed with interpolation and smoothing. Smoothed inspection track points of the regions are connected as per a result of the dividing to obtain a ship bottom inspection track, a real-time position of the ROV is obtained, a ship bottom inspection path is generated based on the ship bottom inspection track and the real-time position. The ROV is controlled to move as per the ship bottom inspection path.

Abstract: Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Method and apparatus are disclosed for image recognition. The method may include performing a vision task on an image by using a multi-scales capsules network, wherein the multi-scales capsules network includes at least two branches and an aggregation block, each of the at least two branches includes a convolution block, a primary capsules block and a transformation block, and a dimension of capsules of the primary capsules block in each of the at least two branches is different.

Abstract: In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

Abstract: A method for intelligently measuring vehicle speed based on a binocular stereo vision system includes: training a Single Shot Multibox Detector neural network to obtain a license plate recognition model; calibrating the binocular stereo vision system to acquire parameters of two cameras; detecting the license plates in the captured video frames with the license plate recognition model, locating the license plate position; performing feature point extraction and stereo matching by a feature-based matching algorithm; screening and eliminating the matching point pairs, and reserving the coordinates of the matching point pair closest to the license plate center; performing stereo measurement on the screened matching point pair to get the spatial coordinates of the position; calculating and obtaining the speed of the target vehicle.

Abstract: Disclosed herein is a method of determining a user profile based on a set of user-selected images, a method of selecting images from an image database of digital images based on a user profile, a computer system and a computer program product.

Abstract: The present invention is generally directed to systems and methods for preparing and producing a custom-fitted face mask, including, as non-limiting examples, a CPAP nasal mask and a CPAP full face mask. At least one embodiment of the invention utilizes infrared (IR) lasers, such as, for example, those found on smartphone cameras, in order to generate a 3D point cloud model of an individual's face. This point cloud model is then used to produce a custom face mask cushion, which is used to customize a generic face mask to conform to the user's specific facial geometry.

Abstract: A system and methods for a CODEC driving a real-time light field display for multi-dimensional video streaming, interactive gaming and other light field display applications is provided applying a layered scene decomposition strategy. Multi-dimensional scene data is divided into a plurality of data layers. One or more polygons representative of corresponding portions of objects in the scene are obtained for each layer and used to determine a view-independent representation. This view independent representation and data layers are sampled using a plenoptic sampling scheme and rendered using hybrid rendering to encode light fields corresponding to each data layer. The resulting compressed, (layered) core representation of the multi-dimensional scene data is produced at predictable rates, reconstructed and merged at the light field display in real-time by applying view synthesis protocols, including edge adaptive interpolation, to reconstruct pixel arrays in stages (e.g.

Abstract: There are provided methods and systems for estimating the mass of one or more occupants in a vehicle cabin comprising obtaining multiple images of the one or more occupants, comprising a sequence of 2D (two dimensional) images and 3D (three dimensional) images of the vehicle cabin captured by an image sensor, applying a pose detection algorithm on each of the obtained sequences of 2D images to yield one or more skeleton representations of said one or more occupants and combining the one or more 3D images of said sequence of 3D images with said one or more skeleton representations of said one or more occupants to yield skeleton models and analyze the skeleton models to extract one or more features of each of the one or more occupants and further process the one or more extracted features of the skeleton models to estimate the mass of the one or more occupants.

Abstract: A system and method of generating trackable frames from a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a set of frames for classification using a principal component analysis model. The set of frames are a subset of the plurality of video frames. The computing system partitions each frame of the set of frames into a plurality of clusters. The computing system classifies each frame of the plurality of frames as trackable or untrackable. Trackable frames capture a unified view of the sporting event. The computing system compares each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames. The computing system classifies each cluster that includes at least the threshold number of trackable frames as trackable.

Abstract: A vehicle exterior environment recognition apparatus includes a monocular distance calculator, a relaxation distance calculator, and an updated distance calculator. The monocular distance calculator calculates a monocular distance of a three-dimensional object from a luminance image generated by an imaging unit. The relaxation distance calculator calculates a relaxation distance of the three-dimensional object from two luminance images generated by two imaging units based on a degree of image matching between the two luminance images determined using a threshold more lenient than another threshold used to determine the degree of image matching to generate a stereo distance of the three-dimensional object. The updated distance calculator calculates an updated distance of the three-dimensional object by mixing the monocular distance and the relaxation distance at a predetermined ratio.

Abstract: A method for aligning a wafer image with a reference image, comprising: searching for a targeted reference position on the wafer image for aligning the wafer image with the reference image; and in response to a determination that the targeted reference position does not exist: defining a current lock position and an area that encloses the current lock position on the wafer image; computing an alignment score of the current lock position; comparing the alignment score of the current lock position with stored alignment scores of positions previously selected in relation to aligning the wafer image with the reference image; and aligning the wafer image with the reference image based on the comparison.

Abstract: An intraoral scanning system for scanning an intraoral dental structure of a patient in a dental procedure may include an intraoral scanner and one or more processors coupled to the intraoral scanner and programmed with instructions that when executed by the one or more processors cause the intraoral scanning system to build a 3D model of the intraoral dental structure of the patient based on scan data acquired via the intraoral scanner and output feedback on a display. The feedback may include highlighting a portion of the 3D model that includes scan data that includes a finish line of the intraoral dental structure, wherein the scan indicates that the finish line is not suitable for receiving a prosthetic.

Abstract: A method includes obtaining at least three input image frames of a scene captured using at least three imaging sensors. The input image frames include a reference image frame and multiple non-reference image frames. The method also includes generating multiple disparity maps using the input image frames. Each disparity map is associated with the reference image frame and a different non-reference image frame. The method further includes generating multiple confidence maps using the input image frames. Each confidence map identifies weights associated with one of the disparity maps. In addition, the method includes generating a depth map of the scene using the disparity maps and the confidence maps. The imaging sensors are arranged to define multiple baseline directions, where each baseline direction extends between the imaging sensor used to capture the reference image frame and the imaging sensor used to capture a different non-reference image frame.

Abstract: Systems and methods for training an adapter network that adapts a model pre-trained on synthetic images to real-world data are disclosed herein. A system may include a processor and a memory in communication with the processor and having machine-readable that cause the processor to output, using a neural network, a predicted scene that includes a three-dimensional bounding box having pose information of an object, generate a rendered map of the object that includes a rendered shape of the object and a rendered surface normal of the object, and train the adapter network, which adapts the predicted scene to adjust for a deformation of the input image by comparing the rendered map to the output map acting as a ground truth.

Abstract: An apparatus including an interface and a processor. The interface may be configured to receive pixel data. The processor may be configured to generate a reference image and a target image from the pixel data, perform disparity operations on the reference image and the target image and build a disparity map in response to the disparity operations. The disparity operations may comprise selecting a guide node from the pixel data comprising a pixel and a plurality of surrounding pixels, determining a peak location for the pixel by performing a full range search, calculating a shift offset peak location for each of the surrounding pixels by performing block matching operations in a local range near the peak location and generating values in a disparity map for the pixel data in response to the peak location for the pixel and the shift offset peak location for each of the surrounding pixels.

Abstract: A list for accurately identifying objects with different sizes that are the same attributes can be generated automatically. A classification unit classifies, from a group of images consisting of images including objects with any of a plurality of attributes, each of the images including the objects that have an identical attribute and have different real sizes into an identical cluster. An output unit outputs each of clusters into which at least two of the images are classified as a list of images with different real sizes of the objects for the identical attribute.

Abstract: Devices and techniques are generally described for determining localized visual similarity. In some examples, a selection of a first location of interest on a first image data depicting at least one article of clothing may be received. In some examples, a first machine learning model may generate a feature map representing the first image data. In some examples, a reduced feature map may be generated based at least in part on a mapping of the first location of interest to the feature map. In some examples, a second image depicting at least a second article of clothing may be determined based at least in part on the reduced feature map.