Abstract: An instance segmentation method includes: performing feature extraction on an image via a neural network to output features at at least two different hierarchies; extracting region features corresponding to at least one instance candidate region in the image from the features at the at least two different hierarchies, and fusing region features corresponding to a same instance candidate region, to obtain a first fusion feature of each instance candidate region; and performing instance segmentation based on each first fusion feature, to obtain at least one of an instance segmentation result of the corresponding instance candidate region or an instance segmentation result of the image.

Abstract: A method and apparatus is provided to inspect coverage of a coating applied to a surface of a component. A coating color space value of a color of the coating is obtained based on a camera, a light source, and the surface, each value having an associated coating thickness applied to the surface. Images of the coating covered surface are obtained. Each image is processed by determining color space values of the image, determining whether the associated coating thickness of the color space values is within a specified tolerance of a required thickness of coating based on a comparison of the color space values to color space values associated with the required thickness of coating, and responsive to the associated thickness of coating of the color space values being outside of the specified tolerance, providing an indication that the surface shown in the image is outside of the specified tolerance.

Abstract: Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation.

Abstract: Techniques are described for detecting and replacing burned-in subtitles in image and video content.

Abstract: Example implementations described herein involve defect analysis for images received from a camera system, which can involve applying a first model configured to determine regions of interest of the object from the images, applying a second model configured to identify localized areas of the object based on the regions of interest on the images; and applying a third model configured to identify defects in the localized ones of the images.

Abstract: A system of detecting vehicle components in operation in a vehicle includes at least one light source configured to emit a predetermined wavelength of light onto structures within the vehicle, wherein at least one of the structures may include a passenger seat belt assembly having a pattern of reflection and retroreflective surfaces tuned to a predetermined wavelength at a preferred luminance. At least one 3-D time of flight camera is positioned in the vehicle to receive reflected light from the structures in the vehicle and provide images of the structures that distinguish the preferred luminance of the pattern from other structures in the vehicle. A computer processor connected to computer memory and the camera includes computer readable instructions causing the processor to reconstruct 3-D information in regard to respective images of the structures and calculate a depth measurement of the distance of the reflective pattern on the passenger seat belt assembly from the camera.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: Method and system for reducing a number of eigenvectors. For example, a computer-implemented method for reducing a number of eigenvectors, the method comprising: obtaining a plurality of to-be-processed matrices; mapping the plurality of to-be-processed matrices to a space of symmetric positive definite matrices to form a Riemannian manifold corresponding to a Riemannian kernel function; obtaining a kernel-function matrix by using at least a principal component analysis to calculate one or more inner products of the mapped plurality of matrices based on at least the Riemannian kernel function; calculating a first group of eigenvectors of the kernel-function matrix, the first group of eigenvectors including a first number of eigenvectors; and selecting one or more eigenvectors from the first group of eigenvectors to obtain a second group of eigenvectors, the second group of eigenvectors including a second number of eigenvectors; wherein the second number is less than the first number.

Abstract: Image data from two different devices is used to identify a physical interaction between two users to authenticate a digital interaction between the users.

Abstract: A system and method for method for determining common patterns based on key points in multimedia data elements (MMDEs). The method includes: identifying a plurality of candidate key points in each of the plurality of MMDEs, wherein a size of each candidate key point is equal to a predetermined size and a scale of each candidate key point is equal to a predetermined scale; analyzing the identified candidate key points to determine a set of properties for each candidate key point; comparing the sets of properties of the plurality of candidate key points of each MMDE; selecting, for each MMDE, a plurality of key points from among the candidate key points based on the comparison; generating, based on the key points for each MMDE, a signature for the MMDE; and comparing the signatures of the plurality of MMDEs to output at least one common pattern among the plurality of MMDEs.

Abstract: There is provided a system and method for automated video processing of an input video signal using tracking of a single moveable bilaterally-targeted game-object involved in a team-based sporting event.

Abstract: Systems, methods, and computer readable medium are provided for determining a wall loss measurement associated with corrosion and/or erosion present within an insulated pipe. A calibration image is acquired for the pipe wall at a first location and used in conjunction with an inspection image acquired for the pipe wall at a second location to determine an inspection thickness of the pope wall. An amount of wall loss measurement can be determined based on a difference of a calibration thickness for the pipe wall at the first location and the inspection thickness of the pipe wall at the second location. The wall loss measurement characterizing an amount of wall material lost due to corrosion and/or erosion present in the pipe wall at the second location. The wall loss measurement can be output for further processing and/or display.

Abstract: A feature point position estimation device is provided. The feature point position estimation device includes a subject detection section for detecting a subject region from a subject image, a feature point positioning section for positioning a feature point at a preliminarily prepared initial feature point position with respect to the subject region, a feature amount acquisition unit for acquiring a feature amount of the feature points arranged, a regression calculation unit for calculating a deviation amount of a position of a true feature point with respect to the position of the feature point by performing a regression calculation on the feature amount, and a repositioning unit for repositioning the feature points based on the deviation amount. The regression calculation unit calculates the deviation amount by converting the feature amount in a matrix-resolved regression matrix.

Abstract: Provided are an image extraction apparatus and an image extraction method of finding a similar image conforming to a user's intention. A similarity determination condition designation window in which a plurality of similarity determination conditions are displayed is displayed. A user selects a desired similarity determination condition from among the plurality of similarity determination conditions displayed in the displayed similarity determination condition designation window. The user can determine a similarity determination condition, and can thus find a similar image conforming to the user's intention.

Abstract: A method for determining the scale of a digital 3D model may include receiving, corresponding to a plurality of images, a respective plurality of first distances between the camera and the object in the environment and calculating, using a simultaneous localization and mapping (SLAM) algorithm, a plurality of second distances between the camera and the object in the 3D model of the environment. The method may include calculating a plurality of ratios corresponding to the plurality of images based on respective ones of the plurality of first distances and respective ones of the second distances and determining a scale of the 3D model based on the plurality of ratios. The method may include creating a scaled digital 3D model based on the 3 D model and the determined scale of the 3 D model. Distances and sizes in the scaled 3D model may correspond to actual distances and sizes of the environment.

Abstract: A computer implemented color space encoding system, encoder, decoder, method and data signal are disclosed.

Abstract: According to one embodiment, an image processing apparatus includes a memory and one or more hardware processors electrically coupled to the memory. The one or more hardware processors acquire a first image of an object including a first shaped blur and a second image of the object including a second shaped blur. The first image and the second image are acquired by capturing at a time through a single image-forming optical system. The one or more hardware processors acquire distance information to the object based on the first image and the second image, with a statistical model that has learnt previously.

Abstract: A method includes obtaining surface samples that represent three-dimensional locations of surfaces of an environment; generating a voxelized representation of the surfaces of the environment in three-dimensional space using the surface samples; obtaining an image that shows the surfaces of the environment; associating each of the surface samples with image information that corresponds to a portion of the image that is spatially correlated with a respective one of the surface samples; determining voxel features for voxels from the voxelized representation based on the surface samples and the image information using a first trained machine learning model, wherein the voxel features each describe three-dimensional shapes present within a respective one of the voxels; and detecting objects based on the voxel features.

Abstract: It is provided a lock device for controlling access to a physical space. The lock device comprises: an electronically controllable lock; and a handle comprising a fingerprint sensor for capturing a fingerprint of a finger presented to the fingerprint sensor and obtaining fingerprint data based on a captured fingerprint, wherein the handle is configured to communicate wirelessly with the electronically controllable lock to selectively control unlocking of the electronically controllable lock based on the fingerprint data. The handle is configured to identify a user from the captured fingerprint, wherein an identifier of the identified user is communicated wirelessly from the handle to the electronically controllable lock to enable the electronically controllable lock to evaluate whether to perform an unlocking action.

Abstract: Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.