Abstract: Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, a novel AutoEncoder framework is presented to explicitly disentangle pose and appearance features from RGB imagery and a long short-term memory integration of pose features over time produces the gait feature.

Abstract: Systems and methods for image based location estimation are described. In one example embodiment, a first positioning system is used to generate a first position estimate. A set of structure façade data describing one or more structure façades associated with the first position estimate is then accessed. A first image of an environment is captured, and a portion of the image is matched to part of the structure façade data. A second position is then estimated based on a comparison of the structure façade data with the portion of the image matched to the structure façade data.

Abstract: The disclosure provides method of training a machine-learning model employing a procedurally synthesized training dataset, a machine that includes a trained machine-learning model, and a method of operating a machine. In one example, the method of training includes: (1) generating training image definitions in accordance with variations in content of training images to be included in a training dataset, (2) rendering the training images corresponding to the training image definitions, (3) generating, at least partially in parallel with the rendering, ground truth data corresponding to the training images, the training images and the ground truth comprising the training dataset, and (4) training a machine-learning model using the training dataset and the ground truth data.

Abstract: An image capturing device including a cover plate, an image capturing module, a frame body, a first adhesive layer, and a second adhesive layer is provided. The frame body and the image capturing module are located on the same side of the cover plate. The frame body is joined to the cover plate through the first adhesive layer. The image capturing module is joined to the frame body through the second adhesive layer. An orthographic projection of the second adhesive layer on the cover plate falls within an orthographic projection of the frame body on the cover plate.

Abstract: A method and device for automatically identifying a point of interest in a depth measurement on a viewed object using a video inspection device is disclosed. The video inspect device determines the three-dimensional coordinates in a region of interest on the viewed object and analyzes those surface points to determine the desired measurement application (e.g., determining the deepest point, the highest point, or the clearance between two surfaces). Based on the desired measurement application, the video inspection device automatically identifies the point of interest on the viewed object and places a cursor at that location.

Abstract: Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

Abstract: A fingerprint sensor and a method of manufacturing the same are provided. The fingerprint sensor includes an active region including driving electrodes and sensing electrodes crossing the driving electrodes, tracer regions connected respectively to the driving electrodes and the sensing electrodes, and shield layers disposed respectively on the tracer regions.

Abstract: The invention relates a method for generating a motion-corrected image for visual-inertial odometry comprising an event camera rigidly connected to an inertial measurement unit (IMU), wherein the event camera comprises pixels arranged in an image plane that are configured to output events in presence of brightness changes in a scene at the time they occur, wherein each event comprises the time at which it is recorded and a position of the respective pixel that detected the brightness change, the method comprising the steps of: Acquiring at least one set of events (S), wherein the at least one set (S) comprises a plurality of subsequent events (e); Acquiring IMU data (D) for the duration of the at least one set (S); Generating a motion-corrected image from the at least one set (S) of events (e), wherein the motion-corrected image is obtained by assigning the position (xj) of each event (ej) recorded at its corresponding event time (tj) at an estimated event camera pose (Ttj) to an adjusted event position (x?j)

Abstract: A method for managing tracklets in a particle filter estimation framework includes executing a tracklet prediction dependent on a list of previous tracklets, thereby determining persistent tracklets and new tracklets; sampling new measurements for initializing the new tracklets, thereby determining an amount of estimated new tracklets; and determining an amount of the persistent tracklets dependent on the list of previous tracklets. The method further includes determining an amount of the new tracklets and an amount of updated persistent tracklets to be sampled dependent on the amount of estimated new tracklets, the amount of the persistent tracklets, and a memory bound; sampling the updated persistent tracklets from a list of the persistent tracklets dependent on the determined amount of the updated persistent tracklets; and sampling the new tracklets from unassociated measurements dependent on the determined amount of the new tracklets.

Abstract: A surveillance method, apparatus, system, electronic device and computer-readable storage medium are provided. In the surveillance method, a non-visible light image and a target image are obtained, wherein the target image is generated from a visible light signal captured during a capture period of the non-visible light image. The method then detects whether an object is present in the non-visible light image. When an object is detected in the non-visible light image, a second location area of the object in a visible light image is determined according to a first location area of the object in the non-visible light image, such that surveillance of the object is implemented based on the visible light image, wherein the visible light image is an image determined based on the target image.

Abstract: An image processing device includes: a delimiting line detection unit configured to detect a delimiting line candidate based on image data obtained by capturing a surrounding of a vehicle; an exclusion determination unit configured to determine whether there is a parking-unavailable area in which parking of the vehicle is not permitted; and a parking space detection unit configured to detect the parking space based on the delimiting line candidate. In a case where plural parallel lines that are adjacent to the delimiting line candidate and that have a predetermined angle to the delimiting line candidate is detected, the exclusion determination unit regards an area in which the plural parallel lines are provided as the parking-unavailable area, and prohibits the parking space detection unit from detecting the parking space across the delimiting line candidate adjacent to the parking-unavailable area.

Abstract: Examples of various method and systems are provided for information extraction from scene information. 2D image information can be generated from 2D images of the scene that are overlapping at least part of one or more object(s). The 2D image information can be combined with 3D information about the scene incorporating at least part of the object(s) to generate projective geometry information. Clustered 3D information associated with the object(s) can be generated by partitioning and grouping 3D data points present in the 3D information. The clustered 3D information can be used to provide, e.g., measurement information, dimensions, geometric information, and/or topological information about the object(s). Segmented 2D information can also be generated from the 2D image information. Validated 2D and 3D information can be produced by cross-referencing between the projective geometry information, clustered 3D information, and/or segmented 2D image information, and used to label the object(s) in the scene.

Abstract: The disclosure provides a method for verifying training data, a training system, and a computer program produce. The method includes: receiving a labelled result with a plurality of bounding regions, wherein the labelled result corresponds to an image, the bounding regions are labelled by a plurality of annotators, the annotators comprises a first annotator and a second annotator, and the bounding region comprises a first bounding region labelled by the first annotator and a second bounding region labelled by the second annotator; and determining the first bounding region and the second bounding region respectively corresponds to different two target objects or corresponds to one target object according to a similarity between the first bounding region and the second bounding region.

Abstract: A microscopic imaging method, includes illuminating a specimen with illumination radiation and capturing detection radiation along a detection axis. The detection radiation is caused by the illumination radiation, at a first time as a wide-field signal and at a second time as a composite signal. The composite signal is formed by a superposition of a confocal image and a wide-field image; extracting the confocal image by subtracting the wide-field signal from the composite signal, wherein a correction factor is used. A current correction factor is ascertained for each executed imaging and/or for each imaged specimen (1) and the confocal image is extracted using the respective current correction factor.

Abstract: A distributed object tracking system includes a plurality of image analysis devices and a cluster management service device connected to the plurality of image analysis devices, wherein each image analysis device analyzes an object in a corresponding real-time video stream to generate an analysis result for tracking the object, and the cluster management service device includes a tracking module to form a trajectory of the object according to the analysis result generated by each of the plurality of image analysis devices.

Abstract: An image processing method and apparatus are provided. The image processing apparatus includes a receiver configured to receive image data; and a processor configured to generate first data by performing a Fourier calculation on the received image data, generate second data by performing prism phase computation on the first data, generate third data by adding the first data and the second data, and perform encoding based on the third data.

Abstract: A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

Abstract: An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

Abstract: A method is disclosed and includes receiving a seismic cube. The seismic cube includes a three-dimensional image of a portion of a subsurface area. The method further includes providing the seismic cube to a machine learning process. The machine learning process includes one or more neural networks used for predicting a location of a subsurface seismic layer in the received seismic cube. The method also includes receiving, from the machine learning process, the prediction of the location of the subsurface seismic layer in the seismic cube.

Abstract: A user identification system includes an image recognition network to analyze image data and generate shape data based on the image data. The system also includes a generalist network to analyze the shape data and generate general category data based on the shape data. The system further includes a specialist network to compare the general category data with a characteristic to generate narrow category data. Moreover, the system includes a classifier layer including a plurality of nodes to represent a classification decision based on the narrow category data.