Abstract: A method of determining dynamic properties of a structure (linear or nonlinear) includes receiving spatio-temporal inputs, generating mode shapes and modal components corresponding to the spatio-temporal inputs using a trained deep complexity coding artificial neural network, and subsequently generating the dynamic properties by analyzing each modal component using a trained learning machine. A computing system for non-contact determination of dynamic properties of a structure includes a camera, a processor, and a memory including computer-executable instructions. When the instructions are executed, the system is caused to receive spatio-temporal image data, decompose the spatio-temporal image data into constituent manifold components using an autoencoder, and analyze the constituent manifold components using a trained learning machine to determine the dynamic properties.

Abstract: A display panel inspection system includes a camera to image respective frames of a mother substrate in a frame-by-frame manner, a stage to move the mother substrate relative to the camera, an image obtaining unit to store frames imaged by the camera, a gray level extracting unit that obtains gray level values of the frames, a frame matching unit that searches and matches two frames that match each other among the frames imaged by the camera, a correcting unit that performs correction on one of two frames matched with each other in consideration of an alignment error and an image distortion between the two frames, and a comparing unit that compares the gray level values of the two frames matched with each other, after the correction.

Abstract: A gaze estimation method and apparatus is disclosed. The gaze estimation method includes obtaining an image including an eye region of a user, extracting, from the obtained image, a first feature of data, obtaining a second feature of data used for calibration of a neural network model, and estimating a gaze of the user using the first feature and the second feature.

Abstract: Provided are an image detection device, an image detection method and a program, which are capable of improving correspondence to a target deformation by optimizing a template shape, when performing target detection using template matching. An image detection device 100 for detecting a target from an input image comprises: a template generation unit 10 that generates a template for detecting a target; a mask generation unit 20 that generates a mask which shields a portion of the template, on the basis of temporal variations of a feature point extracted from an area including the image target; and a detection unit 30 that detects the target from the image using the template a portion of which is shielded by the mask.

Abstract: Various techniques are disclosed for separating and removing low-frequency shadow or shading (also referred to herein as “non-uniformity”) from images that have been corrupted by the non-uniformity. A non-uniformity estimate that approximates the non-uniformity effect on the corrupted image may be generated by iteratively adding new blotches of non-uniformity data represented by two-dimensional (2D) functions, such as 2D Gaussian functions, to the non-uniformity estimate and applying filters to smoothen the 2D functions. In each iteration of the non-uniformity estimate generation process, a new non-uniformity update candidate that minimizes a cost function is identified. The corrupted image is processed based on the non-uniformity estimate to generate a corrected image.

Abstract: An approach is provided for determining a location of a shared vehicle based on fused location data. The approach includes initiating a capture of an image of a shared vehicle using a camera sensor of a device. The approach also includes processing the image to determine an image-based location of the shared vehicle. The approach also includes fusing the image-based location with at least one other source of location data indicating a position of the shared vehicle to determine the location of the shared vehicle.

Abstract: A method for image processing includes: acquiring features of multiple images of a target object and a standard feature of the target object; and determining trusted images of the target object from the multiple images of the target object according to similarities between the features of the multiple images of the target object and the standard feature thereof, wherein similarities between features of the trusted images of the target object and the standard feature of the target object meet a preset similarity requirement. The image processing method may be applied to application scenarios such as image comparison, identity recognition, target object search, and similar target object determination.

Abstract: A method for accurately determining a positioning of an automated guided vehicle relative to a substrate includes scanning a substrate surface of the substrate to produce two or more two-dimensional images, with each respective one of the produced two-dimensional images is associated with a known portion of the substrate surface. The method further includes storing each one of the produced images. The method further includes positioning the automated guided vehicle relative to the substrate surface, and scanning the substrate surface corresponding to the positioned automated guide vehicle to produce a supplemental two-dimensional image. The supplemental image is compared to each of the stored two or more images to accurately determine the position of the vehicle relative to the substrate surface at the position. This information can also be used to drive the automated guided vehicle from the determined position to another known position relative to the substrate surface.

Abstract: An approach is provided for generating a polyline from line segments (e.g., line segments representing objects detected by a computer vision system). The approach involves selecting a line segment from a plurality of line segments. The approach also involves determining a neighboring line segment from among the plurality of line segments. The determined neighboring line segment has a closest distance to the line segment from among the plurality of line segments. The approach further involves merging the line segment and the neighboring line segment into a polyline based on determining that the closest distance is a mutual closest distance between line segment and the neighboring line segment.

Abstract: A method for detecting motion information includes the following steps. First, a pixel array is provided for detecting an image of a measured object located in a first distance range or in a second distance range, and the pixel array includes a plurality of invisible image sensing pixels and a plurality of visible image sensing pixels. Then, image detection is conducted within the first distance range by using the invisible image sensing pixels to output a plurality of invisible images. Next, the image detection is conducted within the second distance range by using the visible image sensing pixels to output a plurality of visible images. Then, the plurality of invisible images and the plurality of visible images are analyzed by using a processing unit, so as to obtain motion information of the measured object. A pixel array for detecting motion information and an image sensor are also provided.

Abstract: The present invention relates to a method to determine geometrical parameters of an object under study by radiography, the object can be described geometrically, wherein intercepts that go through the material of the object under study can be determined from a projection of the object—e.g. a tube—imaged by an X- or gamma-radiation source if exposition data of the radiographic image are available. These intercepts that go through the material of the object—i.e. the intercept curves—allow that the object under study—e.g. the tube—have a dimension that is larger than the dimension of the device (film/detector) used to take the radiographic image. During the course of said method, the source of radiation, the object under study and the device (film/detector) used to take the radiographic image are in a fixed position.

Abstract: Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

Abstract: Methods and systems are disclosed for user recognition and express for an automobile. For one example, a data processing system for an automobile includes a database, a user capture device, and a computer. The database stores at least an image of a user of the automobile. The user capture device captures at least an image of the user. The computer is coupled to the database and the user capture device. The computer allows the user access to the automobile if the user is authenticated as a valid user by determining if stored image of the user match the captured image of the user. The facial features can include two-dimensional (2D) or three-dimensional (3D) images including facial features. The computer allows the user access to the automobile and controls of the automobile if the user is authenticated as a valid user. The computer can also deny the user access to the automobile and controls of the automobile if the user is not authenticated as a valid user.

Abstract: An automated process empirically normalizes a “dark” image by adjusting the apparent exposure to compensate for nonlinearity in the luminance response of the image sensor. The process includes receiving at least two digital images, one of the digital images having an exposure value that is greater than that of another of the digital images. A reduced-resolution pair of images is produced from the at least two digital images. At least one representative scale factor is calculated from tonal values in the two images and at least one empirical scale factor is determined by selective interpolation between the representative scale factor and a comparative scale factor. The empirical scale factor is used in a function applied pixelwise to the darker of the digital images to produce an empirically normalized digital image.

Abstract: The invention relates to methods and systems of detection of smoke or the like in an environment, such as a closed room but not limited thereto, based on video data processing of video data captured in said environment, and wherein the video data processing is for example executed on a computation system.

Abstract: Disclosed are various embodiments for controlling access to resources by a client device. Methods may include receiving a user request to access a resource on the device and determining whether the resource requires a facial capture. If the resource requires a facial capture, a camera of the device may be automatically activated to capture an image and the resource may be rendered on the device. In some cases, access to the resource may be limited based on whether the image includes a face or not. A record associating the image and the requested resource may be stored, for example, on the device or on a remote server.

Abstract: Systems and methods related to identifying locations and/or ranges to objects using airborne imaging devices are disclosed. An object tracking system may include a plurality of aerial vehicles having associated imaging devices and a control station. Information related to positions and orientations of aerial vehicles and associated imaging devices may be received. In addition, imaging data may be received and processed to identify optical rays associated with objects within the imaging data. Further, a three-dimensional mapping of the identified optical rays may be generated, and locations or ranges of the objects relative to the aerial vehicles may be determined based on any intersections of optical rays within the three-dimensional mapping.

Abstract: The disclosed techniques include systems and methods for implementing liveliness detection in an authentication process using pupil or iris tracking. The disclosed techniques can utilize a combination of facial recognition and pupil or iris tracking for liveliness detection in an authentication process to provide an extra layer of security against impersonation attacks.

Abstract: In one implementation, a method of training a type-agnostic object segmentation system is performed in a type-agnostic object segmentation system including one or more processors, and a non-transitory memory. The method includes obtaining a data set; generating a respective embedding vector for each of a plurality of pixels of the image frame; mapping a plurality of embedding vector representations into a dataspace defined by a dimensionality of the plurality of embedding vector representations; comparing the at least one object instance representation candidate against the respective segmentation mask; and adjusting the type-agnostic object segmentation system in order to satisfy an error threshold across the plurality of image data frames according to a determination that the at least one object instance representation candidate and the respective segmentation mask differ by a threshold value.

Abstract: Methods, systems, and techniques for generating two-dimensional (2D) and three-dimensional (3D) images and image streams. The images and image streams may be generated using active stereo cameras projecting at least one illumination pattern, or by using a structured light camera and a pair of different illumination patterns of which at least one is a structured light illumination pattern. When using an active stereo camera, a 3D image may be generated by performing a stereoscopic combination of a first set of images (depicting a first illumination pattern) and a 2D image may be generated using a second set of images (optionally depicting a second illumination pattern). When using a structured light camera, a 3D image may be generated based on a first image that depicts a structured light illumination pattern, and a 2D image may be generated from the first image and a second image that depicts a different illumination pattern.