Abstract: A method for tracking a lane on a road is presented. The method comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further includes generating, by the one or more processors, a predicted spline comprising (i) a first spline and (ii) a predicted extension of the first spline in a direction in which the imaging system is moving. The first spline describes a boundary of a lane and is generated based on the set of pixels. The predicted extension of the first spline is generated based at least in part on a curvature of at least a portion of the first spline.

Abstract: A method of analyzing film on a substrate comprises receiving surface profile data obtained from measurements of a plurality of discrete regions on a substrate, the plurality of discrete regions comprising one or more film layers; extracting a plurality of parameters from the received surface profile data, the plurality of parameters comprising one or more parameters of the one or more film layers of each of the plurality of discrete regions, wherein the extracting is based on a predetermined pattern for the plurality of the discrete regions on the substrate; and displaying a user interface.

Abstract: The current document is directed to methods and systems that effectively and efficiently employ incomplete training data to train machine-learning-based systems. Incomplete training data, as one example, may include training data with erroneous or inaccurate input-vector/label pairs. In currently disclosed methods and systems, Incomplete training data is mapped to loss classes based on addition training-data information and specific, different additional-information-dependent loss-generation methods are employed for training data of different loss classes during machine-learning-based-system training so that incomplete training data can be effectively and efficiently used.

Abstract: A system and method for providing an interpretable and unified representation for trajectory prediction that includes receiving birds-eye image data associated with travel of at least one agent within a roadway environment. The system and method also include analyzing the birds-eye image data to determine a potential field associated with the roadway environment and analyzing the birds-eye image data to determine a potential field associated with a past trajectory of the at least one agent. The system and method further include predicting a future trajectory of the at least one agent based on analysis of the potential fields.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: A method and system for checking data gathering conditions or image capturing conditions associated with images during AI based visual-inspection process. The method comprises generating a first representative (FR1) image for a first group of images and a second representative image (FR2) for a second group of images. A difference image data is generated between FR1 image and the FR2 image based on calculating difference between luminance values of pixels with same coordinate values. Thereafter, one or more of a plurality of white pixels or intensity-values are determined within the difference image based on acquiring difference image data formed of luminance difference-values of pixels. An index representing difference of data-capturing conditions across the FR1 image and the FR2 image is determined, said index having been determined at least based on the plurality of white pixels or intensity-values, for example, based on application of a plurality of AI or ML techniques.

Abstract: Embodiments disclosed herein include, but are not limited to, methods for capturing video sampling data comprising a plurality of video images of a moving object, for example using one or more cameras positioned on a stationary frame of reference adjacent to the mechanical component under investigation, in which a change in motion of the moving object is correlated to an origin frame obtained from the sampling data and representing a point at which the change in motion first occurs.

Abstract: An authentication system according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: track an object included in a video captured by a first capture device; detect a candidate for biometric authentication in the object being tracked; determine whether biometric authentication has been performed for the candidate based on a record of biometric authentication performed for the object being tracked; and perform the biometric authentication for the candidate based on a video of an authentication part of the candidate when the biometric authentication has not been performed for the candidate, the video of the authentication part being captured by a second capture device having a capture range including a part of a capture range of the first capture device.

Abstract: Disclosed are systems and methods for full body measurements extraction using a mobile device camera.

Abstract: Provided is a reward function generation method for calculating a reward in reinforcement learning, the method being executed by a computer, and the method includes accepting input of an instruction to generate a reward function including a plurality of setting data that is information regarding a key performance indicator, generating one partial reward function for one of the setting data, generating a linear combination of a plurality of the partial reward functions as the reward function, and outputting information regarding the reward function generated to the computer that executes the reinforcement learning, by the computer.

Abstract: The disclosure is generally directed to systems and methods for sharing a video feed of a camera among multiple image processing components in a vehicle. A first priority may be applied to a first image processing component that performs a first image processing function. A second priority that is lower than the first priority, is applied to a second image processing component that performs a second image processing function. The first function may be deemed more important than the second function due to various reasons. Consequently, the first image processing component is offered priority to apply a first set of camera settings on the camera. The second image processing component may prefer to apply a different set of camera settings for executing the second image processing function. An access arbitrator allows the second image processing component to do so when the first image processing component relinquishes control of the camera.

Abstract: Disclosed herein is a system and method for characterizing adventitial tissue. In one aspect, a system and method are disclosed that characterizes tissue types within the adventitial tissue including nerve bundles and blood vessels. In a further aspect, the adventitia is imaged and characterized to provide guidance for crossing lesions within an occluded vessel.

Abstract: Motion of actors within a scene may be detected based on imaging data, using machine learning tools operating on cameras that captured the imaging data. The machine learning tools process images to perform a number of tasks, including detecting heads of actors, and sets of pixels corresponding to the actors, before constructing line segments from the heads of the actors to floor surfaces on which the actors stand or walk. The line segments are aligned along lines extending from locations of heads within an image to a vanishing point of a camera that captured the image. Trajectories of actors and visual data are transferred from the cameras to a central server, which links trajectories captured by multiple cameras and locates detected actors throughout the scene, even when the actors are not detected within a field of view of at least one camera.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract: A hybrid approach for using reference frames is presented in which a series of anchor frames is used, effectively resetting a global frame upon a trigger event. With each new anchor frame, parameter values for lane boundary estimates (known as lane boundary states) can be recalculated with respect to the new anchor frame. Triggering events may a based on a length of time, distance traveled, and/or an uncertainty value.

Abstract: A convolutional neural network is used to generate hash strings corresponding to object instances. The characteristic hash strings are used to recognize the same object instance depicted in images generated at different times and by different camera devices.

Abstract: The present disclosure describes a method of foreground segmentation and nucleus ranking for scoring dual ISH images. The method has been developed to better identify those nuclei, within a selected field of view, that meet the criteria for dual ISH scoring.

Abstract: An image processing apparatus includes: a determiner that determines whether or not a vehicle in which a camera is mounted is located at a specific place where a traveling direction of the vehicle changes, the camera picking up an image of an entire image pickup range with first image quality and picking up an image of a partial region in the image pickup range with second image quality that is higher image quality than the first image quality; and a region changer that, when it is determined that the vehicle is located at the specific place, changes a position of the partial region in the image pickup range such that an image of a region of attention that is included in the image pickup range and an image of which is to be picked up with the second image quality is picked up.

Abstract: According to an embodiment of the disclosure, an electronic device may include a communication interface, a dynamic vision sensor (DVS) to generate bit data for each of a plurality of image frames, based on change in illuminance, a processor electrically connected with the communication interface and the DVS, and a memory electrically connected with the processor. The memory may store instructions, and the instructions may cause the processor to filter out at least a partial frame of the plurality of image frames, based on a ratio of the number of a bit value, which is included in the bit data and corresponds to each pixel, to the number of total pixels constituting each of the plurality of image frames, and recognize a shape of a surrounding object of the electronic device, based on another frame of the plurality of image frames. Moreover, various embodiment found through the disclosure are possible.

Abstract: A vehicle includes a sensor unit configured to acquire positional information of at least one object located in the vicinity of the vehicle, the sensor unit comprising a plurality of sensor modules. A controller is configured to determine a tracking filter based on a correspondence relationship between the plurality of sensor modules and the at least one object and to track the position of the at least one object using the positional information of the at least one object and the tracking filter.