Abstract: Systems and methods for providing autonomous vehicle assistance are disclosed. In one embodiment, a method is disclosed comprising recording an image of a scene surrounding an autonomous vehicle; classifying the image using a machine learning system, the classifying comprising identifying whether the image includes a danger; determining whether the autonomous vehicle is able to respond to the danger in response to identifying that the image includes the danger; and executing one or more security maneuvers, the security maneuvers manipulating the operation of the autonomous vehicle in response to the danger.

Abstract: A system includes a camera configured to generate two-dimensional (2D) image or video data of an area of a route ahead of a vehicle moving along the route, wherein wayside equipment is disposed alongside the route in the 2D image or video data. The system also includes one or more processors configured to determine a distance between reference markings in the route along a direction that is transverse to a direction of travel of the vehicle along the route. The one or more processors also are configured to determine a separation distance between the camera and the wayside equipment based on the distance between the reference markings in the route.

Abstract: A friction estimation system for estimating friction-related data associated with a surface on which a vehicle travels, may include a camera array including a plurality of imagers configured to capture image data associated with a surface on which a vehicle travels. The image data may include light data associated with the surface. The friction estimation system may also include an image interpreter in communication with the camera array and configured to receive the image data from the camera array and determine friction-related data associated with the surface based, at least in part, on the image data. The image interpreter may be configured to be in communication with a vehicle control system and provide the friction-related data to the vehicle control system.

Abstract: The invention relates to a method for monitoring the vehicle surroundings (U) of a vehicle combination (1), in particular utility vehicle combination (1), composed of a traction vehicle (2) and at least one trailer (3) using a monitoring system with a camera (11), having at least the following steps:—defining a monitoring space (13) within a capture range (12) of the camera (11), wherein the monitoring system monitors only the monitoring space (13) for critical objects which can give rise to a collision probability, wherein the monitoring space (13) is defined and oriented in such a way that the vehicle surroundings (U) are located at least partially in the monitoring space (13), and at the same time the vehicle combination (1) is not located in the monitoring space (13), wherein for this purpose—a variable (?, ?) which characterizes the orientation between the traction vehicle (2) and the trailer (3) is acquired; and—a manipulated variable (?) is determined as a function thereof, with which method the monito

Abstract: A vision system of a vehicle includes at least one camera configured to be disposed at a vehicle so as to have a field of view exterior of the vehicle. Responsive to image processing of captured image data and with the at least one camera disposed at the vehicle, the image processor determines a three dimensional object present in the field of view of the camera and determines a point of interest on the determined object. The vision system uses triangulation to determine an estimated location in three dimensional space of the determined point of interest. The vision system processes additional frames of captured image data to enhance the estimation of the location in three dimensional space of the determined point of interest. The image processor is operable to estimate a distance to the determined object by comparing multiple frames of captured image data.

Abstract: According to an embodiment, an object detecting apparatus includes a detecting unit and a non-linear processing unit. The detecting unit is configured to detect one or more object-candidate regions from a captured image. The non-linear processing unit is configured to input the entire captured image or a part of the captured image at least including the object-candidate region to a neural network having been trained to estimate the posture of an object in the object-candidate region and the distance to the object simultaneously, and output object information at least including information on the distance to the object, using an output from the neural network.

Abstract: Provided herein is a system and method that detects an airborne object and determines a driving action based on the airborne object. The system comprises one or more sensors; one or more processors; a memory storing instructions that, when executed by the one or more processors, causes the system to perform detecting an airborne object within a detection radius of a vehicle. In response to detecting the airborne object, the system performs tracking the airborne object to obtain 3-D coordinate information of the airborne object at distinct times, determining a probability that the airborne object will collide with the one or more sensors based on the 3-D coordinate information, determining whether to take an action of the vehicle based on the determined probability, the action being one of actuating a shield to block the airborne object or activating an air curtain; and performing the action in response to determining to take the action.

Abstract: A parking assist system for a vehicle uses cameras to collect ground images at opposite edges of the vehicle. A speed sensor provides a speed signal representing a speed of the vehicle. When speed is less than a threshold, a navigation system compares a geographic location of the vehicle to map data or the camera images are monitored to detect parking zones. A parking controller is coupled to the cameras, a display panel, navigation system, and speed sensor. When the parking controller detects that the geographic location of the vehicle coincides with a parking zone then the parking controller 1) examines the ground images to recognize a pair of edge boundaries of a parking spot, 2) calculates a proportion of an intersection length of the vehicle contained within the boundaries to a full length of the vehicle, and 3) displays the calculated proportion as a percentage on the display panel.

Abstract: Aspects of the disclosure provide for a method for identifying speed limit signs and controlling an autonomous vehicle in response to detected speed limit signs. The autonomous vehicle's computing devices identifies a speed limit sign in a vehicle's environment and a location and orientation corresponding to the speed limit sign. Then, the and orientation location of the speed limit sign is determined to not correspond to a pre-stored location and a pre-stored orientation of a speed limit sign that is pre-stored in map information. An effect zone of the speed limit sign is determined based on the location and orientation of the speed limit sign and characteristics of surrounding areas or other detected object before or after the speed limit sign. The autonomous vehicle's computing devices determines a response of the vehicle based on the determined effect zone, and controls the autonomous vehicle based on the determined response.

Abstract: A recognition timing estimation device configured to estimate a timing of a driver's recognizing action related to a subsequent travel segment of a plurality of travel segments that constitute a traveling course of a vehicle, is provided. The device includes a processor configured to execute a traveling information acquiring module to acquire information related to a driving operation of a driver or the traveling course, a travel segment estimating module to estimate a pause between the travel segments based on the information acquired by the traveling information acquiring module, an eye detecting module to detect a behavior of a driver's eye, and a recognition timing estimating module to estimate the timing of the driver's recognizing action based on a temporal relationship between the pause between the travel segments estimated by the travel segment estimating module and the behavior of the eye detected by the eye detecting module.

Abstract: Provided are an electronic device and operation method thereof. The electronic device may include: a display having a biometric sensing region; a biometric sensor disposed in the biometric sensing region; and a processor.

Abstract: The present invention is to reduce the time required to detect an object after completion of the rotation of a head or a body of a robot. A robot 100 includes a camera 111, and a control unit 127 which determines an overlapping area between a first image captured with the camera 111 at first timing and a second image captured with the camera 111 at second timing later than the first timing to detect an object included in an area of the second image other than the determined overlapping area.

Abstract: An identification method and an electronic device are provided. The identification method comprises: detecting at least one object using the electronic device; providing an identification box having a first appearance which corresponds with the at least one object as detected; and displaying the identification box having the first appearance via the electronic device.

Abstract: The subject matter of this specification can be implemented in, among other things, a method that includes receiving a first image from a first camera depicting a first view of a physical item, where the physical item displays a plurality of characters. The method includes receiving a second image from a second camera depicting a second view of the physical item. The method includes performing optical character recognition on the first image to identify first characters and a first layout in the first image and on the second image to identify second characters and a second layout in the second image. The method includes combining the first characters with the second characters by comparing the first characters with the second characters and the first layout with the second layout. The method includes storing the combined first and second characters.

Abstract: Systems and methods for realizing practical applications of high speed digital image correlation (DIC) for dynamic quantities of interest are provided. In particular, a series of images are captured for a component of interest in which a non-filtered sensor and an analog low-pass filtered sensor are included within the region of interest for the series of images. Displacement signals are obtained for the component of interest, the non-filtered sensor, and the analog low-pass filtered sensor by applying digital image correlation processing to the series of images, which may also be wavelet filtered. Dynamic quantities of interest may be generated and derived from the displacement signals after having been wavelet filtered. Such dynamic quantities of interest based on the wavelet filtered DIC-derived displacement signal may be compared to sensor-derived dynamic quantities of interest to determine if aliasing is or is likely to be present.

Abstract: An example method includes receiving the one or more image frames successively in time, wherein a first image frame is received before a second image frame in time and defining one or more blocks in each of the one or more image frames, wherein the first image frame includes a first block, and the second image frame includes a second block. The method also includes in response to the second block having a probability higher than a threshold to be associated with a region of interest (ROI), selecting the second block from the one or more blocks in the second image frame, and in response to the selected second block being associated with the first block, determining an ROI status of the second block to represent whether the second block includes any part of the ROI based on an ROI status of the first block.

Abstract: A method and system for recording and reporting license plate indicia are provided. The method includes receiving an input command from a user of a vehicle requesting a capture of an image of license plate indicia of a second vehicle, capturing, via one or more cameras, one or more image of the license plate of the second vehicle, determining, using the processing circuitry, a license plate number from the captured one or more images of the license plate, and displaying the recognized license plate number on a display of the vehicle. The method further includes detecting a location of the vehicle and storing the determined license plate number, the location of the vehicle, and the image of the license plate indicia in an audio head unit or in a navigation unit.

Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.

Abstract: A glare reducing optical recognition system that recognizes alphanumeric text wherein the system includes a first light emitter that emits light in a first direction and a second light emitter that emits light in a second direction different from the first direction. The system includes an image capturing device that captures a first image of alphanumeric text illuminated by the first light emitter emitting light in the first direction, and a second image of the alphanumeric text illuminated by the second light emitter emitting light in the second direction. The system includes an image processor that constructs a glare reduced image by comparing sections of the first image with corresponding sections of the second image and selecting the section with the least luminosity to populate the corresponding section of the glare reduced image. The system may include a character recognition processor, a label producing apparatus, and/or a conveyance system.

Abstract: An image capturing apparatus including a substrate, a light source, a sensor, a light shielding element, a first reflective element, and a transparent colloid curing layer is provided. The light source, the sensor, the light shielding element, the first reflective element, and the transparent colloid curing layer are disposed on the substrate. The sensor is located next to the light source. The light shielding element is located between the light source and the sensor. The first reflective element is located between the light shielding element and the sensor. The transparent colloid curing layer covers the light source, the sensor, the light shielding element, and the first reflective element. A manufacturing method of the image capturing apparatus is also provided.

Abstract: A method for analyzing images to generate a plurality of output features includes receiving input features of the image and performing Fourier transforms on each input feature. Kernels having coefficients of a plurality of trained features are received and on-the-fly Fourier transforms (OTF-FTs) are performed on the coefficients in the kernels. The output of each Fourier transform and each OTF-FT are multiplied together to generate a plurality of products and each of the products are added to produce one sum for each output feature. Two-dimensional inverse Fourier transforms are performed on each sum.

Abstract: A method for transforming an image descriptor, based on a gradient histogram (h) having a plurality of histogram bins (hi), into a transformed gradient histogram descriptor (v) including a set of values (vj), wherein it is provided the step of applying at least one criterium for calculating the set of values (vj), at least one criterium having a location criterium of the histogram bins (hi). The invention also relates to an image processing device suitable for carrying out the method.

Abstract: Systems, methods, and computer-executable instructions for extracting key value data. Optical character recognition (OCR) text of a document is received. The y-coordinate of characters are adjusted to a common y-coordinate. The rows of OCR text are tokenized into tokens based on a distance between characters. The tokens are ordered based on the x,y coordinates of the characters. The document is clustered into a cluster based on the ordered tokens and ordered tokens from other documents. Keys for the cluster are determined from the first set of documents. Each key is a token from a first set of documents. A value is assigned to each key based on the tokens for the document, and values are assigned to each key for the other documents. The values for the document and the values for the other documents are stored in an output document.

Abstract: [Object] To obtain a more useful image when an image including a 360° view around the camera is used without being remapped. [Solution] Provided is an image processing device including: an image acquisition unit that acquires a 360° image; and a rotational angle computation unit that computes a rotational angle of the 360° image in a manner that a reference point included in the 360° image is positioned in a designated orientation with respect to a center of the 360° image.

Abstract: A data extraction system for extracting a unique identifier from a plurality of different types of tokens, the data extraction system comprising a central processing system arranged to receive a data string representing an image of the token, the central processing system comprises: a data store of predetermined data records relating to the plurality of different types of token, each predetermined data record including a plurality of discrete features derived from an image of each type of token; a token type recognition module for identifying a type of token which the representation relates to, the recognition module comprising: a discrete feature identifier for iteratively identifying discrete features present in the representation; and a matching engine for iteratively comparing each of the identified features with each of the discrete features of the plurality of different types of token stored in the data store and registering each matched feature; wherein the recognition module is arranged to compare a c

Abstract: A method for determining a spatial position of an object includes obtaining an image with at least one camera. The object is identified in the image. At least one of a pixel size of the object in the image and a pixel offset of the object from a center of an image plane of the at least one camera is determined. A distance between the object and the camera image plane is determined using either the pixel size and the pixel offset. The spatial position is determined using the distance and at least one known distance between the object and another element of the image.

Abstract: A system for evaluating a user-marked document having a response area and at least one image marker, the system includes a means for receiving a digital image of the user-marked document and a computer to identify user-mark positional information of at least one user-mark within the response area relative to the at least one image marker from the received digital image.

Abstract: Aspects of the subject disclosure may include, for example, a method for determining a first set of features in first images of first media content, generating a similarity score by processing the first set of features with a favorability model derived by identifying generative features and discriminative features of second media content that is favored by a viewer, and providing the similarity score to a network for predicting a response by the viewer to the first media content. Other embodiments are disclosed.

Abstract: An image processing apparatus includes a scale-down factor determination unit that generates two-dimensional images of an object viewed from a plurality of viewpoints using three-dimensional data representing a three-dimensional shape of the object and determines a scale-down factor for each viewpoint, a template generation unit that scales down the generated two-dimensional image from each viewpoint using the scale-down factor determined for the viewpoint and calculates a feature quantity from a scaled-down image to generate a template, and a template information output unit that outputs, for each viewpoint, the template and the scale-down factor used for generating the template in a manner associated with each other.

Abstract: A neuromorphic device including a convolution neural network is described. The convolution neural network may include an input layer having a plurality of input pixels, a plurality of kernel resistors, each of the kernel resistors corresponding to one of the plurality of input pixels, and an intermediate layer having a plurality of intermediate pixels electrically connected to the plurality of kernel resistors.

Abstract: The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.

Abstract: Provided are neural network-based image processing method and apparatus, and a computer-readable storage medium. The image processing method includes: inputting an image into an optimized neural network; extracting, by the optimized neural network, image features of the image; and outputting the image features, wherein the optimized neural network is obtained by performing a first optimization process on at least one sub-layer in a pre-trained initial neural network, each sub-layer of the at least one sub-layer includes a convolutional layer, and the first optimization process comprises: for each sub-layer of the at least one sub-layer, determining one or more channels to be removed from a filter of the convolutional layer and removing said one or more channels, and optimizing parameters of remaining channels in the filter of the convolutional layer, so that error of output features of each optimized sub-layer is minimized.

Abstract: Methods and systems for training a learning based defect classifier are provided. One method includes training a learning based defect classifier with a training set of defects that includes identified defects of interest (DOIs) and identified nuisances. The DOIs and nuisances in the training set include DOIs and nuisances identified on at least one training wafer and at least one inspection wafer. The at least one training wafer is known to have an abnormally high defectivity and the at least one inspection wafer is expected to have normal defectivity.

Abstract: A computer system may provide Encrypted Deep Learning Service (EDLS) to a client. The computer system includes one or more processors and memory storing instructions. When instructions are executed by the one or more processors, the instructions cause the computer system to perform acts including: receiving training data from the client, where the training data comprise cipher images that are encrypted using an orthogonal transformation that hides sensitive information in original images. The acts further include training a deep neural network using the training data in the computer system; and producing cipher inference using the deep neural network when the computer system receives new data including new images encrypted using the orthogonal transformation.

Abstract: A camera method and a monitoring camera with an animal recognition function are provided. The camera method includes two steps of image capture and feature identification. Specifically, a camera system is started when a trigger signal representing that a moving object enters a camera range is captured by a trigger sensor of a monitoring camera, and the monitoring camera captures an image of an animal within the camera range. Then the captured image is transmitted to a feature identification system configured on the monitoring camera to determine an animal category of the animal presented in the captured image by the feature identification system, thereby classifying and storing the captured image. The camera method and the monitoring camera store the captured images separately from other images as needed, thereby effectively improving efficiency of browsing and analyzing the images of the monitored animal, which brings convenience to the user.

Abstract: A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. Processor executable instructions are organized into a module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels. The image projection process performs multi-level spatial convolution, pooling, subsampling, and interpolation.

Abstract: Aspects of the present disclosure involve a system and method for learning from images of raw data, including transactional data. In one embodiment, a system is introduced that can learn from the images of such data. In particular, machine learning is implemented on images in order to classify information in a more accurate manner. The images are created from raw data deriving from data sources relating to user accounts, in various embodiments.

Abstract: A non-transitory recording medium stores a program causing a computer to execute a process including determining a reference value on a decision surface classifying a group of image data represented by a plurality of types of feature quantities into two classes in a feature quantity space, based on a feature quantity of given image data that is closest to the decision surface and is classified into one of the classes of the group of image data in the feature quantity space, plotting and displaying pieces of image data of the group of image data along at least one axis indicating a degree of separation of each of the pieces of image data relative to the reference value, modifying, upon receiving a single piece of image data identified from the plotted and displayed pieces of image data, the decision surface based on the identified single piece of image data.

Abstract: A system and method is provided that utilizes deep learning, including convolutional neural networks, to identify subject objects in unconstrained user images such as unknown pills. An image of, e.g., a pill, may be captured and subsequently processed using deep learning models to identify the pill. The deep learning models may be optimized to have a small footprint (in terms of computational and memory resources) suitable for a resource-limited device such as a smartphone while retaining a high object recognition accuracy. Each such model may also be run on modified versions of the unconstrained image, for example on color, greyscale, and gradient images, to focus the models on different distinguishing features of the object.

Abstract: A method for classifying an occluded object includes receiving, by one or more processing circuits, an image of the object that is partially occluded by a foreign object and classifying, by the one or more processing circuits, the object of the image into one of one or more classes of interest via an artificial neural network (ANN) by determining a plurality of neuron activations of neurons of the ANN for one or more foreign classes and the one or more classes of interest, subtracting one or more of the neuron activations of the one or more foreign classes from the neuron activations of the one or more classes of interest, wherein the foreign object belongs to one of the one or more foreign classes, and classifying the object of the image into the one of the one or more classes of interest based on the subtracting.

Abstract: In some embodiments, the system is programmed to build from multiple training sets multiple digital models, each for recognizing plant diseases having symptoms of similar sizes. Each digital model can be implemented with a deep learning architecture that classifies an image into one of several classes. For each training set, the system is thus programmed to collect images showing symptoms of one or more plant diseases having similar sizes. These images are then assigned to multiple disease classes. For a first one of the training sets used to build the first digital model, the system is programmed to also include images that correspond to a healthy condition and images of symptoms having other sizes. These images are then assigned to a no-disease class and a catch-all class. Given a new image from a user device, the system is programmed to then first apply the first digital model.

Abstract: Systems and methods for enhanced training of machine learning systems based on automatically generated visually realistic gameplay. An example method includes obtaining electronic game data that includes rendered images and associated annotation information, the annotation information identifying features included in the rendered images to be learned, and the electronic game data being generated by a video game associated with a particular sport. Machine learning models are trained based on the obtained electronic game data, with training including causing the machine learning models to output annotation information based on associated input of a rendered image. Real-world gameplay data is obtained, with the real-world gameplay data being images of real-world gameplay of the particular sport. The obtained real-world gameplay data is analyzed based on the trained machine learning models. Analyzing includes extracting features from the real-world gameplay data using the machine learning models.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: A web detection system processes webpage information and performs automated feature extraction of webpages including machine processable information. In an embodiment, the web detection system determines a subset of webpages having a target characteristic by processing markup language. For a webpage of the subset, the web detection system determines that a first image overlaps at least a portion of a second image in the webpage. The web detection system generates an image of the webpage such that the portion of the second image is obscured by the first image. The web detection system determines a graphical feature of the webpage by processing the image, e.g., using optical character recognition. Responsive to determining that the graphical feature corresponds to graphical features of images of a different set of webpages associated with a target entity, the web detection system determines that the webpage is also associated with the target entity.

Abstract: A printing apparatus configured to print an image by discharging inks of different colors from a printing head onto a printing medium includes an edge extractor that extracts an edge pixel constituting a contour of the image from image data and a dot data generator that generates dot data indicating the recording state of dots corresponding to the image data.

Abstract: A system provides verification of image content in software modules within a system of software under development. Embodiments retrieve image data from a software module written in a native or first computer program type or language. The retrieved image data may be evaluated so that a conversion image space is prepared as a new coordinate space that will be used to render the image data into an image object outside of the native computer program type or language. In an exemplary embodiment, a bit-per-pixel value may be applied to the image data during conversion to a second computer program format/language. The converted image data may then be rendered into an image object in the new coordinate space according to the new readable format. The rendered image object may then be displayed to provide convenient and quick visual verification of the accuracy of the unrendered image data content.

Abstract: An electronic device includes a marker with a pattern of marks encoding product information of the electronic device. The pattern includes first marks having a first material with a first isotopic ratio of an element, and second marks having a second material with a second isotopic ratio of the element different than the first isotopic ratio.

Abstract: Embodiments are directed to rectifiers using a single bias current or bias current path to bias multiple rectifying elements. A rectifier that has multiple rectifier stages coupled together serially includes a bias current path coupled to each of the rectifier stages. Thee bias current path is configured to simultaneously bias rectifying elements in each of the rectifier stages by using a bias current to bias a first rectifying element and reusing the bias current to bias other rectifying elements.

Abstract: A system for locating an asset can include a first visible light communication (VLC) device having a first controller, a first transceiver, and a first light source, where the first transceiver has a first range, where the first controller broadcasts, using the first transceiver, a first communication signal, where the first communication signal comprises an identification of the asset. The system can also include an asset communication device associated with the asset, where the asset communication device includes a second transceiver and a second controller, where the second transceiver has a second range, where the asset communication device is within the first range, where the second controller broadcasts, using the second transceiver, a second communication signal in response to the first communication signal, where the second communication signal comprises the identification of the asset.

Abstract: One example of a device includes a radio-frequency identification (RFID) circuit and a security circuit. The security circuit disables the RFID circuit directly in response to a predetermined number of readings of the RFID circuit.