Abstract: In the case where a three-dimensional position of a moving body is calculated based on image data taken by a plurality of cameras that are synchronized, high performance equipment and system such as a system to make the synchronization among the plurality of cameras and a camera having a built-in function to make the synchronization are required. It is also required to fix the camera position with high accuracy beforehand. It is made possible to calculate a trajectory of a moving body as a target in the three-dimensional space using image data taken by a plurality of cameras that are non-synchronized mutually, thereby solving the above issue. And positions of respective cameras are calculated in the three-dimensional space from a plurality of reference points having fixed position coordinates in the three-dimensional space that are commonly shown in the image data of the respective cameras, thereby solving the above issue.

Abstract: An abnormality determination apparatus is mounted on a vehicle. A person is identified in a captured image acquired from a camera that captures a range including a driver seat of the vehicle. It is determined whether a person other than the driver of the vehicle is present in an operable range set as a range in which a steering wheel of the vehicle is enabled to be operated, based on a result from identifying a person. An abnormality process assuming that the driver of the vehicle is in an abnormal state is executed in response to determining that the person other than the driver is present in the operable range.

Abstract: A model constructing method for a neural network model applicable for image recognition processing is disclosed. The model constructing method includes the following operation: updating, by a processor, a plurality of connection variables between a plurality of layers of the neural network model, according to a plurality of inputs and a plurality of outputs of the neural network model. The plurality of outputs represent a plurality of image recognition results. The plurality of connection variables represent a plurality of connection intensities between each two of the plurality of layers.

Abstract: Technologies are provided for assessing liveness of a subject presented to a set of one or more cameras. A first image and a second image depicting a face are captured during a first time period, and a third image and a fourth image depicting the face are captured during a second time period. A first variance in facial appearance is detected between the first depiction of the face and the third depiction of the face, and a second variance in facial appearance is detected between the second depiction of the face and the fourth depiction of the face. A liveliness of a person is verified based on a determination that the first variance in facial appearance corresponds in time to the second variance in facial appearance.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of an inspection system. Thereafter, a representation for each image is generated using a first machine learning model and based on the received data. Later, one or more second machine learning models can cluster the images using the corresponding representations into groups that each correspond to one of a plurality of different object attributes. Thereafter, access to the groups can be provided to a consuming application or process for analysis and the like. In some variations, the representations are analyzed by at least one third machine learning model prior to the clustering. In other variations, the representations are analyzed by at least one third machine learning model after the clustering. Related apparatus, systems, and methods are also described.

Abstract: Provided are a method and an apparatus for recognizing a gesture, an electronic device and a storage medium. In one or more embodiments, the method includes: detecting at least one hand region from a video image and obtaining hand image information of each of the at least one hand region; obtaining hand motion information of each of the at least one hand region by tracking the at least one hand region; determining a gesture corresponding to each of the at least one hand region according to the hand image information and/or the hand motion information of each of the at least one hand region; wherein the gesture comprises at least one of a single-hand static gesture, a single-hand dynamic gesture, a double-hand static gesture or a double-hand dynamic gesture.

Abstract: An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: A method of detecting at least one element of interest visible on an input image, by means of a convolutional neural network, CNN, the method comprises the steps of: (a) extracting, by means of an ascending branch of a first subnetwork of said CNN of feature pyramid network, FPN, type, a plurality of initial feature maps (C1, C2, C3, C4, C5) representative of the input image at different scales, said FPN further comprising a descending branch and lateral connections between the ascending branch and the descending branch, at least one lateral connection comprising an attention module; (b) generating, by means of said descending branch of the FPN, a plurality of enriched feature maps (P2, P3, P4, P5) also representative of the input image at different scales, each enriched feature map (P2, P3, P4, P5) incorporating the information from the initial feature maps (C1, C2, C3, C4, C5) of smaller or equal scale; (d) detecting at least one element of interest visible on an input image, by means of a second subnetw

Abstract: Provided is an object recognition device for performing object recognition on a field of view (FoV). The object recognition device includes a light detection and ranging (LiDAR) data acquisition module configured to acquire data for the FoV from a sensor configured to project the FoV with a laser and receive reflected light, and a control module configured to perform object recognition on an object of interest in the FoV using an artificial neural network, wherein the control module includes a region of interest extraction module configured to acquire region of interest data based on acquired intensity data for the FoV, and an object recognition module configured to acquire object recognition data using an artificial neural network, and recognize the object of interest for the FoV.

Abstract: A driver assistance system of an ego vehicle is operated. The ego vehicle has at least one surroundings sensor for detecting the surroundings of the ego vehicle. Movements of multiple vehicles are detected with the at least one surroundings sensor in the surroundings of the ego vehicle. A movement model is generated based on the detected movements of the respective vehicles. A traffic situation is ascertained and a probability of correct classification of the traffic situation on the basis of the generated movement model by a machine learning method. The traffic situation and the probability of the correct classification of the traffic situation are ascertained by the machine learning method on the basis of the learned characteristic features of the movement model. The driver assistance system of the ego vehicle is adapted to the ascertained traffic situation.

Abstract: An identity document can be authenticated using format data of a barcode on the document, such as a barcode on a driver's license. Scan data is obtained by decoding a plurality of barcodes. Format features of the plurality of barcodes are extracted. Scan data is classified into two or more clusters. Each cluster is characterized by a set of format features extracted from the scan data. A barcode on an ID to be verified is scanned. Format features from the barcode of the ID to be verified is compared to at least one of the two or more clusters to authenticate the ID.

Abstract: A handwriting input apparatus that displays stroke data handwritten based on a position of an input unit contacting a touch panel, includes circuitry configured to implement a handwriting recognition control unit for recognizing stroke data and converting the stroke data into text data, and an authentication control unit for authenticating a user based on the stroke data, and a display unit for displaying a display component for receiving a signature together with the text data when the authentication control unit determines that a user has been successfully authenticated.

Abstract: A method of surface splash scoring includes receiving, at an electronic device, a set of camera frames corresponding to images of a water surface. The electronic device processes the set of camera frames with a trained machine learning model to generate one or more quantifications associated with fish activity proximate the water surface. In some embodiments, a surface splash score is computed that represents an appetite level anticipated to be exhibited for a first time period. Subsequently, the electronic device generates an output indicative of the surface splash score.

Abstract: A clustering method includes configuring a network with clustering target objects; collecting significance of the clustering target objects; performing network embedding for outputting a set of vectors representing neighboring objects of the clustering target objects constituting the network using a neural network; and performing clustering on the clustering target objects using the set of vectors and information on each of the clustering target objects, wherein the neural network is trained so that neighboring objects having high significance are output with higher probability.

Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Abstract: A method and system of signature verification that includes receiving a signature-in-question, extracting at least one of an image of the signature-in-question and a trajectory of the signature-in-question from the received signature-in-question, accessing a reference signature, extracting at least one of a reference image and a reference trajectory from the accessed reference signature, comparing the at least one of the image of the signature-in-question and trajectory of the signature-in-question to the at least one of the reference image and reference trajectory, and determining whether signature-in-question is authentic based on the comparison.

Abstract: In one embodiment, a method includes receiving sensor data of an environment of the vehicle generated by one or more sensors of the vehicle, the sensors comprising a camera, identifying, based on the sensor data, one or more objects in a field of view of the camera and one or more object types that correspond to the one or more objects, determining one or more target histograms that correspond to the object types, generating a processed image based on an image captured by the camera, wherein the processed image has a histogram based on the target histograms, and using the processed image to determine state information associated with the objects. The processed image may be generated by processing the image captured by the camera using a histogram matching algorithm to generate the histogram of the processed image based on the target histograms.

Abstract: An image processing device includes a document image extraction unit, a business card array image detection unit, and a business card image separation unit. The document image extraction unit extracts one document image in a read image obtained by scanning one or a plurality of documents placed on a platen glass of an image reading device. When the document is a plurality of business cards arranged without gaps, the business card array image detection unit detects the document image as a business card array image in which a plurality of business card images are arranged without gaps, based on at least one of the distribution of character string objects and the distribution of character sizes in the document image. The business card image separation unit separates the detected business card array image into the plurality of business card images.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: In some embodiments, reduction of computational resource usage related to image labeling and/or segmentation may be facilitated. In some embodiments, a collection of images may be used to train one or more prediction models. Based on a presentation of an image on a user interface, an indication of a target quantity of superpixels for the image may be obtained. The image may be provided to a first prediction model to cause the prediction model to predict a quantity of superpixels for the image. The target quantity of superpixels may be provided to the first model to update the first model's configurations based on (i) the predicted quantity and (ii) the target quantity. A set of superpixels may be generated for the image based on the target quantity, and segmentation information related to the superpixels set may be provided to a second prediction model to update the second model's configurations.