Abstract: A method, apparatus, device and storage medium for recognizing a bill image may include: performing text detection on a bill image, and determining an attribute information set and a relationship information set of each text box of at least two text boxes in the bill image; determining a type of the text box and an associated text box that has a structural relationship with the text box based on the attribute information set and the relationship information set of the text box; and extracting structured bill data of the bill image, based on the type of the text box and the associated text box that has the structural relationship with the text box.

Abstract: Provided is an information processing device that includes a movement estimation unit that estimates position and posture movement by using corresponding-point information obtained based on image capturing, and a corresponding-point information selection unit that selects the corresponding-point information to be used by the movement estimation unit from among a plurality of pieces of corresponding-point information. The plurality of pieces of corresponding-point information includes first corresponding-point information obtained based on image capturing by a first camera set including a first camera and a second camera, and second corresponding-point information obtained based on image capturing by a second camera set including a third camera and a fourth camera. The first camera set has a wider view angle than that of the second camera set. The first camera set and the second camera set have image capturing ranges that are at least partially identical to each other.

Abstract: A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

Abstract: A method of a mobile device configured to photograph an object includes controlling a display panel to display an image, a touch panel to receive a user input, and a network interface to wirelessly communicate with an external network, unlocking the mobile device when the mobile device is in an area condition, a wireless communication condition, and a time condition, and unlocking the mobile device according to a password when the mobile device is not in the time condition.

Abstract: Systems and methods for automatic identification and presentation of edges, shapes and unique objects in an image used for machine vision job setup are disclosed herein. An example method includes receiving, by one or more processors, an image file. The method further includes automatically determining, by the one or more processors, an item of interest within the image file. The method further includes analyzing, by the one or more processors, the item of interest to determine an appropriate tool for processing the item of interest. The method further includes displaying, by the one or more processors, on a display screen: (i) an image corresponding to the image file, (ii) an indication of the item of interest, and (iii) an indication of the appropriate tool.

Abstract: A 3D image sliced into a plurality of slices including the first slice on which a label is annotated and a plurality of second slices on which the label is not annotated is provided as a training sample. A computing device trains a neural network based on the first slice, determines an expandable second slice which is expandable from the first slice from among the plurality of second slices based on the trained neural network; and trains the neural network based on expanded slices including the expandable second slice.

Abstract: A mobile device includes a user interface including a display panel to display a menu image and a touch panel to receive a password, a network interface to detect an external device, a functional unit to sense a motion of the mobile device and to photograph a user, and a control unit to control the user interface, the network interface, the memory, and the functional unit, to unlock the mobile device according to a user identification process of identifying an authorized user, when the mobile device is in a time condition, in response to the sensed motion, and to unlock the mobile device according to the password when the mobile device is not in the time condition, in response to the sensed motion.

Abstract: Embodiments provide improved techniques for identifying object and vehicle locations on an HD map using camera images. Synthetic data may be generated by populating a high-definition (HD) map with an object. The populated HD map can be projected to a two-dimensional camera view image depicting the object. The HD map and object detection data generated from image (e.g., identifying lane/object locations within the image) can be used to train the model to identify HD map locations of an autonomous vehicle capturing images as it travels, as well as various objects detected from those captured images. Subsequently, a new image may be processed using object detection techniques to detect lane/object locations within the image. An HD map and the detected lane/object data may be provided to the model, which in turn identifies, on the HD map, the locations of the vehicle and the various objects.

Abstract: An ultrasound diagnostic apparatus 1 includes a bladder pattern storage unit 22, a reference pattern setting unit 21, a bladder extraction unit 18 that extracts a bladder region from an ultrasound image, a bladder extraction success/failure determination unit 19 that determines whether the bladder region represents a bladder having the reference pattern, and an image quality adjustment unit 20 that adjusts the image quality of the ultrasound image in a case where determination is made that the bladder region does not represent the bladder having the reference pattern, in which in a case where the determination is made that the bladder region does not represent the bladder having the reference pattern even in an ultrasound image of which the image quality is adjusted, the bladder extraction success/failure determination unit 19 determines whether the bladder region represents the bladder having the abnormal bladder pattern.

Abstract: A first tone curve referred to in generating an image recognition processing signal as an image output signal for image recognition processing and a second tone curve referred to in generating a visual recognition signal as an image output signal for visual recognition are separately generated. The second tone curve for visual recognition is generated by connecting a dark control point, which is a control point specified in a luminance-pixel value coordinate system corresponding to the lowest luminance portion in a luminance histogram, a light control point, which is a control point specified in the luminance-pixel value coordinate system corresponding to the highest luminance portion in the luminance histogram, and an intermediate control point, which is a control point specified in the luminance-pixel value coordinate system corresponding to an intermediate luminance portion in the luminance histogram.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: There is a growing problem in correctional facilities in which contraband devices may be smuggled into a correctional facility. These devices are not subject to the typical monitoring and control provided by the inmate communication system provided by the correctional facility, and thus pose a significant security risk. The present disclosure provides details of a system and method that leverages the sanctioned mobile devices and the wireless infrastructure of the inmate communication system to scan for and triangulate the location of contraband devices. The mobile and wireless infrastructure devices scan for transmissions indicative of a contraband device and send alerts to each other and to a central monitoring system. The central monitoring system then leverages multiple alerts to determine a location of the contraband device using multiple triangulation techniques. The triangulation may also be performed by the mobile devices themselves.

Abstract: A method for identifying individual objects and their number and respective locations in a sealed case creates a detection model by using standard data as a training set. Image of the sealed case is captured and divided into a specified number of detection regions. Images of first detection regions in the detection image as taken as local images and input into the detection model. The numbers and locations of target objects in the sealed case is obtained based on a statistic result outputted by the detection model. The standard data includes sample images of the sealed case with different numbers and respective locations of the target objects in the case. A capacity of the sealed case is M, and a number of the sample image is less than 2M. A target identification apparatus and a storage medium applying the method are also disclosed.

Abstract: An image acquisition section of an information processing apparatus acquires, from an imaging apparatus, polarization images in a plurality of orientations. A normal acquisition section of an image analysis section detects a surface of a subject by acquiring a normal vector on the basis of orientation dependence of polarization luminance. A surface assumption section assumes the presence of an undetected surface continuing from the detected surface. A subject detection section confirms whether or not the assumed surface is present using a normal vector estimated for the assumed surface. An output data generation section generates and outputs output data using information regarding the detected surface.

Abstract: Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using contrastive learning. One of the methods includes obtaining a network input representing an environment; processing the network input using a first subnetwork of the neural network to generate a respective embedding for each location in the environment; processing the embeddings for each location in the environment using a second subnetwork of the neural network to generate a respective object prediction for each location; determining, for each of a plurality of pairs of the plurality of locations in the environment, whether the respective object predictions of the pair of locations characterize the same possible object or different possible objects; computing a respective contrastive loss value for each of the plurality of pairs of locations; and updating values for a plurality of parameters of the first subnetwork using the computed contrastive loss values.

Abstract: System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.

Abstract: System, methods, and embodiments described herein relate to predicting a future state of an object detected in a vicinity of a vehicle. In one embodiment, a method for predicting a state of an object includes detecting, at a plurality of discrete times [t, t?1, t?2, . . . ], a respective plurality of states of the object, obtaining, based at least in part on a present location of the vehicle, supplemental information, associated with an environment of the present location, that indicates at least a speed reduction factor, executing a prediction operation to determine a predicted state of the object at a time t+1 based at least in part on the detected plurality of states and the supplemental information, determining an actual state of the object at a time t+1 based on data from the one or more sensors, and modifying the prediction operation based at least in part on the actual state.

Abstract: Provided is a face recognition method comprising acquiring a masked face image including a masked region and an un-masked region; obtaining an image feature from the masked face image; inputting the image feature to a pre-trained segmentation model to automatically estimate a feature of the masked region; and refining the image feature using the estimated feature of the masked region, wherein the refining step comprising focusing on a feature of the un-masked region and discarding the estimated feature of the masked region.

Abstract: A neural network can be configured to produce an electronic road map. The electronic road map can have information to distinguish lanes of a road. A feature in an image can be detected. The image can have been produced at a current time. The image can be of the road. The feature in the image can be determined to correspond to a feature, of a plurality of features, in a feature map. The feature map can have been produced at a prior time from one or more images. A labeled training map can be produced from the feature in the image and the plurality of features in the feature map. The labeled training map can have the information to distinguish the lanes of the road. The neural network can be trained to produce, in response to a receipt of the image and the feature map, the labeled training map.