Abstract: An image tracking method of the present invention includes the following steps: (A) obtaining a plurality of original images by using an image capturing device; (B) transmitting the plurality of original images to a computing device, and generating a position box based on a preset image set; (C) obtaining an initial foreground image including a target object, and an identified foreground image is determined based on a pixel ratio and a first threshold; (D) obtaining a feature and obtaining a first feature score based on the feature of the identified foreground images; and (E) generating a target object matching result based on the first feature score and a second threshold, and recording a moving trajectory of the target object based on the target object matching result.

Abstract: An image processing apparatus includes a low-resolution image generating circuit configured to generate a low-resolution image including a second pixel corresponding to first pixels based on an input image including the first pixels, and an edge preserving smoothing circuit configured to generate a reliability of the second pixel based on characteristics of values of the first pixels and perform edge preserving smoothing on the input image using a value of the second pixel of which a reflection ratio is adjusted, based on the reliability of the second pixel.

Abstract: An information processing device includes: a processor configured to: obtain sensor data; determine partial data; input the sensor data to an input layer of a first neural network recognition model; input the partial data to an input layer of a second neural network recognition model; align second intermediate output data from a second intermediate layer of the second neural network recognition model, relative to first intermediate output data from a first intermediate layer of the first neural network recognition model; generate intermediate input data from the first intermediate output data and the second intermediate output data aligned; and input the intermediate input data to a third intermediate layer of the first neural network recognition model subsequent to the first intermediate layer.

Abstract: Embodiments of the present disclosure provide systems and methods for recognizing a masked face. According to the present disclosure, the disclosed systems and methods include features that provide augmentation of existing face recognition databases, real-time mask detection, and real-time masked face recognition. In embodiments, masked face recognition includes a multi-layered approach, which includes finding matching simulated masked faces in the database that match the masked face being analyzed, comparing the unmasked portion of the masked face to stored unmasked faces in a database to identify any matches, and executing face restoration algorithms in which the masked portion is reconstructed to generate an unmasked representation which may then be matched against unmasked faces in the database.

Abstract: Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

Abstract: Disclosed herein are system, method, and computer program product embodiments for securing ATM transactions based on facial detection. The approach contemplates performing facial detection on an image of a user of an ATM when the user authenticates themselves, and comparing the user's detected face to facial detection results of subsequent images taken at the ATM. Based on this comparison, various security events may be detected and, for any given security event, a particular action may be taken.

Abstract: A computer-implemented method for detecting fraudulent behavior in a facial recognition process includes: receiving, by a computing device, a facial recognition request from a user; collecting bypass information of the user, in which the bypass information includes user device information and user behavior information; inputting the bypass information into at least one decision model to obtain a bypass decision result; and determining, based on the bypass decision result, whether fraudulent behavior is present in the facial recognition process.

Abstract: The present disclosure provides a processor, a display driving circuit, and an electronic device. The processor includes: a memory being divided into a first part that stores correction data for correcting an image signal, a second part that stores data of an image signal to be displayed, and a third part that stores data of an application to be executed; and an output port configured to transmit the data of the image signal and the correction data stored in the memory to an external display driving circuit, separately.

Abstract: There are provided systems and methods for performing image compensation using image enhancement effects. In one implementation, such a system includes a computing platform having a hardware processor and a memory storing an image compensation software code. The hardware processor is configured to execute the image compensation software code to receive image data corresponding to an event being viewed by a viewer in a venue, the image data obtained by a wearable augmented reality (AR) device worn by the viewer, and to detect a deficiency in an image included in the image data. The hardware processor is further configured to execute the image compensation software code to generate one or more image enhancement effect(s) for compensating for the deficiency in the image and to output the image enhancement effect(s) for rendering on a display of the wearable AR device while the viewer is viewing the event.

Abstract: A method includes retrieving, by a device, contextual information based on at least one of an image, the device, user context, or a combination thereof. At least one model is identified from multiple models based on the contextual information and at least one object recognized in an image based on at least one model. At least one icon is displayed at the device. The at least one icon being associated with at least one of an application, a service, or a combination thereof providing additional information.

Abstract: Systems, methods, devices, media, and computer-readable instructions are described for local image tagging and processing in a resource-constrained environment such as a mobile device. In some embodiments, characteristics associated with images are used to determine whether to store content (e.g., images and video clips) as ephemeral content or non-ephemeral content. Based on the determination, the image is stored in a non-ephemeral camera roll storage of the mobile device, or an ephemeral local application storage. Additional storage operations such as encryption or backup copying may additionally be determined and performed based on the analysis of the content. In some embodiments, such images may be indexed, sorted, and searched based on the image tagging operations used to characterize the content.

Abstract: Aspects of the technology described herein improve an object recognition system by specifying a type of picture that would improve the accuracy of the object recognition system if used to retrain the object recognition system. The technology described herein can take the form of an improvement model that improves an object recognition model by suggesting the types of training images that would improve the object recognition model's performance. For example, the improvement model could suggest that a picture of a person smiling be used to retrain the object recognition system. Once trained, the improvement model can be used to estimate a performance score for an image recognition model given the set characteristics of a set of training of images. The improvement model can then select a feature of an image, which if added to the training set, would cause a meaningful increase in the recognition system's performance.

Abstract: One embodiment provides a method comprising classifying one or more objects present in an input comprising visual data by executing a first set of models associated with a domain on the input. Each model corresponds to an object category. Each model is trained to generate a visual classifier result relating to a corresponding object category in the input with an associated confidence value indicative of accuracy of the visual classifier result. The method further comprises aggregating a first set of visual classifier results based on confidence value associated with each visual classifier result of each model of the first set of models. At least one other model is selectable for execution on the input based on the aggregated first set of visual classifier results for additional classification of the objects. One or more visual classifier results are returned to an application running on an electronic device for display.

Abstract: A method, system, and neural network for identifying direction of a document where the method comprises: extracting a text line in the document; calculating a first normal direction result indicative of the text line probably being in a normal direction and a first upside-down direction result indicative of the text line probably being in a direction upside-down with respect to the normal direction; calculating a second normal direction result indicative of the text line after being rotated by 180 degrees probably being in the normal direction and a second upside-down direction result indicative of the text line after being rotated by 180 degrees probably being in the direction upside-down with respect to the normal direction; and determining the direction of the document according to the first normal direction result, the first upside-down direction result, the second normal direction result and the second upside-down direction result.

Abstract: A face identification system includes a transmitter, a receiver, a database, an artificial intelligence chip, and a main processor. The transmitter is used for emitting at least one first light signal to an object. The receiver is used for receiving at least one second light signal reflected by the object. The database is used for saving training data. The artificial intelligence chip is coupled to the transmitter, the receiver, and the database for identifying a face image from the object according to the at least one second light signal and the training data. The main processor is coupled to the artificial intelligence chip for receiving a face identification signal generated from the artificial intelligence chip.

Abstract: A method of assessing damage to a component includes displaying a sensor image of the component in a first viewing pane, displaying a reference image of the component, which is a graphical depiction of the component with accurate dimensions, in a second viewing pane, placing a plurality of first identification markers on the sensor image of the component in the first viewing pane to correspond to a matching location with a second identification marker on the component in the reference image, identifying a region of damage on the component in the sensor image, mapping the region of damage to the component in the reference image using the plurality of first and second identification markers, and calculating a size of the region of damage.

Abstract: Embodiments of the present disclosure relate to a recurrent framework based on Recurrent Highway Networks (RHNs) for sequence modeling using batch normalization. In certain embodiments, constraints within the RHNs are relaxed to reduce or avoid gradient vanishing or exploding by normalizing the current transition units in highway layers.

Abstract: Embodiments of the present disclosure provide an iris recognition method applied for an electronic device and includes: capturing a plurality of face image frames of an object to be recognized; determining whether a face contour of the object to be recognized changes based on the plurality of face image frames; and capturing an iris image of the object to be recognized to perform iris recognition when the face contour changes. Further, embodiments of the present disclosure provide an electronic device and a computer-readable storage medium.

Abstract: This disclosure relates to an apparatus and a method for 3D printing. The 3D object detection apparatus includes one or more processing circuit configured to acquire a robust shape value of a 3D object; match the robust shape value of the 3D object with a robust shape value of a 3D sample in a 3D sample database; determine a robust design value of the 3D object based on a robust design value of the matched 3D sample; and detect the 3D object based on a result of the matching process for the robust shape value and the determined robust design value of the 3D object. With the apparatus and the method for 3D printing according to the disclosure, an owner, a designer and a manufacturer of a 3D model can protect the 3D model from infringing intellectual property.

Abstract: Provided is an apparatus for encoding and transcoding which applies a multi-format ultra-high definition high-efficiency codec, the apparatus being capable of encoding and transcoding, in one codec apparatus, uncompressed ultra-high definition broadcast content and compressed ultra-high definition broadcast content as ultra-high definition digital broadcast signals, and also being capable of transmitting the encoded digital broadcast signals in various transmission formats such as ASI and IP streams.