Abstract: Embodiments described herein provide systems and processes for scene-aware object detection. This can involve an object detector that modulates its operations based on image location. The object detector can be a neural network detector or a scanning window detector, for example.

Abstract: A control apparatus is provided, including: an other-vehicle emotion acquiring unit configured to acquire an other-vehicle emotion indicating an emotion of an occupant of a second vehicle different from a first vehicle; a determination unit configured to determine whether to perform notification to an occupant of the first vehicle based on the other-vehicle emotion; and a notification control unit configured to perform control to notify the occupant of the first vehicle of notification information based on the other-vehicle emotion when the determination unit determines to perform the notification.

Abstract: An Artificial Intelligence (AI) roof deterioration analysis system that tracks changes in roofs over time by evaluating a series of features in high-resolution images.

Abstract: Front end systems and related devices, integrated circuits, modules, and methods are disclosed. One such front end system includes a low noise amplifier in a receive path and a multi-mode power amplifier circuit in a transmit path. An overload protection circuit can adjust an impedance of a switch coupled to the low noise amplifier based on a signal level of the low noise amplifier. The multi-mode power amplifier circuit includes a stacked output stage including a transistor stack of two or more transistors. The multi-mode power amplifier circuit also includes a bias circuit configured to control a bias of at least one transistor of the transistor stack based on a mode of the multi-mode power amplifier circuit. Other embodiments of front end systems are disclosed, along with related devices, integrated circuits, modules, methods, and components thereof.

Abstract: A learning apparatus for creating an emotion expression video according to an embodiment disclosed include first generative adversarial networks (GAN) that receive text for creating an emotion expression video, extract vector information by performing embedding on the input text, and create an image based on the extracted vector information, and second generative adversarial networks that receive an emotion expression image and a frame of comparison video, and create a frame of emotion expression video from the emotion expression image and the frame of comparison video.

Abstract: In a general aspect, a rate at which MIMO transmissions are elicited from wireless communication devices is varied. A first wireless communication device may be configured to wirelessly transmit a first set of messages at a first transmission rate to a second wireless communication device. The first wireless communication device may further be configured to receive MIMO transmissions from the second wireless communication device. The first wireless communication device may additionally be configured to generate first channel information based on respective training fields in each of the first MIMO transmissions; determine a rate at which the first channel information is generated; vary the first transmission rate to a second, different transmission rate based on the rate at which the first channel information is generated; and wirelessly transmit a second set of messages at the second transmission rate to the second wireless communication device.

Abstract: Methods described herein relate to disambiguating objects for a particular location. Methods may include receiving, from a vehicle, an indication of an object associated with an object classification for the object in view of the vehicle. The object classification is further associated with an object type. The method further includes receiving, from the vehicle, information pertaining to one or more other objects of the same object type within a predefined distance of the object; determining an expectation count for the object type based at least in part on a count of the object and the one or more objects of the same object type for the particular location; and disambiguating one or more object classifications from a plurality of object classifications based at least in part on the expectation count. A corresponding apparatus and computer program product are also provided.

Abstract: The present disclosure provides a signal processing device that includes: a signal collector configured to obtain an image to be processed, a signal collector configured to collect an input signal, an instruction converter configured to convert the signal into an image processing instruction according to a target signal instruction conversion model, an image processor configured to edit the image to be processed according to the image processing instruction and a target image processing model to obtain a result image. Examples taught in the present disclosure implements a user command to process images, which saves users' time spent in learning image processing software prior to image processing and improves user experience.

Abstract: Various embodiments of the disclosure provide apparatuses, systems, and computer program products for predictive data labelling using a dual-model system. Embodiments provide various advantages in accuracy of predicted labels, for example in various contexts such as medical data analysis for difficult to diagnose diseases.

Abstract: A method and apparatus for processing a video is provided. A current image frame of a video and a mask for a first area are obtained. The Image included in the first area is to be removed from the current image frame. A first resultant image of the current image frame is obtained with an image processing network based on the current image frame and the mask. The first resultant image is an image from which the image included in the first area is removed from the current image frame. A second resultant image of a previous image frame of the current image frame is obtained. The second resultant image is an image from which the image included in the second area is removed from the previous image frame. A final resultant image of the current image frame is obtained by fusing the first resultant image and the second resultant image.

Abstract: A learning device includes one or more processors. The processors calculate a likelihood of belonging to a plurality of estimated classes, of learning data, by using an estimation model for estimating to which of the estimated classes input data belongs. The processors calculate a weight of a loss function to be used in learning the estimation model such that, when a likelihood of a first class that is closer to correct data than other estimated classes among the estimated classes and likelihoods of a second class and a third class that are adjacent to the first class are applied to a function having a predetermined shape, a position that has an extreme value of the function corresponds to the correct data. The processors learn the estimation model by using the loss function.

Abstract: A system for infrastructure system calibration includes a sensor configured to be mounted to an infrastructure component and configured to detect an object. A corner reflector has an optical pattern and is arranged within a field of view of the sensor. The corner reflector has three surfaces that meet at a point.

Abstract: Methods and systems for training a model labeling two or more organic structures within an image. One method includes receiving a set of training images. The set of training images including a first plurality of images and a second plurality of images. Each of the first plurality of images including a label for each of the two or more organic structures and each of the second plurality of images including a label for only a subset of the two or more organic structures. The method further includes training the model using the first plurality of images, the second plurality of images, and a label merging function mapping a label from the first plurality of images to a label included in the second plurality of images.

Abstract: An improved method of performing object segmentation and classification that reduces the memory required to perform these tasks, while increasing predictive accuracy. The improved method utilizes a capsule network with dynamic routing. Capsule networks allow for the preservation of information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for the reconstruction of an input image from output capsule vectors. The present invention expands the use of capsule networks to the task of object segmentation and medical image-based cancer diagnosis for the first time in the literature; extends the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules; extends the masked reconstruction to reconstruct the positive input class; and proposes a capsule-based pooling operation for diagnosis.

Abstract: A video capture and processing system includes a memory configured to store a pose database. The pose database includes poses that indicate a start or stoppage in an event. The system also includes a processor operatively coupled to the memory. The processor is configured to generate a pose of an individual in a video frame of captured video of the event. The pose can be three-dimensional pose or a two-dimensional pose. The processor is also configured to determine, based on the pose database, whether the pose of the individual indicates a start or a stoppage in the event. The processor is further configured to control an upload of video of the event based on the determination of whether the pose indicates the start or the stoppage in the event.

Abstract: An image-processing device includes: a reliability calculation unit configured to calculate reliability of a character recognition result for a document image which is a character recognition target on the basis of a descriptive feature amount of a character string of a specific item included in the document image; and an image output unit configured to output an image of the character recognition result indicating the character string of the specific item in a display mode in accordance with the reliability.

Abstract: A pupil detection device includes: a filter weight learner configured to generate a target image on the basis of pupil coordinate data acquired from a learning image and learn weights of a plurality of filters in order to generate a filtered image, by filtering the learning image using the plurality of filters, that is within a predetermined reference range of the target image; a split image generator configured to generate a pupil region split image for an input image using the plurality of filters having the learned weights; and a pupil coordinate detector configured to remove noise of the pupil region split image, select at least one of a plurality of pixels from which noise is removed, and detect pupil coordinates.

Abstract: A method for identifying a mechanical impedance of an electromagnetic load may include generating a waveform signal for driving an electromagnetic load and, during driving of the electromagnetic load by the waveform signal or a signal derived therefrom, receiving a current signal representative of a current associated with the electromagnetic load and a back electromotive force signal representative of a back electromotive force associated with the electromagnetic load. The method may also include implementing an adaptive filter to identify parameters of the mechanical impedance of the electromagnetic load, wherein an input of a coefficient control for adapting coefficients of the adaptive filter is a first signal derived from the back electromotive force signal and a target of the coefficient control for adapting coefficients of the adaptive filter is a second signal derived from the current signal.

Abstract: Embodiments related to systems and methods for determining an identity of a trailer coupled to a vehicle are provided herein. The system includes one or more trailer lamps, a trailer lamp current sensing device electrically coupled to the one or more trailer lamps, one or more processors, one or more data storage devices, one or more memory modules communicatively coupled to the one or more processors and storing machine readable instructions that cause the system to perform at least the following when executed by the one or more processors: receive a sensed current signal from the trailer lamp current sensing device, determine whether an amperage level of the sensed current signal is associated with a trailer identification stored within the one or more data storage devices, and notifying a user of the trailer identification stored in the one or more data storage devices for the trailer connected to the vehicle.

Abstract: A base station for a wireless communications system operating in an unlicensed target band of a wireless medium includes a receiver configured to wirelessly (i) receive non-cooperative carrier data within the target band from a user equipment, and (ii) detect an operation of at least one spread spectrum channel within the target band. The station further includes a transmitter configured to wirelessly send non-cooperative carrier data within the target band to the user equipment, a memory configured to store computer-executable instructions, and a processor configured to (i) execute the computer-executable instructions, (ii) determine, based on the detection operation of the receiver, at least one sequence of the at least one spread spectrum channel, and (iii) perform at least one corrective action to mitigate interference between the transmitter and an operation of the at least one spread spectrum channel based on the determination of the at least one sequence.