The present disclosure is related in general to image processing and a method and system for generating a multi-level classifier for image processing. An image processing system may analyze an input image of a predetermined image type to extract unique key feature descriptors associated with the input image. Further, the unique key feature descriptors are resized into a predefined standard template format which is utilized to develop an image type classifier. Furthermore, the unique key feature descriptors are resized into each of one or more template classifiers of the predetermined image type. Further, signal quality value of each of the template classifiers is determined by validating each of the unique key feature descriptors resized based on each of the template classifiers and an image prediction classifier is developed based on the signal quality value.

A deep learning based compression (DLBC) system generates a progressive representation of the encoded input image such that a client device that requires the encoded input image at a particular target bitrate can readily be transmitted the appropriately encoded data. More specifically, the DLBC system computes a representation that includes channels and bitplanes that are ordered based on importance. For a given target rate, the DLBC system truncates the representation according to a trained zero mask to generate the progressive representation. Transmitting a first portion of the progressive representation enables a client device with the lowest target bitrate to appropriately playback the content. Each subsequent portion of the progressive representation allows the client device to playback the content with improved quality.

An approach is provided for an unknown moving object detection system. The approach, for instance, involves capturing a plurality of unknown object events indicating an unknown object detected by one or more computer vision systems. The approach also involves clustering the plurality of unknown object events into a plurality of clusters based on one or more clustering parameters. The approach further involves selecting at least one cluster of the plurality of clusters based on a selection criterion. The approach further involves determining at least one operating scenario for the one or more computer vision systems based on a combination of the one or more clustering parameters associated with the selected at least one cluster.

An apparatus and method for determining driving risks of a driver using deep learning algorithms and a vehicle including the same are provided. The apparatus comprises a processor, a network interface, a memory, and a computer program loaded to the memory and executed by the processor, wherein the processor is configured to receive image data and CAN data obtained by a vehicle equipped with a lidar sensor or a camera sensor while the vehicle is driving, input the obtained image data and CAN data to a first deep learning algorithm trained through pre-stored image data to output image features related to driving risks of a driver driving the vehicle, output image features related to the driver's driving risk by the first deep learning algorithm, and capture a first image corresponding to the output image features and transmit the captured first image to a connect program.

Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Disclosed are systems and methods for improving interactions with and between computers in computerized security and content monitoring, hosting and providing devices, systems and/or platforms. The disclosed systems and methods provide a novel framework that adaptively distinguishes between known people versus unknown people based on a dynamically applied, anonymous facial recognition methodology. The disclosed framework provides such functionality by recognizing faces within captured images without storing any information or annotations regarding or revealing the captured person's identity. The framework is configured to adaptively learn to distinguish between faces seen for the first time and faces it has previously seen by locally processing a captured image and only sending face embeddings to a network location for future comparisons of subsequently, anonymously captured images.

Distributed fiber optic sensing (DFOS) systems, methods and structures for determining the proximity of vibration sources located perpendicular to a sensor fiber that is part of the DFOS system that may potentially threaten/damage or otherwise compromise the sensor fiber itself. Systems, methods, and structures according to aspects of the present disclosure employ Artificial Intelligence (AI) methodology(ies) that use as input a fundamental physical understanding of wave propagation and attenuation in the ground along with Bayesian inference and Maximum Likelihood Estimation (MLE) techniques for estimating/determining the proximity of potentially damaging vibration sources to the optical sensor fiber.

The systems and methods disclosed herein include a path integration system that calculates optic flow, infers angular velocity from the flow field, and incorporates this velocity estimate into heading calculations. The resulting system fuses heading estimates from accelerometers, gyroscopes, engine torques, and optic flow to determine self-localization. The system also includes a motivational system that implements a reward drive, both positive and negative, into the system. In some implementations, the drives can include: a) a curiosity drive that encourages exploration of new areas, b) a resource drive that attracts the agent towards the recharging base when the battery is low, and c) a mineral reward drive that attracts the agent towards previously explored scientific targets.

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.

A disclosed face detection system (and method) is based on a structure of a convolutional neural network (CNN). One aspect concerns a method for automatically training a CNN for face detection. The training is performed such that balanced number of face images and non-face images are used for training by deriving additional face images from the face images. The training is also performed by adaptively changing a number of trainings of a stage according to automatic stopping criteria. Another aspect concerns a system for performing image detection by integrating data at different scales (i.e., different image extents) for better use of data in each scale. The system may include CNNs automatically trained using the method disclosed herein.