Abstract: A method and information storage media having instructions stored thereon for supervised Deep Learning (DL) systems to learn directly from unlabeled data without any user annotation. The annotation-free solutions incorporate a new learning module, the Localization, Synthesis and Teacher/Annotation Network (LSTN) module, which features a data synthesis and generation engine as well as a Teacher network for object detection and segmentation that feeds the processing loop with new annotated objects detected from images captured at the field. The first step in the LSTN module learns how to localize and segment the objects within a given image/scene following an unsupervised approach as no annotations about the objects' segmentation mask or bounding box are provided.

Abstract: An exemplary aspect relates to the field of pattern recognition, and in one exemplary embodiment to the field of image recognition. More specifically, an embodiment relates to the use of deep neural networks for image recognition and how these kinds of pattern classification structures may be augmented in order to become aware of the available computation time, and the available computational resources so as to appropriately adjust the computational complexity of their associated algorithms and consequently their need for computing resources. The methods and systems described herein at least enable more economical and flexible implementations for porting to embedded computing frameworks by respecting their computational resources.

Abstract: The disclosed system incorporates a new learning module, the Learning Kernel Activation Module (LKAM), at least serving the purpose of enforcing the utilization of less convolutional kernels by learning kernel activation rules and by actually controlling the engagement of various computing elements: The exemplary module activates/deactivates a sub-set of filtering kernels, groups of kernels, or groups of full connected neurons, during the inference phase, on-the-fly for every input image depending on the input image content and the learned activation rules.

Abstract: An exemplary embodiment relates to the field of Automatic Face Recognition (AFR) systems. More specifically one exemplary embodiment relates at least to a method and a system capable of recognizing the face of a person using a device equipped with a camera of any kind and an associated computer, such as an embedded computer. The system is alternatively suitable to be implemented as an embedded system with minimal processing hardware capabilities, consuming very low power.

Abstract: An exemplary embodiment relates to the field of Automatic Face Recognition (AFR) systems. More specifically one exemplary embodiment relates at least to a method and a system capable of recognizing the face of a person using a device equipped with a camera of any kind and an associated computer, such as an embedded computer. The system is alternatively suitable to be implemented as an embedded system with minimal processing hardware capabilities, consuming very low power.

Abstract: In the event that a moving body (e.g. a person, a car, etc.) is outfitted with a video camera or with a camera-equipped device (e.g. a tablet or a mobile phone), the system described in one aspect is able to understand the motion of the moving by analyzing the video frame sequence captured by the camera. This means that the system can categorize the motion of the body-carrying camera to one of several types (e.g., is this a person walking? is this a person running? etc.), understand the nature of the moving body holding the camera-equipped device (e.g. Is this a car?, Is this a person? etc.) and even to identify the moving body (which car?, which person? etc.).

Abstract: A stand-alone computer-camera system capable of extracting car-plate information. This is achieved by using an on-board computer in order to analyze the video stream recorded by the camera sensor, and can be used with any type of camera sensor. The system features specific characteristics making it extremely fast and able to catch plates of cars moving at high-speed. The special algorithms incorporated in this system, are specially implemented, in order to be able to be ported on an embedded computer system, which has usually lower capabilities in terms of processing power and memory than a general-purpose computer.

Abstract: In the event that a moving body (e.g. a person, a car, etc.) is outfitted with a video camera or with a camera-equipped device (e.g. a tablet or a mobile phone), the system described in one aspect is able to understand the motion of the moving by analyzing the video frame sequence captured by the camera. This means that the system can categorize the motion of the body-carrying camera to one of several types (e.g., is this a person walking? is this a person running? etc.), understand the nature of the moving body holding the camera-equipped device (e.g. Is this a car?, Is this a person? etc.) and even to identify the moving body (which car?, which person? etc.).

Abstract: A stand-alone computer-camera system capable of extracting car-plate information. This is achieved by using an on-board computer in order to analyze the video stream recorded by the camera sensor, and can be used with any type of camera sensor. The system features specific characteristics making it extremely fast and able to catch plates of cars moving at high-speed. The special algorithms incorporated in this system, are specially implemented, in order to be able to be ported on an embedded computer system, which has usually lower capabilities in terms of processing power and memory than a general-purpose computer.

Abstract: By collecting, analyzing and processing a series of images captured by a camera one can estimate the motion that a device containing the camera has experienced. Exemplary techniques disclosed herein at least enable device motion estimation based on any selection of images from a camera related to the device.