We investigate a neural network–based solution for the Automatic Meter Reading detection problem, applied to analog dial gauges. We employ a convolutional neural network with a non-linear Network in Network kernel. Presently, there is a significant interest in systems for automatic detection of analog dial gauges, particularly in the energy and household sectors, but the problem is not yet sufficiently addressed in research. Our method is a universal three-level model that takes an image as an input and outputs circular bounding areas, object classes, grids of reference points for all symbols on the front panel of the device and positions of display pointers. Since all analog pointer meters have a common nature, this multi-cascade model can serve various types of devices if its capacity is sufficient. The model is using global regression for locations of symbols, which provides resilient results even for low image quality and overlapping symbols. In this work, we do not focus on the pointer location detection since it heavily depends on the shape of the pointer. We prepare training data and benchmark the algorithm with our own framework a3net, not relying on third-party neural network solutions. The experimental results demonstrate the versatility of the proposed methods, high accuracy, and resilience of reference points detection.
In the article a certain class of feature extractors for face recognition is presented. The extraction is based on simple approaches: image scaling with pixel concatenation into a feature vector, selection of a small number of points from the face area, face image's spectrum, and finally pixel intensities histogram. The experiments performed on several facial image databases (BioID [4], ORL face database [27], FERET [30]) show that face recognition using this class of extractors is particularly efficient and fast, and can have straightforward implementations in software and hardware systems. They can also be used in fast face recognition system involving feature-integration, as well as a tool for similar faces retrieval in 2-tier systems (as initial processing, before exact face recognition).
Abstract. The paper addresses the problem of face recognition for images registered in variable lighting, which is common for real-world conditions. Presented algorithm is based on orthogonal transformation preceded by simple transformations comprising of equalization of brightness gradients, removal of spatial low frequency spectral components and fusion of spectral features depending on average pixels intensity. Two types of transformations: 2DDCT (two-dimensional Discrete Cosine Transform) and 2DKLT (two-dimensional Karhunen-Loeve Transform) were investigated in order to find the most optimal algorithm setup. The results of experiments conducted on Yale B and Yale B+ datasets show that a quite simple algorithm is capable of successful recognition without high computing power demand, as opposite to several more sophisticated methods presented recently.
Предмет исследования. Обсуждается алгоритм формирования цветных QR-кодов для задач лицевой биометрии. Методы основаны на процедурах обработки изображений, методах линейной алгебры и реализованы в среде пакета МАТЛАБ. Основные результаты. Представлен новый графический объект-цветной биометрический QR-код и метод формирования цветных QR-кодов для задач лицевой биометрии, описан алгоритм его реализации, показана функциональная схема устройства его реализации, приводятся примеры различных вариантов цветных биометрических QRкодов. Практическая значимость. Полученные результаты могут быть использованы в практической биометрии и ее приложениях, а также для создания новых видов тестовых баз изображений лиц. Ключевые слова цветной биометрический QR-код, декомпозиция цветного изображения, антропометрия, биометрическая информация, генерация QR-кода
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