Анализ алгоритмов обнаружения импульсного шума на цифровых изображениях

Выполнен анализ способов обнаружения присутствия импульсного шума на цифровых изображениях. Приведены результаты сопоставления различных алгоритмов обнаружения пикселей, искаженных импульсным шумом

Алгоритмы выделения лиц на статических RGB изображениях и в видеопотоке

Разработаны алгоритмы выделения лиц на статических изображениях и в видеопотоке: алгоритм обнаружения лица с помощью цветовой сегментации, алгоритм выделения лиц на статических RGB изображениях с помощью деформируемых эллиптических моделей, метод статических моментов для выделения лиц в видеопотоке

Система поиска, выделения и распознавания лиц на изображениях

Для решения задачи выделения и распознавания лиц использованы сверточные нейронные сети. Представлена структура разработанной нейронной сети. Представлен алгоритм масштабирования и кластеризации изображений

Face Recognition and Gender Determination

The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

Effectiveness of Multi-View Face Images and Anthropometric Data In Real-Time Networked Biometrics

Over the years, biometric systems have evolved into a reliable mechanism for establishing identity of individuals in the context of applications such as access control, personnel screening and criminal identification. However, recent terror attacks, security threats and intrusion attempts have necessitated a transition to modern biometric systems that can identify humans under unconstrained environments, in real-time. Specifically, the following are three critical transitions that are needed and which form the focus of this thesis: (1) In contrast to operation in an offline mode using previously acquired photographs and videos obtained under controlled environments, it is required that identification be performed in a real-time dynamic mode using images that are continuously streaming in, each from a potentially different view (front, profile, partial profile) and with different quality (pose and resolution). (2) While different multi-modal fusion techniques have been developed to improve system accuracy, these techniques have mainly focused on combining the face biometrics with modalities such as iris and fingerprints that are more reliable but require user cooperation for acquisition. In contrast, the challenge in a real-time networked biometric system is that of combining opportunistically captured multi-view facial images along with soft biometric traits such as height, gait, attire and color that do not require user cooperation. (3) Typical operation is expected to be in an open-set mode where the number of subjects that enrolled in the system is much smaller than the number of probe subjects; yet the system is required to generate high accuracy.;To address these challenges and to make a successful transition to real-time human identification systems, this thesis makes the following contributions: (1) A score-based multi- modal, multi-sample fusion technique is designed to combine face images acquired by a multi-camera network and the effectiveness of opportunistically acquired multi-view face images using a camera network in improving the identification performance is characterized; (2) The multi-view face acquisition system is complemented by a network of Microsoft Kinects for extracting human anthropometric features (specifically height, shoulder width and arm length). The score-fusion technique is augmented to utilize human anthropometric data and the effectiveness of this data is characterized. (3) The performance of the system is demonstrated using a database of 51 subjects collected using the networked biometric data acquisition system.;Our results show improved recognition accuracy when face information from multiple views is utilized for recognition and also indicate that a given level of accuracy can be attained with fewer probe images (lesser time) when compared with a uni-modal biometric system

Frontal-view Face Detection in The Presence of Skin-Tone Regions Using a New Symmetry Approach

In this paper, an efficient algorithm for detecting frontalview faces in color images is proposed. The proposed algorithm has a special task; it detects faces in the presence of skin-tone regions such as human body, clothes, and background. Firstly, a pixel based color classifier is applied to segment the skin pixels from background. Next, a hybrid cluster algorithm is applied to partition the skin region. It is well known that the frontal face is symmetrical; therefore we introduce a new symmetry approach, which is the main distinguishing feature of the proposed algorithm. It measures a symmetrical value, searches for the real center of the region, and then removes the extra unsymmetrical skin pixels. The cost functions are adopted to locate the real two eyes of the candidate face region. Finally, a template matching process is preformed between an aligning frontal face model and the candidate face region as a verification step. We have tested our algorithm on 200 images from different sets. Experimental results reveal that our algorithm can perform the detection of faces successfully under wide variations of captured images.Facultad de Informátic

Generalization to Novel Views: Universal, Class-based, and Model-based Processing", Int

Abstract. A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewing conditions such as viewing position and illumination. In this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place: universal, class, and model-based. The first is applicable equally to all objects, the second to a class of objects, and the third uses known properties of individual objects. We derive theoretical limitations on each of the three generalization levels. For the universal level, previous results have shown that no invariance can be obtained. Here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed. We also extend the results to changes of illumination direction. For the class level, previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist. Here, we distinguish between classes that admit such invariance and classes that do not. We demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects. Furthermore, we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level, when the objects are restricted to belong to a given class, an invariant function to illumination direction can be defined. A general conclusion of this study is that class-based processing, that has not been used extensively in the past, is often advantageous for dealing with variations due to viewpoint and illuminant changes. Keywords: object recognition, invariance 1