9 research outputs found
A New Video authentication Template Based on Bubble random Sampling
The rapid growth of digital video distribution has highlighted new important issues in digital rights management, as well as in other important applications such as video authentication. Digital watermarking offers a promising solution against piracy and it is therefore a very active area of research. However, robustness to video manipulations, either malicious or not, is a demanding task because there are many different types of possible attacks that can be envisioned. Among these, geometric and temporal distortions play the major roles. The countermeasures against these specific attacks are still an open challenge. In this paper we propose the use of a video authentication template based on bubble random sampling. The authentication template is introduced in order to ensure temporal synchronization
and to prevent content tampering. The simulation results are encouraging and this approach is therefore worth further development efforts
A novel content-based image retrieval system based on Bayesian logistic regression
In this work, a novel content-based image retrieval (CBIR) method is presented. It has been implemented and run on “Qatris
IManager” [14], a system belonging to SICUBO S.L. (spin-off from University of Extremadura, Spain). The system offers
some innovative visual content search tools for image retrieval from databases. It searches, manages and classifies images
using four kinds of features: colour, texture, shape and user description.
In a typical CBIR system, query results are a set of images sorted by feature similarities with respect to the query. However,
images with high feature similarities to the query may be very different from the query in terms of semantics. This discrepancy
between low-level features and high-level concepts is known as the semantic gap.
The search method presented here, is a novel supervised image retrieval method, based in Bayesian Logistic Regression, which
uses the information from the characteristics extracted from the images and from the user’s opinion who sets up the search. The
procedure of search and learning is based on a statistical method of aggregation of preferences given by Arias-Nicolás et al. [1]
and is useful in problems with both a large number of characteristics and few images.
The method could be specially helpful for those professionals who have to make a decision based in images, such as doctors to
determine the diagnosis of patients, meteorologists, traffic police to detect license plate, etc
Application of the fuzzy logic in content-based image retrieval
This paper imports the fuzzy logic into image retrieval to deal with the vagueness and ambiguity of human judgment of image similarity. Our retrieval system has the following properties: firstly adopting the fuzzy language variables to describe the similarity degree of image features, not the features themselves; secondly making use of the fuzzy inference to instruct the weights assignment among various image features; thirdly expressing the subjectivity of human perceptions by fuzzy rules impliedly; lastly we propose an improvement on the traditional histogram called the Average Area Histogram (AAH) to represent color features. Experimentally we realized a fuzzy logic-based image retrieval system with good retrieval performance.Facultad de Informátic
Image Processing Applications in Real Life: 2D Fragmented Image and Document Reassembly and Frequency Division Multiplexed Imaging
In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied.
In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the Fourier domain. Finally, a Texas Instruments digital micromirror device (DMD) based implementation of FDMI is presented and results are shown.
Chapter 2 discusses the problem of image reassembly which is to restore an image back to its original form from its pieces after it has been fragmented due to different destructive reasons. We propose an efficient algorithm for 2D image fragment reassembly problem based on solving a variation of Longest Common Subsequence (LCS) problem. Our processing pipeline has three steps. First, the boundary of each fragment is extracted automatically; second, a novel boundary matching is performed by solving LCS to identify the best possible adjacency relationship among image fragment pairs; finally, a multi-piece global alignment is used to filter out incorrect pairwise matches and compose the final image. We perform experiments on complicated image fragment datasets and compare our results with existing methods to show the improved efficiency and robustness of our method.
The problem of reassembling a hand-torn or machine-shredded document back to its original form is another useful version of the image reassembly problem. Reassembling a shredded document is different from reassembling an ordinary image because the geometric shape of fragments do not carry a lot of valuable information if the document has been machine-shredded rather than hand-torn. On the other hand, matching words and context can be used as an additional tool to help improve the task of reassembly. In the final chapter, document reassembly problem has been addressed through solving a graph optimization problem
Automatic extraction of regions of interest from images based on visual attention models
This thesis presents a method for the extraction of regions of interest (ROIs) from images. By ROIs we mean the most prominent semantic objects in the images, of any size and located at any position in the image. The novel method is based on computational models of visual attention (VA), operates under a completely bottom-up and unsupervised way and does not present con-straints in the category of the input images. At the core of the architecture is de model VA proposed by Itti, Koch and Niebur and the one proposed by Stentiford. The first model takes into account color, intensity, and orientation features and provides coordinates corresponding to the points of attention (POAs) in the image. The second model considers color features and provides rough areas of attention (AOAs) in the image. In the proposed architecture, the POAs and AOAs are combined to establish the contours of the ROIs. Two implementations of this architecture are presented, namely 'first version' and 'improved version'. The first version mainly on traditional morphological operations and was applied in two novel region-based image retrieval systems. In the first one, images are clustered on the basis of the ROIs, instead of the global characteristics of the image. This provides a meaningful organization of the database images, since the output clusters tend to contain objects belonging to the same category. In the second system, we present a combination of the traditional global-based with region-based image retrieval under a multiple-example query scheme. In the improved version of the architecture, the main stages are a spatial coherence analysis between both VA models and a multiscale representation of the AOAs. Comparing to the first one, the improved version presents more versatility, mainly in terms of the size of the extracted ROIs. The improved version was directly evaluated for a wide variety of images from different publicly available databases, with ground truth in the form of bounding boxes and true object contours. The performance measures used were precision, recall, F1 and area overlap. Experimental results are of very high quality, particularly if one takes into account the bottom-up and unsupervised nature of the approach.UOL; CAPESEsta tese apresenta um método para a extração de regiões de interesse (ROIs) de imagens. No contexto deste trabalho, ROIs são definidas como os objetos semânticos que se destacam em uma imagem, podendo apresentar qualquer tamanho ou localização. O novo método baseia-se em modelos computacionais de atenção visual (VA), opera de forma completamente bottom-up, não supervisionada e não apresenta restrições com relação à categoria da imagem de entrada. Os elementos centrais da arquitetura são os modelos de VA propostos por Itti-Koch-Niebur e Stentiford. O modelo de Itti-Koch-Niebur considera as características de cor, intensidade e orientação da imagem e apresenta uma resposta na forma de coordenadas, correspondentes aos pontos de atenção (POAs) da imagem. O modelo Stentiford considera apenas as características de cor e apresenta a resposta na forma de áreas de atenção na imagem (AOAs). Na arquitetura proposta, a combinação de POAs e AOAs permite a obtenção dos contornos das ROIs. Duas implementações desta arquitetura, denominadas 'primeira versão' e 'versão melhorada' são apresentadas. A primeira versão utiliza principalmente operações tradicionais de morfologia matemática. Esta versão foi aplicada em dois sistemas de recuperação de imagens com base em regiões. No primeiro, as imagens são agrupadas de acordo com as ROIs, ao invés das características globais da imagem. O resultado são grupos de imagens mais significativos semanticamente, uma vez que o critério utilizado são os objetos da mesma categoria contidos nas imagens. No segundo sistema, á apresentada uma combinação da busca de imagens tradicional, baseada nas características globais da imagem, com a busca de imagens baseada em regiões. Ainda neste sistema, as buscas são especificadas através de mais de uma imagem exemplo. Na versão melhorada da arquitetura, os estágios principais são uma análise de coerência espacial entre as representações de ambos modelos de VA e uma representação multi-escala das AOAs. Se comparada à primeira versão, esta apresenta maior versatilidade, especialmente com relação aos tamanhos das ROIs presentes nas imagens. A versão melhorada foi avaliada diretamente, com uma ampla variedade de imagens diferentes bancos de imagens públicos, com padrões-ouro na forma de bounding boxes e de contornos reais dos objetos. As métricas utilizadas na avaliação foram presision, recall, F1 e area of overlap. Os resultados finais são excelentes, considerando-se a abordagem exclusivamente bottom-up e não-supervisionada do método
A tree grammar-based visual password scheme
A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. Johannesburg, August 31, 2015.Visual password schemes can be considered as an alternative to alphanumeric
passwords. Studies have shown that alphanumeric passwords
can, amongst others, be eavesdropped, shoulder surfed, or
guessed, and are susceptible to brute force automated attacks. Visual
password schemes use images, in place of alphanumeric characters,
for authentication. For example, users of visual password schemes either
select images (Cognometric) or points on an image (Locimetric)
or attempt to redraw their password image (Drawmetric), in order
to gain authentication. Visual passwords are limited by the so-called
password space, i.e., by the size of the alphabet from which users can
draw to create a password and by susceptibility to stealing of passimages
by someone looking over your shoulders, referred to as shoulder
surfing in the literature. The use of automatically generated highly
similar abstract images defeats shoulder surfing and means that an almost
unlimited pool of images is available for use in a visual password
scheme, thus also overcoming the issue of limited potential password
space.
This research investigated visual password schemes. In particular,
this study looked at the possibility of using tree picture grammars to
generate abstract graphics for use in a visual password scheme. In this
work, we also took a look at how humans determine similarity of abstract
computer generated images, referred to as perceptual similarity
in the literature. We drew on the psychological idea of similarity and
matched that as closely as possible with a mathematical measure of
image similarity, using Content Based Image Retrieval (CBIR) and
tree edit distance measures. To this end, an online similarity survey
was conducted with respondents ordering answer images in order
of similarity to question images, involving 661 respondents and 50
images. The survey images were also compared with eight, state of
the art, computer based similarity measures to determine how closely
they model perceptual similarity. Since all the images were generated
with tree grammars, the most popular measure of tree similarity, the
tree edit distance, was also used to compare the images. Eight different
types of tree edit distance measures were used in order to cover
the broad range of tree edit distance and tree edit distance approximation
methods. All the computer based similarity methods were
then correlated with the online similarity survey results, to determine
which ones more closely model perceptual similarity. The results were
then analysed in the light of some modern psychological theories of
perceptual similarity.
This work represents a novel approach to the Passfaces type of visual
password schemes using dynamically generated pass-images and their
highly similar distractors, instead of static pictures stored in an online
database. The results of the online survey were then accurately
modelled using the most suitable tree edit distance measure, in order
to automate the determination of similarity of our generated distractor
images. The information gathered from our various experiments
was then used in the design of a prototype visual password scheme.
The generated images were similar, but not identical, in order to defeat
shoulder surfing. This approach overcomes the following problems
with this category of visual password schemes: shoulder surfing,
bias in image selection, selection of easy to guess pictures and infrastructural
limitations like large picture databases, network speed and
database security issues. The resulting prototype developed is highly
secure, resilient to shoulder surfing and easy for humans to use, and
overcomes the aforementioned limitations in this category of visual
password schemes
Analyse et recherche d'oeuvres d'art 2D selon le contenu pictural
État de l'art des méthodes manuelles et automatiques d'analyse des oeuvres d'art 2D -- Recherche d'images selon l'organisation spatiale des couleurs -- Seuil automatique pour la recherche d'images selon l'OSC -- Extraction des contours des traits -- Analyse de l'impact pictural dans les oeuvres au trait -- Conclusion et perspectives