Biblioteca Digital

This paper presents a novel algorithm to successfully achieve viable integrity and authenticity addition and verification of n-frame DICOM medical images using cryptographic mechanisms. The aim of this work is the enhancement of DICOM security measures, especially for multiframe images. Current approaches have limitations that should be properly addressed for improved security. The algorithm proposed in this work uses data encryption to provide integrity and authenticity, along with digital signature. Relevant header data and digital signature are used as inputs to cipher the image. Therefore, one can only retrieve the original data if and only if the images and the inputs are correct. The encryption process itself is a cascading scheme, where a frame is ciphered with data related to the previous frames, generating also additional data on image integrity and authenticity. Decryption is similar to encryption, featuring also the standard security verification of the image. The implementation was done in JAVA, and a performance evaluation was carried out comparing the speed of the algorithm with other existing approaches. The evaluation showed a good performance of the algorithm, which is an encouraging result to use it in a real environment.; CNPq; FINEP

A distribuição da energia espacial do feixe de laser é de grande importância para a eficiência em tratamentos dermatológicos. Os métodos existentes para a calibração do feixe podem ser realizados por meio de equipamentos óticos ou por materiais fotossensíveis. Este trabalho apresenta uma metodologia que pode ser descrita por quatro fases. Na primeira, foi obtido o polímero poliacetal, material que foi responsável por registrar o perfil transversal do feixe de laser (Nd:YAG 1064 nm). Na segunda fase os valores referenciais dos parâmetros do feixe foram extraídos por meio do método knife edge. Posteriormente, as técnicas de processamento de imagem foram aplicadas sendo responsáveis pela extração de características do feixe registradas no polímero. Como resultados, determinaram-se os seguintes parâmetros: área do feixe, circularidade, perfil da borda, assinatura, diâmetros, divergência e cintura do feixe. Na última fase o feixe foi classificado em calibrado e descalibrado por meio da Rede Neural Artificial (RNA) Multilayer Perceptron (MLP), que apresentou 88,5% de taxa de acertos. Outro parâmetro utilizado para a calibração foi a circularidade, onde adotou-se o valor limiar de 0,85 para decisão entre feixes calibrados e feixes descalibrados. Frente aos resultados obtidos...

A verificação de assinaturas utilizando algoritmos de imagem, já é uma realidade à algum tempo. Visando preencher as lacunas deixadas por esse tipo de sistema, um novo fator biométrico é alvo de estudos. Pensando em inserir dados referentes ao momento em que se dá a assinatura, uma nova abordagem vem sendo utilizada. Ao invés de analisar uma imagem, toma-se como base o som que é produzido no ato da assinatura, gerado pelo atrito entre a caneta e a superfície de suporte. O processo de falsificação é dificultado, uma vez que o falsário precisará de informações da grafia da assinatura original e também dos aspectos referentes à dinâmica (velocidade, força e ordem de escrita dos caracteres que compõem a assinatura) que o assinante legítimo desempenha ao assinar. Nesse trabalho, adotou-se tal abordagem biométrica. Foi desenvolvido um dispositivo e um software, que juntos, são capazes de realizar a captura do som emitido durante a assinatura dos usuários. De posse disso, foi gerada uma base de assinantes legítimos e outra com as referentes falsificações, e então, colocou-se o sistema à prova. Afim de tornar as comparações possíveis, gerou-se vetores de características, extraídas com base na transformada Wavelet-Packet discreta. As comparações foram feitas por dois métodos distintos...

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.

Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the mid-wave infrared portion of the electromagnetic spectrum. The goal is to design specialized algorithms that would extract vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal imaging. Thermal facial signature authentication is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through the principal component analysis. Feature extraction was accomplished by first registering the images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate a thermal signature template for each subject to contain only the most prevalent and consistent features. Finally a similarity measure technique was used to match images to the signature templates and the Principal Component Analysis (PCA) was used to validating the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using the similarity measures showed 88% accuracy in case of skeletonized feature signatures and 90% accuracy for anisotropically diffused feature signatures. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the developed thermal infrared system to be used on other thermal imaging based systems and related databases.

A digital image ssynthesizer (DIS), especially useful as a counter-targeting signal repeater, (i.e., for synthesizing the characteristic echo signature of a pre-selected target) is reported. The DIS has a digital radio frequency memory (DRFM) and associated circuitry, including digital tapped delay lines and a modulator in each delay line to impose both amplitude and frequency modulation in each line. A unique property of the digital image synthesizer is its ability to synthesize false targets using wideband chirp signals of any duration. To generate the target, the user can program the target extent (number of taps) and the amplitude and Doppler frequency of each range-Doppler cell within the image. The system-on-a-chip uses a scalable CMOS technology that increases the bandwidth and sensitivity of such a repeater over prior analog based systems. The application specific integrated circuit reduces the noise of the repeated signal, reduces the size and cost of such a system, and permits real time alteration of operating parameters, permitting rapid and adaptive shifting among different types of targets to be synthesized. A scan path test capability is also included to allow intre-schip signal analysis and verification.

A common requirement when working with images is to modify their colour distribution, for example to correct for changes in lighting or to achieve a certain visual effect. We present a novel interface for the modification of colour in images and video sequences through direct visualisation and manipulation of an image signature: a coarse representation of the image in three dimensional colour space. Additionally, we show how the signature of one image can be closely matched to the signature of another using an optimisation technique which minimises the Earth Mover's Distance between the two signatures. Our results are applied to single images for colour matching as well as video sequences, where we interactively correct non-uniform lighting in a video sequence.; Millard, John and Nasilowski, Simeon; © Copyright 2008 IEEE – All Rights Reserved

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity...

Exoplanetary transits produce a double-homed color signature that is distinct from both binaries and blends and can thus be used to separate exoplanets from false positives in transit searches. Color photometry with precision sufficient to detect this sig

The paper approaches the binary signature for each image based on the percentage of the pixels in each color images, at the same time the paper builds a similar measure between images based on EMD (Earth Mover's Distance). Besides, the paper proceeded to create the S-tree based on the similar measure EMD to store the image's binary signatures to quickly query image signature data. From there, the paper build an image retrieval algorithm and CBIR (Content-Based Image Retrieval) based on a similar measure EMD and S-tree. Based on this theory, the paper proceeded to build application and experimental assessment of the process of querying image on the database system which have over 10,000 images.; Comment: 14 pages, 3 figures, Appendix

This paper addresses the problem of learning word image representations: given the cropped image of a word, we are interested in finding a descriptive, robust, and compact fixed-length representation. Machine learning techniques can then be supplied with these representations to produce models useful for word retrieval or recognition tasks. Although many works have focused on the machine learning aspect once a global representation has been produced, little work has been devoted to the construction of those base image representations: most works use standard coding and aggregation techniques directly on top of standard computer vision features such as SIFT or HOG. We propose to learn local mid-level features suitable for building word image representations. These features are learnt by leveraging character bounding box annotations on a small set of training images. However, contrary to other approaches that use character bounding box information, our approach does not rely on detecting the individual characters explicitly at testing time. Our local mid-level features can then be aggregated to produce a global word image signature. When pairing these features with the recent word attributes framework of Almaz\'an et al., we obtain results comparable with or better than the state-of-the-art on matching and recognition tasks using global descriptors of only 96 dimensions.

5 pages, 5 figures.; The security of a recently proposed technique for encryption images by Sinha and Singh [A. Sinha, K. Singh, A technique for image 9 encryption using digital signature, Opt. Commun. 218 (2003) 229–234], based on the use of digital signatures and error correcting codes, 10 is analyzed. The proposed cryptosystem is shown to have some weakness. In fact, the secret key and the original image can be recovered 11 efficiently by a brute force attack.; This work is supported by Ministerio de Educacio´n y 493 Ciencia of Spain, under grant SEG2004-02418.; Peer reviewed

The paper “A Colour Code Algorithm for Signature Recognition” accounts an image processing application where any user can verify signature instantly. The system deals with a Colour code algorithm, which is used to recognize the signature. The paper deals with the recognition of the signature, as human operator generally make the work of signature recognition. Hence the algorithm simulates human behavior, to achieve perfection and skill through AI. The logic that decides the extent of validity of the signature must implement Artificial Intelligence Pattern recognition is the science that concerns the description or classification of measurements, usually based on underlying model. Since most pattern recognition tasks are first done by humans and automated later, the most fruitful source of features has been to asked the people who classify the objects how they tell them a part . Signatures are a behavioural biometric that change over a period of time and are influenced by physical and emotional conditions of a subject. In addition to the general shape of the signed name. The algorithm is tested on various operating systems & we find that it works very well & satisfactory. While implementing the recognition process, we have used quite simpler way. At this stage we are getting accuracy up to about 80% to 90%.

The development of powerful low-cost desktop computer systems has changed the pre-press business where tight deadlines must be met per sistently. An increasing number of newspapers and magazines are acquiring, handling, and storing images digitally while the use of hardcopies and slides decreases. Today's computers and high capacity storage-media enable stock pho tography agencies to build digital image databases, giving users fast access to large numbers of images. However, the transition from analog to digital image archives imposes new problems: with thousands of images at hand, the search for a particular image may turn into the search for the needle in a haystack. The first image Database Management Systems (DBMSs) were extended text DBMSs, which stored the image data along with a set of manually entered descriptive keywords. The major problem with this approach is that there is no generally agreed-upon language to describe images. Even sophis ticated DBMSs are unable to detect synonyms; hence, an image described with certain properties such as "curvy" may not be found if a user enters "wavy" as a search criterion. Furthermore, some image properties are hard to describe with keywords. A search is likely to fail if properties were not described at the database population stage when images are added to the database. Finally...

A fundamental task when performing target detection on spectral imagery is ensuring that a target signature is in the same metric domain as the measured spectral data set. Remotely sensed data are typically collected in digital counts and calibrated to radiance. That is, calibrated data have units of spectral radiance, while target signatures in the visible regime are commonly characterized in units of re°ectance. A necessary precursor to running a target detection algorithm is converting the measured scene data and target signature to the same domain. Atmospheric inversion or compensation is a well-known method for transforming mea- sured scene radiance values into the re°ectance domain. While this method may be math- ematically trivial, it is computationally attractive and is most e®ective when illumination conditions are constant across a scene. However, when illumination conditions are not con- stant for a given scene, signi¯cant error may be introduced when applying the same linear inversion globally. In contrast to the inversion methodology, physics-based forward modeling approaches aim to predict the possible ways that a target might appear in a scene using atmospheric and radiometric models. To fully encompass possible target variability due to changing illumination levels...

An automated approach to change detection analysis was developed for use in multi-temporal image comparisons. An algorithm was developed which enables the user to perform automatic image-to-image rectification. Manual registration techniques are utilized to register a reference image to a Universe Transverse Mercator map projection. Control points, or kernel images, are extracted from the rectified reference image and located automatically in the to-be-mapped images via mathematical correlation. A two windowed approach is used that requires an estimation of the location of the control point in the to-be-mapped image. This estimate is used to create a search area which is correlated with the kernel image. The images were rectified to within approximately two pixels. The images were radiometrically normalized so that actual ground changes can be distinguished from those that occur due to imaging conditions. This was done through a simple histogram matching technique. Next, the images were classified to illustrate the changes in land cover type. An unsupervised classification was used to train the reference image. The rest of the images in the set were classified using the spectral signature data generated from this process. The classification accuracy was dependent on the normalization procedure used. The process was demonstrated using LANDSAT MSS imagery to show the extent to which the logging technique of clearcutting has devastated the forest stands in the North Cascades of Washington state.

This paper proposes a new image signature, called Compass Rose, which is particularly suited to optical flow computation. It is differentiable, fast to compute, and robust to additive illumination changes, translation, and fast rotation. We design a sparse flow computation system based on the invariance of the Compass Rose signatures. This is then extended to dense motion estimation by the addition of an optional diffusion step. Quantitative testing on several benchmark sequences shows the Compass Rose attains higher accuracy than the traditional flow signatures under a range of conditions. Finally, we demonstrate its application to human motion estimation, which is challenging for optical flow methods due to fast limb rotation.; Yan Niu, Anthony Dick, and Michael Brooks

A new method of metal stamping force signature analysis is presented, based on the image recognition technique Point Distribution Models (PDMs). The approach is shown to be an effective method for analyzing force signatures, able to identify not only the

Inseridas em modelo de planejamento estratégico, muitas cidades vêm buscando a receita da transformação urbana, por meio de grandes projetos de desenvolvimento urbano que expressem um urbanismo monumental e contenha arquiteturas de grife. Dentre as diversas cidades que já experimentaram esse tipo de planejamento urbano, destaca-se Bilbao, a qual, com a construção do Museu Guggenheim, projetado pelo arquiteto Frank Gehry, passou por uma transformação em sua estrutura urbana e por uma nova construção imagética da cidade no mundo contemporâneo dos negócios e dirigida pelo capital financeiro, bem como por uma economia cada vez mais dependente da obtenção de rendas de monopólio. Assim sendo, o modelo adotado nessa cidade provocou o chamado" efeito Bilbao", sendo o museu, em grande medida, o principal responsável por essa implicação, tornando-se também o principal ícone da capital basca. No entanto, a cidade vem buscando auto-afirmar-se à custa de outras intervenções e, principalmente, de outras arquiteturas de grife, com a agregação de mais valor simbólico às novas construções, muitas das quais seguindo a receita de sucesso do Guggenheim. Diante de tal fato, entender os grandes projetos urbanos, isto é, o" urbanismo monumentalista patriótico" de Bilbao e...