A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewedA semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote sensing images applying a tree structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of themmultispectral or hyperspectral image is used to embed the mark in it order to detect any significant modification of the original image. The image is segmented into threedimensional blocks and a tree structured vector quantizer is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.Se presenta un esquema de marcas de agua semi-frágiles para múltiples imágenes de banda. Proponemos incorporar una marca en imágenes de detección remota, aplicando un enfoque de cuantización del vector de árbol estructurado con las definiciones de píxel, en lugar de procesar cada banda por separado. La firma de la imagen hiperespectral se utiliza para insertar la marca en el mismo orden para detectar cualquier modificación significativa de la imagen original. La imagen es segmentada en bloques tridimensionales y un cuantificador de vector de estructura de árbol se construye para cada bloque. Estos árboles son manipulados utilizando un algoritmo iteractivo hasta que el bloque resultante satisface un criterio necesario que establece la marca incrustada. El método se muestra para poder preservar la marca bajo compresión con pérdida (por encima de un umbral establecido) pero, al mismo tiempo, detecta posiblemente bloques forjados y su posición en la imagen entera.Es presenta un esquema de marques d'aigua semi-fràgils per a múltiples imatges de banda. Proposem incorporar una marca en imatges de detecció remota, aplicant un enfocament de quantització del vector d'arbre estructurat amb les definicions de píxel, en lloc de processar cada banda per separat. La signatura de la imatge hiperespectral s'utilitza per inserir la marca en el mateix ordre per detectar qualsevol modificació significativa de la imatge original. La imatge és segmentada en blocs tridimensionals i un quantificador de vector d'estructura d'arbre es construeix per a cada bloc. Aquests arbres són manipulats utilitzant un algoritme iteractiu fins que el bloc resultant satisfà un criteri necessari que estableix la marca incrustada. El mètode es mostra per poder preservar la marca sota compressió amb pèrdua (per sobre d'un llindar establert) però, al mateix temps, detecta possiblement blocs forjats i la seva posició en la imatge sencera

Semi-supervised Multiple Classifier Systems: Background and Research Directions

Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised classification has not received much attention. Few works on semi-supervised multiple classifiers appeared in the machine learning literature. This paper’s goal is to review the background results that can be exploited to promote research on semi-supervised multiple classifier systems, and to outline some future research direction

Urinary Bladder Tumor Grade Diagnosis Using On-line Trained Neural Networks

This paper extends the line of research that considers the application of Artificial Neural Networks (ANNs) as an automated system, f or the assi nment of tumors rade. One hundred twenty nine cases were classified accordin to the WHO radin system by experienced patholoists in three classes: Grade I, Grade II and Grade III. 36 morpholo ical and textural, cell nuclei feig res represented each case. Thesef eatures were used as an input to the ANN classifier, which was trained usin a novel stochastic trainin al orithm, namely, the Adaptive Stochastic On-Line method. The resultin automated classification system achieved classification accuracyof 90%, 94.9% and 97.3%f. tumors of Grade I, II and III respectively.

Discriminative Analysis of Brain Function at Resting-State for Attention-Deficit/Hyperactivity Disorder

In this work, a discriminative model of attention deficit hyperactivity disorder (ADHD) is presented on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model consists of two parts, a classifier and an intuitive representation of discriminative pattern of brain function between patients and normal controls. Regional homogeneity (ReHo), a measure of brain function at resting-state, is used here as a feature of classification. Fisher discriminative analysis (FDA) is performed on the features of training samples and a linear classifier is generated. Our initial experimental results show a successful classification rate of 85%, using leave-one-out cross validation. The classifier is also compared with linear support vector machine (SVM) and Batch Perceptron. Our classifier outperforms the alternatives significantly. Fisher brain, the optimal projective-direction vector in FDA, is used to represent the discriminative pattern. Some abnormal brain regions identified by Fisher brain, like prefrontal cortex and anterior cingulate cortex, are well consistent with that reported in neuroimaging studies on ADHD. Moreover, some less reported but highly discriminative regions are also identified. We conclude that the discriminative model has potential ability to improve current diagnosis and treatment evaluation of ADHD. ? Springer-Verlag Berlin Heidelberg 2005.EI