Optimization of watermarking performances using error correcting codes and repetition

With the ever increasing development and usage of digital technologies and digital data the question of protecting intellectual property of digital data is becoming more and more important. Digital watermarking which allows to embed copyright information into the digital data has become indispensable. Due to its characteristics one of the problems in digital watermarking for fixed images is to decide how to hide in an image as many bits of information for signature as possible while ensuring that the signature can be correctly retrieved at the detecting stage even after various image manipulation including attacks. Error correcting codes and repetition are the natural choices to use in order to correct possible errors when extracting the signature. In this paper we have investigated different ways of applying error correcting codes, repetition and some combinations of the two given different capacities of a fixed image for different error rates of the watermarking channel in order to obtain optimal selection for a given length of signature. We present both the qualitative and quantitative results. The goal of this work is to explore applying coding methods for watermarking purpose

Wavelet coherence-based classifier : a resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism

\u3cbr/\u3eBackground and Objective\u3cbr/\u3eThe autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI.\u3cbr/\u3eMethods\u3cbr/\u3eWe investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset.\u3cbr/\u3eResults\u3cbr/\u3eWe distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity).\u3cbr/\u3eConclusions\u3cbr/\u3eThis study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.\u3cbr/\u3

Content-based text line comparison for historical document retrieval

In the historical handwritten document retrieval system that we are currently building, the training data set elements are the images of handwritten lines with the manually made text transcriptions. We apply sequence comparison algorithms to these text transcriptions. We explore several sequence comparison algorithms that have been applied to phonology for their usefulness in solving a problem of retrieving handwritten material. Finding an appropriate method for comparing text lines will allow us to cluster the corresponding images of handwritten lines into training sets. These training sets can then be used for pattern recognition - an important part of the historical handwritten document retrieval system. At first we study the information needs of the users of an archive where the historical documents are stored. Then we explore the longest common substring (LCS), Levenshtein and Jaccard measures for matching the text lines. Taking into account the drawbacks of these methods, we propose to weight the words in the text proportionally to their information content. This weighting is expected to provide results closer to the information needs of users. We evaluate the results in terms of the precision values for k top retrieved text lines. Using the mean precision curves we show that the performance of sequence comparisons increases up to 18% when we use the weighted sequence comparisons

Fast scene analysis for surveillance & video databases

In professional/consumer domains, video databases are broadly applied, facilitating quick searching by fast region analysis, to provide an indication of the video contents. For realtime and cost-efficient implementations, it is important to develop algorithms with high accuracy and low computational complexity. In this paper, we analyze the accuracy and computational complexity of newly developed approaches for semantic region labeling and salient region detection, which aim at extracting spatial contextual information from a video. Both algorithms are analyzed by their native DSP computations and memory usage to prove their practical feasibility. In the analyzed semantic region labeling approach, color and texture features are combined with their related vertical image position to label the key regions. In the salient region detection approach, a discrete cosine transform (DCT) is employed, since it provides a compact representation of the signal energy and the computation can be implemented at low cost. The techniques are applied to two complex surveillance use cases, moving ships in a harbor region and moving cars in traffic surveillance videos, to improve scene understanding in surveillance videos. Results show that our spatial contextual information methods quantitatively and qualitatively outperform other approaches with up to 22% gain in accuracy, while operating at several times lower complexity

Detection of human groups in videos

In this paper, we consider the problem of finding and localizing social human groups in videos, which can form a basis for further analysis and monitoring of groups in general. Our approach is motivated by the collective behavior of individuals which has a fundament in sociological studies. We design a detection-based multi-target tracking framework which is capable of handling short-term occlusions and producing stable trajectories. Human groups are discovered by clustering trajectories of individuals in an agglomerative fashion. A novel similarity function related to distances between group members, robustly measures the similarity of noisy trajectories. We have evaluated our approach on several test sequences and achieved acceptable miss rates (19.4%, 29.7% and 46.7%) at reasonable false positive detections per frame (0.129, 0.813 and 0.371). The relatively high miss rates are caused by a strict evaluation procedure, whereas the visual results are quite acceptable

Cancer detection in histopathology whole-slide images using conditional random fields on deep embedded spaces

\u3cp\u3eAdvanced image analysis can lead to automated examination to histopatholgy images which is essential for ob-jective and fast cancer diagnosis. Recently deep learning methods, in particular Convolutional Neural Networks (CNNs), have shown exceptionally successful performance on medical image analysis as well as computational histopathology. Because Whole-Slide Images (WSIs) have a very large size, the CNN models are commonly applied to classify WSIs per patch. Although a CNN is trained on a large part of the input space, the spatial dependencies between patches are ignored and the inference is performed only on appearance of the individual patches. Therefore, prediction on the neighboring regions can be inconsistent. In this paper, we apply Con-ditional Random Fields (CRFs) over latent spaces of a trained deep CNN in order to jointly assign labels to the patches. In our approach, extracted compact features from intermediate layers of a CNN are considered as observations in a fully-connected CRF model. This leads to performing inference on a wider context rather than appearance of individual patches. Experiments show an improvement of approximately 3.9% on average FROC score for tumorous region detection in histopathology WSIs. Our proposed model, trained on the Camelyon17\u3csup\u3e1\u3c/sup\u3e ISBI challenge dataset, won the 2\u3csup\u3end\u3c/sup\u3e place with a kappa score of 0.8759 in patient-level pathologic lymph node classification for breast cancer detection.\u3c/p\u3

Free-viewpoint depth image based rendering

In 3D TV research, one approach is to employ multiple cameras for creating a 3D multi-view signal with the aim to make interactive free-viewpoint selection possible in 3D TV media. This paper explores a new rendering algorithm that enables to compute a free-viewpoint between two reference views from existing cameras. A unique property is that we perform forward warping for both texture and depth simultaneously. Advantages of our rendering are manyfold. First, resampling artifacts are filled in by inverse warping. Second, disocclusions are processed while omitting warping of edges at high discontinuities. Third, our disocclusion inpainting approach explicitly uses depth information. We obtain an average PSNR gain of 3 dB and 4.5 dB for the ‘Breakdancers’ and ‘Ballet’ sequences, respectively, compared recently published results. Moreover, experiments are performed using compressed video from surrounding cameras. The overall system quality is dominated by rendering quality and not by coding

Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?

Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.\u3cbr/\u3e