Digitisation Processing and Recognition of Old Greek Manuscipts (the D-SCRIBE Project)

After many years of scholar study, manuscript collections continue to be an important source of novel information for scholars, concerning both the history of earlier times as well as the development of cultural documentation over the centuries. D-SCRIBE project aims to support and facilitate current and future efforts in manuscript digitization and processing. It strives toward the creation of a comprehensive software product, which can assist the content holders in turning an archive of manuscripts into a digital collection using automated methods. In this paper, we focus on the problem of recognizing early Christian Greek manuscripts. We propose a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. Based on the existence of closed cavity regions in the majority of characters and character ligatures in these scripts, we propose a novel, segmentation-free, fast and efficient technique that assists the recognition procedure by tracing and recognizing the most frequently appearing characters or character ligatures

On the Inverse Hough Transform

AbstractÐIn this paper, an Inverse Hough Transform algorithm is proposed. This algorithm reconstructs correctlythe original image, using onlythe data of the Hough Transform space and it is applicable to anybinaryimage. As a first application, the Inverse Hough Transform algorithm is used for straight-line detection and filtering. The lines are detected not just as continuous straight lines, which is the case of the standard Hough Transform, but as theyreallyappear in the original image, i.e., pixel bypixel. To avoid the quantization effects in the Hough Transform space, inversion conditions are defined, which are associated onlywith the dimensions of the images. Experimental results indicate that the Inverse Hough Transform algorithm is robust and accurate. Index TermsÐHough Transform, edge extraction, line detection, nonlinear filtering.

A ROBUST IMAGE WATERMARKING TECHNIQUE BASED ON SPECTRUM ANALYSIS AND PSEUDORANDOM SEQUENCES

Abstract: In this paper a watermarking scheme is presented that embeds the watermark message in randomly chosen coefficients along a ring in the frequency domain using non maximal pseudorandom sequences. The proposed method determines the longest possible sequence that corresponds to each watermark bit for a given number of available coefficients. Furthermore, an extra parameter is introduced that controls the robustness versus security performance of the encoding process. This parameter defines the size of a subset of available coefficients in the transform domain which are used for watermark embedding. Experimental results show that the method is robust to a variety of image processing operations and geometric transformations.

Artificial Neural Network Approach for Land Cover Classification of Fused Hyperspectral and Lidar Data

Part 7: Intelligent Signal and Image ProcessingInternational audienceHyperspectral remote sensing images are consisted of several hundreds of contiguous spectral bands that can provide very rich information and has the potential to differentiate land cover classes with similar spectral characteristics. LIDAR data gives detailed height information and thus can be used complementary with Hyperspectral data. In this work, a hyperspectral image is combined with LIDAR data and used for land cover classification. A Principal Component Analysis (PCA) is applied on the Hyperspectral image to perform feature extraction and dimension reduction. The first 4 PCA components along with the LIDAR image were used as inputs to a supervised feedforward neural network. The neural network was trained in a small part of the dataset (less than 0.4%) and a validation set, using the Bayesian regularization backpropagation algorithm. The experimental results demonstrate efficiency of the method for hyperspectral and LIDAR land cover classification

Experimental Sensitivity Investigation of Mean-Shift Variants to Bandwidth Parameters for 3D Buildings Reconstruction

International audienc

Panic Detection Using Machine Learning and Real-Time Biometric and Spatiotemporal Data

It is common sense that immediate response and action are among the most important terms when it comes to public safety, and emergency response systems (ERS) are technology components strictly tied to this purpose. While the use of ERSs is increasingly adopted across many aspects of everyday life, the combination of them with real-time biometric and location data appears to provide a different perspective. Panic is one of the most important emergency indicators. Until now, panic events of any cause tend to be treated in a local manner. Various attempts to detect such events have been proposed based on traditional methods such as visual surveillance technologies and community engagement systems. The aim of this paper is twofold. First, it presents an innovative multimodal dataset containing biometric and spatiotemporal data associated with the detection of panic state in subjects that perform various activities during a certain period. For this purpose, time-enabled location data are combined with biometrics coming from wearables and smartphones that are analyzed in real-time and produce data indicating possible panic events that are geospatially described. Second, the proposed dataset is used to train various machine learning models, and their applicability to correctly distinguish panic states from normal behavior is thoroughly examined. As a result, the Gaussian SVM classifier ranked first among seven classifiers, achieving an accuracy score of 94.5%. The dataset was also tested in a deep learning framework, achieving an accuracy level of 93.4%. A long short-term memory approach was also used, which reached a top accuracy of 94%. Moreover, the contribution of the various biometric and geospatial features is analyzed in-depth to determine their partial importance in the overall panic detection process. This is moving towards the creation of a smart geo-referenced ERS that could be used to inform the authorities regarding a potentially unpleasant event by detecting possible crowd panic patterns and helping to act accordingly, getting the information right from the source of the event, the human body. The proposed dataset is freely distributed to the scientific community under the third version of GNU General Public License (GPL v3) through the GitHub platform

Panic Detection Using Machine Learning and Real-Time Biometric and Spatiotemporal Data

It is common sense that immediate response and action are among the most important terms when it comes to public safety, and emergency response systems (ERS) are technology components strictly tied to this purpose. While the use of ERSs is increasingly adopted across many aspects of everyday life, the combination of them with real-time biometric and location data appears to provide a different perspective. Panic is one of the most important emergency indicators. Until now, panic events of any cause tend to be treated in a local manner. Various attempts to detect such events have been proposed based on traditional methods such as visual surveillance technologies and community engagement systems. The aim of this paper is twofold. First, it presents an innovative multimodal dataset containing biometric and spatiotemporal data associated with the detection of panic state in subjects that perform various activities during a certain period. For this purpose, time-enabled location data are combined with biometrics coming from wearables and smartphones that are analyzed in real-time and produce data indicating possible panic events that are geospatially described. Second, the proposed dataset is used to train various machine learning models, and their applicability to correctly distinguish panic states from normal behavior is thoroughly examined. As a result, the Gaussian SVM classifier ranked first among seven classifiers, achieving an accuracy score of 94.5%. The dataset was also tested in a deep learning framework, achieving an accuracy level of 93.4%. A long short-term memory approach was also used, which reached a top accuracy of 94%. Moreover, the contribution of the various biometric and geospatial features is analyzed in-depth to determine their partial importance in the overall panic detection process. This is moving towards the creation of a smart geo-referenced ERS that could be used to inform the authorities regarding a potentially unpleasant event by detecting possible crowd panic patterns and helping to act accordingly, getting the information right from the source of the event, the human body. The proposed dataset is freely distributed to the scientific community under the third version of GNU General Public License (GPL v3) through the GitHub platform

patchIT: A Multipurpose Patch Creation Tool for Image Processing Applications

Patch-based approaches in image processing are often preferable to working with the entire image. They provide an alternative representation of the image as a set of partial local sub-images (patches) which is a vital preprocessing step in many image processing applications. In this paper, a new software tool called patchIT is presented, providing an integrated framework suitable for the systematic and automatized extraction of patches from images based on user-defined geometrical and spatial criteria. Patches can be extracted in both a sliding and random manner and can be exported either as images, MATLAB .mat files, or raw text files. The proposed tool offers further functionality, including masking operations that act as spatial filters, identifying candidate patch areas, as well as geometric transformations by applying patch value indexing. It also efficiently handles issues that arise in large-scale patch processing scenarios in terms of memory and time requirements. In addition, a use case in cartographic research is presented that utilizes patchIT for map evaluation purposes based on a visual heterogeneity indicator. The tool supports all common image file formats and efficiently processes bitonal, grayscale, color, and multispectral images. PatchIT is freely available to the scientific community under the third version of GNU General Public License (GPL v3) on the GitHub platform