With the introduction of various hardware/software technologies such as Cloud Technologies or Virtualization technologies, there has been a great potential to reuse ICT resources. These technologies also provide various advantages including resource consumption reduction (in the use phase only or in the whole life cycle). In addition, there are additional potentials regarding the resource opacity introduced by these technologies. These potentials could improve cloud solutions and enable tweakability of components' nonessential features that may be not captured in the component models. This is especially of great advantage in the case of heterogeneous/disaggregated infrastructure where developing comprehensive models to cover everything is practically impossible. In this work, by leveraging on the concept of pathways, we develop a few mechanisms that enable transparency and therefore tweakability of features even in the presence of abstraction and heterogeneity. In particular, the layered-stack approach to system decomposition is considered because of its association with software defined networking (SDN). For a concrete example, the case of dynamic frequency scaling of processors is considered and it is shown that the associated consumption could be considerably reduced without requiring additional changes to the middle-layer components.

Among the innovative approaches to reduce the greenhouse gas (GHG) emissions of data centres during their use phase, electrical power from renewable sources appears promising. However, renewable electricity is often intermittent due to meteorological conditions. Consequently, the regional availability of renewable power varies constantly over time. This created the opportunity to deploy cloud computing systems relying on data centres located in different regions. Cloud computing technology enables real-time load migration to a data centre in the region where the GHG emissions per kWh are the lowest. While this approach is becoming popular to manage distributed data centres, there is still room for improvement in its implementation. Indeed, the consequences of data centre power demand migrations across electric networks and the resulting GHG emissions are usually neglected. In this project, we developed a novel GHG emission factor based on the sources of electricity affected by the server load migrations. Then, we used this emission factor in a simulation of distributed data centres to minimize their GHG emissions. Results show, the use of the novel emission factor enables an extra reduction of 23% of GHG emissions as compared to the usual approach.

In this work, a new region-based, multipath-enabled packet routing is presented and called Smart Packet Routing. The proposed approach provides several opportunities to re-distribute the smartness and decision making among various elements of a network including the packets themselves toward providing a decentralized solution for SDNs. This would bring efficiency and scalability, and therefore also lower environmental footprint for the ever-growing networks. In particular, a region-based representation of the network topology is proposed which is then used to describe the routing actions along the possible paths for a packet flow. In addition to a region stack that expresses a partial or full regional path of a packet, QoS requirements of the packet (or its associated flow) is considered in the packet header in order to enable possible QoS-aware routing at region level without requiring a centralized controller.

The first competition on the MultiSpectral Text Extraction (MS-TEx) from historical document images has been organized in conjunction with the ICDAR 2015 conference. The goal of this contest is evaluation of the most recent advances in text extraction from historical document images captured by a multispectral imaging system. The MS-TEx 2015 dataset contains 10 handwritten and machine-printed historical document images along with eight spectral images for each image. This paper provides a report on the methodology and performance of the five submitted algorithms by various research groups across the world. The objective evaluation and ranking was performed by using well-known evaluation metrics of binarization and classification.

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.

In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices.

In this paper, a phase-based binarization model for ancient document images is proposed, as well as a postprocessing method that can improve any binarization method and a ground truth generation tool. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing. In the preprocessing and main binarization steps, the features used are mainly phase derived, while in the postprocessing step, specialized adaptive Gaussian and median filters are considered. One of the outputs of the binarization step, which shows high recall performance, is used in a proposed postprocessing method to improve the performance of other binarization methodologies. Finally, we develop a ground truth generation tool, called PhaseGT, to simplify and speed up the ground truth generation process for ancient document images. The comprehensive experimental results on the DIBCO'09, H-DIBCO'10, DIBCO'11, H-DIBCO'12, DIBCO'13, PHIBD'12, and BICKLEY DIARY data sets show the robustness of the proposed binarization method on various types of degradation and document images.

In recent years, a large number of binarization methods have been developed, with varying performance generalization and strength against different benchmarks. In this work, to leverage on these methods, an ensemble of experts (EoE) framework is introduced, to efficiently combine the outputs of various methods. The proposed framework offers a new selection process of the binarization methods, which are actually the experts in the ensemble, by introducing three concepts: confident ness, endorsement and schools of experts. The framework, which is highly objective, is built based on two general principles: (i) consolidation of saturated opinions and (ii) identification of schools of experts. After building the endorsement graph of the ensemble for an input document image based on the confident ness of the experts, the saturated opinions are consolidated, and then the schools of experts are identified by thresholding the consolidated endorsement graph. A variation of the framework, in which no selection is made, is also introduced that combines the outputs of all experts using endorsement-dependent weights. The EoE framework is evaluated on the set of participating methods in the H-DIBCO'12 contest and also on an ensemble generated from various instances of grid-based Sauvola method with promising performance.

For the purpose of facilitating benchmark contributions for binarization methods, a new fast ground truthing approach, called the PhaseGT, is proposed. This approach is used for building the 1stgroundtruthed Persian Heritage Image Binarization Dataset (PHIBD 2012). The PhaseGT is a semiautomatic approach to ground truthing of images of any language, especially designed for historical document images. The main goal of the PhaseGT is to accelerate the ground truthing process and reduce the manual ground truthing effort. It uses the phase congruency features to preprocess the input image and to provide a more accurate initial binarization to the human expert who performs the manual part. This preprocessing is in turn based on a priori knowledge that is provided by human user. The PHIBD 2012 dataset contains 15 historical document images with their corresponding ground truth binary images. The historical images in the dataset suffer from various types of degradation. It has been also divided into two subsets of training and testing images for those binarization methods that use learning approaches.

Preprocessing and post processing steps significantly improve the performance of binarization methods, especially in the case of severely-degraded historical documents. In this paper, an unsupervised post processing method is introduced based on the phase-preserved denoised image and also phase congruency features extracted from the input image. The core of the method consists of two robust mask images that can be used to cross out false positive pixels on the output of the binarization method. First, a mask with a high recall value is obtained from the denoised image using morphological operations. In parallel, a second mask is obtained based on phase congruency features. Then, a median filter is used to remove noise on these two masks, which then are used to correct the output of any binarization method. This approach was tested along with several state-of the-art binarization methods on the DIBCO'09, H-DIBCO'10, DIBCO'11 and H-DIBCO'12 datasets with promising and robust results. Furthermore, the high performance of the proposed masks shows their potential use as unsupervised semi-ground truth generator for learning-based binarization methods.

With raising concerns about global warming and environmental impacts of Greenhouse Gases (GhGs) emissions, energy efficiency and carbon footprint reduction attracted many researchers to provide efficient models and tools for energy, carbon, and cost estimation and management. In this paper, a model for measuring the energy consumption and carbon footprint of an individual virtual machine is presented based on resource usage and performance monitoring counters. A simple cost model is represented in order to evaluate the energy consumption and carbon footprint models. The model evaluated on a simulated virtual private cloud with different methodologies such as server consolidation and multi-level grouping heuristic algorithms. The results show that such heuristic algorithms are able to significantly reduce the cost of energy and carbon footprint of an individual virtual machine in comparison with other methodologies such as server consolidation. The results also show that this cost reduction efficiency is positively correlated to the increase in carbon footprint tax rates.

Perceptual analysis is an interesting topic in the field of image processing, and can be considered a missing link between image processing and human vision. Of the various forms of perception, one of the most important and best known is shape perception. In this work, a framework based on the online non local patch means (NLPM) method is developed, which is designed to infer possible perceptual observations of an input image using the knowledge images provided. Thanks to the speed of online NLPM, the proposed method can simulate the transformation of the input image to the final perceptual image in real time. In order to improve the performance of the method, a hidden chain series is considered for the model that delivers faster convergence. The capability of the method is evaluated on several well-known perceptual examples.

This paper describes the steps that have been undertaken in order to develop the IBN SINA database, which is designed to apply learning techniques in the processing and understanding of document images. The description of the preparation process, including preprocessing, feature extraction and labeling, is provided. The database has been evaluated using classification techniques, such as the SVM classifiers. In order to make the database compatible with these classifiers, the labels of the shapes have been translated into a set of bi-class problems. Promising results with the SVM classifiers have been obtained.

The goal of this paper is to correct bleed-through in degraded documents using a variational approach. The variational model is adapted using an estimated background according to the availability of the verso side of the document image. Furthermore, for the latter case, a more advanced model based on a global control, the flow field, is introduced. The solution of each resulting model is obtained using wavelet shrinkage or a time-stepping scheme, depending on the complexity and nonlinearity of the models. When both sides of the document are available, the proposed model uses the reverse diffusion process for the enhancement of double-sided document images. The results of experiments with real and synthesized samples are promising. The proposed model, which is robust with respect to noise and complex background, can also be applied to other fields of image processing.

A complete system for preprocessing and word spotting of very old historical document images is presented. Document images are processed for extraction of salient information using a word spotting technique which does not need line and word segmentation and is language independent.A multi-class library of connected components of document text is created based on six features. The spotting is performed using Euclidean distance measure enhanced by rotation and dynamic time wrapping transforms. The method is applied to a dataset from Juma Al Majid Center (Dubai)with promising results. A promising performance of the word spotting technique is obtained using an automatic preprocessing stage. In this stage, using content-level classifiers, accurate stroke pixels are extracted in a robust way. The preprocessed document images are also more legible to the end user and are less costly to archive and transfer.

Segmentation of ancient documents is challenging. In the worst cases, text characters become fragmented as the results of strong degradation processes. New active contour methods allow to handle difficult cases in a spatially coherent fashion. However, most of those method use a restrictive, a priori shape information that limit their application. In this work, we propose to address this issue by combining two complementary approaches. First, multi-level classifiers, which take advantage of the stroke width a priori information, allow to locate candidate character pixels. Second, a level set active contour scheme is used to identify the boundary of a character. Tests have been conducted on a set of ancient degraded Hebraic character images. Numerical results are promising.

A novel method for the segmentation of double-sided ancient document images suffering from bleed-through effect is presented. It takes advantage of the level set framework to provide a completely integrated process for the segmentation of the text along with the removal of the bleed-through interfering patterns. This process is driven by three forces: 1) a binarization force based on an adaptive global threshold is used to identify region of low intensity, 2) a reverse diffusion force allows for the separation of interfering patterns from the true text, and 3) a small regularization force favors smooth boundaries. This integrated method achieves high quality results at reasonable computational cost, and can easily host other concepts to enhance its performance. The method is successfully applied to real and synthesized degraded document images. Also, the registration problem of the double-sided document images is addressed by introducing a level set method; the results are promising.

The non-local means filter is one of powerful denoising methods which allows participation of far, but proper pixels in the denoising process. Although the weights of non-similar pixels are very small, high number of these pixels results in introduction of blur. In this work, we introduce an automatic and robust method to select the best candidate pixels based on their similarity to the target pixel. This method is based on graphs partitioning and uses Markovian clustering on the pixel adjacency graph (PAG). In this way, a set of relevant pixels is obtained that is used in weighted averaging for denoising each pixel. To evaluate the method, denoising of the natural images is conducted, and the results are compared to the standard NLM filter and the SVD-based method. The results are promising.