Hybrid-software-defined networks (HSDN) have opened up an enormous range of functionality and benefits for network users and administrators. Many of these functionalities have been extensively studied and addressed over the past few years. However, it is not a panacea as it brings with it many design challenges to get a robust and reliable network. In this paper, we focus on one of the SDN challenges: the incremental deployment of SDN devices in HSDN treated as an optimization problem. In this context, we developed a mixed integer linear programming (MILP) and a genetic algorithm (GA) considering a hybrid deployment scheme that combines current incremental deployment and replaced deployment techniques. The proposed strategy determines which traditional devices will be replaced by SDN devices, which SDN devices will be added to the network changing its topology, and how the traffic will be routed. Consequently, the aim is to minimize the deployment cost, minimize the routing cost and maximize the traffic controlled by the SDN network simultaneously. The experiments show that the proposed hybrid deployment approach is promising compared to current techniques, and the GA is more robust and scalable than the MILP as the traffic volume increases.

Special devices capable of transforming continuous images into digital formats generate digital images. Image digitization enables treating, visualizing and storing images in a computer system. However, the high-intensity light captured by a device of this type can cause unwanted bright areas in the generated images. Since these bright areas can lead to image processing errors, manifested as a false appearance, their deletion and subsequent reconstruction can produce a more faithful image. A conventional approach, based on mathematical morphology, consists of reducing or eliminating unwanted image brightness by an erosion process. The image obtained through this method undergoes a reconstruction process based on successive geodesic dilations. In this paper, a new brightness reduction method is proposed. This method identifies bright areas by applying a defined brightness limit value to remove pixels with brightness values above it, but leaving the remaining pixels unmodified. Then, the image is reconstructed by the usual image reconstruction approach based on mathematical morphology. Compared to the conventional approach, the proposed method in this work enhances brightness, generating an image more faithful to the actual object.

The acquisition of microscopic images of minerals with good contrast is critical for the identification and analysis of their properties. However, in many cases, the microscopic images of minerals obtained are unclear due to the image environment, imperfect adjustment of the microscopy operators or improper collection of samples. In this paper, we present an algorithm to enhance the microscopic images of minerals by multiscale Top-Hat transform using contrast adjustment weights. First, the multiple dark and bright features of the mineral image are extracted using the top-hat transform. Secondly, bright scale differences and dark scale differences obtained in the previous step are calculated. Third, all the intensities of the multiple dark and bright features from the previous steps are summed separately. Finally, the bright features adjusted for a contrast weight are then added to the image and dark features adjusted for the same weight are subtracted from the image. Experimental results on various kinds of microscopic mineral images verified the effective performance of this proposed enhancing the contrast, improving the detail and spatial information about the images

Infrared images (IR) help us to detect hidden targets in the environment, according to the radiation they emit. These work well on the day, at night and in weather conditions such as rain or fog. At the same time, visible images (VIS) provide us with good details of the scenes, which are better perceived by the human eye. Therefore, the fusion of an infrared image and a visible image of the same scene is very useful. In this paper, we propose a fusion method of visible and infrared images using a multiscale morphological approach. First, the base image is generated through the fusion of the two source images. Second, multiple bright and dark features are extracted from each source image by the top-hat transform. Third, the multiple bright and dark scales of the source images are fused. Finally, the fusion of the visible and infrared images is obtained by adding to the base image the maximum values obtained in the previous step. The results show that the proposed method is competitive compared to state-of-the-art methods in terms of contrast, brightness, texture and spatial information.

Software defined networking (SDN) is an emerging technology based on the separation of the control plane and the data plane. This allows to obtain benefits, in comparison with traditional networks, in terms of network management, global monitoring-control, cost reduction, and in particular the energy saving by the strategic activation of devices. In this paper, we propose an approach that seeks to minimize the global energy consumption of the network by suspending inactive devices, such as chassis and line cards, as well as limiting the use of links in traffic sessions. For this purpose, we developed an Integer Linear Programming (ILP) model for the SDN routing problem in order to obtain the minimum energy consumption, subject to satisfy all traffic demands. The experimental results on two network topologies for a set of static traffic requests indicate that the proposed model is promising, saving up to 42% of the global energy consumption obtaining a better performance to the models proposed in the literature. On the other hand, the experimental results for incremental semi-dynamic traffic indicate that the performance of the optimization with re-routing improves the approach without re-routing when increasing the traffic in the network, but this improvement is not always perceptible. The approach without re-routing in terms of scalability is promising, by increasing the traffic load not generate interruptions to the traffic already attended and affect the quality of the service.

Extracting useful image features and preserving details effectively is a crucial part of fusion of images. Infrared images can distinguish objects from their background based on the difference in radiation. In the other hand, visible images can provide textured details consistent with the human visual system. The fusion of these two types of images can combine the advantages of thermal radiation information in infrared images and detailed texture information in visible images. In this work, we propose an algorithm of fusion of infrared and visible images using the multiscale top-hat transformation. The extraction of bright and dark regions from the images is done using two structuring elements. This algorithm provides significantly better results in contrast, brightness and texture than other state-of-theart algorithms.

Morphological mathematics applied to color images requires a color space and an ordering method to be able to define a complete lattice. Among the drawbacks in the methods proposed in the current literature we find some that produce false colors, or are inefficient at computation time. False colors are undesirable in the process of transformation as they alter original colors of the image, turning them into noise. On the other hand, any vector sorting method must be efficient enough to meet the requirements of procedures that do not allow for lengthy response times. In this work we propose a method of order based on Loewners order, using a transformation of the colors associated with the pixels of an image in the RGB space to symmetric matrices. In order to bring about a comparison of the results obtained through the proposed method and the others, we tested on a consistent basis, measuring the computation time in the process of basic operations, such as dilation and erosion, checked the generation of no false colors, removal of noise (smoothing), brightness reduction and local contrast enhancement, all in the same computing environment. The results demonstrate the validity of the method, resulting in efficiency of time and quality results, as well as producing no false colors.

Due to its versatility, the image processing area offers a very wide range of techniques to solve challenges in an effective way linked to fields such as medicine, agriculture, biology, industrial automation and document processing. Therefore a correct and advanced training of professionals in this area is an important task. In this sense, a new educational image processing tool is currently being developed at the Facultad Politecnica of the Universidad Nacional of Asuncion. The development focused on improving the interaction between students and teachers and also showing new advances in digital image processing area. To achieve this goal, a tool is being developed to be expanded in the future to be adapted to new challenges and different audiences. The first stage of development was completed, which allowed developing an extendable basic tool in the near future.

In certain medical images, it is possible to achieve contrast enhancement at different levels, in order to highlight different structures present therein. This could be useful to medical specialists to perform more specific diagnoses, in chest radiographs and mammograms, where it is possible to highlight different details when contrast is enhanced. Parameter tuning for Contrast Limited Adaptive Histogram Equalization (CLAHE) using a multi-objective meta-heuristic (SMPSO) is proposed, where the objective functions are the maximization of the amount of information available (via Entropy) and minimization of distortion in the resulting images (Structural Similarity Index, SSIM) simultaneously. The results show that our approach calculates a set of non-dominated solutions or Pareto Set, which represents images with different contrast levels and different levels of commitment between Entropy and Structural Similarity Index. Particularly, these objective functions are contradictory. These enhanced images provide useful information for decision making of specialists.

In this paper it is considered the image registration (IR) between medical images of computed tomography and magnetic resonance. Our approach formulates the IR as an optimization problem where mutual information cost function is used as a similarity metric (cost function). The Extremal Optimization algorithm is implemented as the optimizer. The numerical results are contrasted against two state of the art optimization algorithms for this kind of problems (being one deterministic and another evolutionary). Our approach is competitive with the deterministic algorithm in accuracy and with the evolutionary algorithms in computational cost. The qualitative results are quite satisfactory with a 83 % of success, whilst the quantitative results present an average error of 0.36mm with registrations of CT with proton density MR. The results show that the proposal is useful for multimodal registrations.

Mathematical morphology, based on lattice theory, is a nonlinear technique. In color image processing, it is necessary to determine a color space and an ordering to obtain a lattice structure. The classical lexicographical ordering is a total ordering where the choice of the main color component is not a trivial issue. In this work, to avoid this choice, a vectorial method in additive and subtractive color spaces is proposed. The method first consists of a pre-ordering relation based on the image local intensity and a second ordering that ensures a total ordering, in the case that the order between two pixels cannot be established. Experimental results based on morphological erosion and dilation show the proposed approach to be promising in processing color images.

The hemoflagellate protozoan parasite Trypanosoma cruzi is the causative agent of the Chagas disease. The two drugs used clinically have a high level of toxicity and are active only during the acute phase of the disease, making it urgent the development of new safe and effective treatment. The first step in the screening of new compounds is to identify their effectiveness by manual microscopic counting of the intracellular parasite form (amastigotes), which is a slow and tedious methodology. This paper presents an approach for the automatic counting of intracellular parasites using watershed transform with internal and external markers as segmentation technique, and connected components labeling for subsequent counting. The contrast against the classic counting, conducted by experts in the field, validated the technique, showing that the proposal is very efficient and with a low error rate.

This article introduces a partial matching framework, based on set theory criteria, for the measurement of shape similarity. The matching framework is described in an abstract way because the proposed scheme is independent of the selection of a segmentation method and feature space. This paradigm ensures the high adaptability of the algorithm and brings the implementer a wide control over the robustness, the ability to balance between selectivity and sensitivity, and the freedom to deal with more general and arbitrary image transformations required for some particular problem. A strategy to establish a descriptor set obtained from components segmented from the main shape is expounded, and two exclusion measure functions are formulated. Proofs are given to show that it is not required to match the entire descriptor sets to determine that two shapes are similar. The methodology provides a dissimilarity score that may be used for shape-based retrieval and object recognition; this is demonstrated applying the proposed approach in a cattle brand identification system.

We propose a method for automatic segmentation of brain magnetic resonance images (MRI) using a new approach based on learning. The learning process uses only two images, the original one and its ideal segmented version to generate the decision matrix for each pixel. Reusing the knowledge acquired in the decision matrix carries the segmentation of another similar images. New images are segmented by means of a strategy based on the nearest neighbors, that seeks the best solution in the decision matrix. Performed tests on magnetic resonance nonenhancing images showed promising results in segmenting nonenhancing brain tumors. The main advantages of this method are the facility to faithfully reproduce the objectives of the user, the use of only two images and it does not require the use of heuristic parameters neither the interaction of a specialist user after the learning process.

This article describes a new segmentation by thresholding approach based on learning. The method consists in learning to threshold correctly submitting both an image and its ideal thresholded version. From this stage it is generated a decision matrix for each pixel and each gray level that is re-utilized at the moment of the new images segmentation. The new image is thresholded by means of a new strategy based on the nearest neighbors, that seeks, for each pixel of this new image, the best solution in the decision matrix. Performed tests on handwritten documents showed promising results. In terms of quality of the results, the developed technique is equal or superior to the traditional segmentation by thresholding techniques, with the advantage that the one discussed here does not requires the use of heuristic parameters. 1.