CHAIR: automatic image registration based on correlation and Hough transform

Automatic image registration is a process related to several application fields: remote sensing, medicine and computer vision, among others. Particularly in the field of remote sensing, the ever-increasing number of available satellite images requires automatic image registration methods, capable of correctly aligning a new image. An automatic image registration method – CHAIR (correlation- and Hough transform-based method of automatic image registration) – is proposed, the key concept of which relies on the ‘correlation image’ produced in both the horizontal and vertical directions. In particular, the computation of the distance of an identified diagonal brighter strip in the correlation image (through the Hough transform) to an offset (the main diagonal) allows for the determination of translational shifts and consequently control points. The set of obtained control points allows for the correction of several types of distortions. The geometric correction quality achieved by CHAIR was objectively evaluated through measures recently proposed, which allow for a more complete assessment of the obtained results. The CHAIR performance was evaluated on both synthetic and real data, with different spatial resolutions and spectral contents. CHAIR has been shown to be able to correctly align two images with a subpixel accuracy, having a priori a ‘gold standard’ image covering a considerable part of the image to be registered, and has also been shown to work for images of different sensors and/or different spectral bands, situations where traditional correlation methods often yield low and smooth peaks on the correlation surface. It is also able to account for elevation differences and to some extent for rotation and scale effects. Furthermore, it has been shown to have potential for registering synthetic aperture radar (SAR) with optical images.     

A measure for mutual refinements of image segmentations.

In this paper, we recover a graph interpretation of the mutual partition distance, proposed recently by Cardoso and Corte-Real. We deduce some properties of this measure, and establish a correspondence with the partition distance introduced by Almudevar and Field and Gusfield, and independently by Guigues. We also present some different formulations for the computation of the mutual partition distance. Finally, a comparison is made with similar measures.     

A very low bit rate video coder based on vector quantization

Describes a video coder based on a hybrid DPCM-vector quantization algorithm that is suited for bit rates ranging from 8-16 kb/s. The proposed approach involves segmenting difference images into variable-size and variable-shape blocks and performing segmentation and motion compensation simultaneously. The purpose of obtaining motion vectors for variable-size and variable-shape blocks is to improve the quality of motion estimation, particularly in those areas where the edges of moving objects are situated. For the larger blocks, decimation takes place in order to simplify vector quantization. For very active blocks, which are always of small dimension, a specific vector quantizer has been applied, the fuzzy classified vector quantizer (FCVQ). The coding algorithm described displays good performance in the compression of test sequences at the rates of 8 and 16 kb/s; the signal-to-noise ratios obtained are good in both cases. The complexity of the coder implementation is comparable to that of conventional hybrid coders, while the decoder is much simpler in this proposal.     

Filling the gap in quality assessment of video object tracking

Current evaluation methods either rely heavily on reference information manually annotated or, by completely avoiding human input, provide only a rough evaluation of the performance of video object tracking algorithms. The main objective of this paper is to present a novel approach to the problem of evaluating video object tracking algorithms. It is proposed the use different types of reference information and the combination of heterogeneous metrics for the purpose of approximating the ideal error. This will enable a significant decrease of the required reference information, thus bridging the gap between metrics with different requirements concerning this type of data. As a result, evaluation frameworks can aggregate the benefits from individual approaches while overcoming their weaknesses, providing a flexible and powerful tool to assess and characterize the behavior of the tracking algorithms.     

Proapoptotic activity of a Trypanosoma cruzi ceramide-containing glycolipid turned on in host macrophages by IFN-gamma

The effects of glycoinositolphospholipid (GIPL), from the pathogenic protozoan Trypanosoma cruzi, and its isolated glycan and lipid (dihydroceramide) components, were investigated in J774 cells and primary macrophages. Isolated GIPL ceramide, but not intact GIPL or its glycan, induced intense fluid phase endocytosis when added exogenously. In the presence of the cytokine IFN-gamma, GIPL ceramide induced marked apoptosis in J774 cells and macrophages, independent of nitric oxide secretion. When cells were preincubated with the GIPL-derived glycan chain, addition of intact GIPL induced macrophage apoptosis in the presence of IFN-gamma. Synthetic C2-dihydroceramide also induced apoptosis in the presence of IFN-gamma. Induction of apoptosis in T. cruzi-infected macrophages by GIPL ceramide plus IFN-gamma led to increased parasite release compared with IFN-gamma treatment alone. Viable parasites released comprised both infective trypomastigote and spheromastigote forms. These results identify a novel pathway by which T. cruzi glycosylphosphatidylinositol family molecules affect host macrophages, with implications for the infectious process.     

Network flow models for the local access network expansion problem

In the local access network expansion problem we need to expand a given network (with tree topology) due to traffic demand increase. We can expand the network either by increasing the capacity of its edges and/or by installing equipments that concentrate the traffic of several demand points. This problem has received some attention in the literature. Here, we view the problem as an extension of the well-known capacitated minimum spanning tree problem. We adapt two types of flow-based formulations, aggregated and disaggregated formulations and present some valid inequalities to improve the linear programming bounds of the previous formulations.     

Automatic Calibration of the SHETRAN Hydrological Modelling System Using MSCE

Automatic calibration is preferred because it provides an objective and extensive searching in the feasible parameter space. In this study, the Modified Shuffled Complex Evolution (MSCE) optimization algorithm is applied to automatically calibrate the physically-based spatially-distributed hydrological model SHETRAN in the 705-km² semi-arid Cobres basin in southern Portugal, with a spatial resolution of 2 km and a temporal resolution of 1 h. Twenty-two parameters are calibrated for the main types of land-use and soil. Nash-Sutcliffe Efficiency (NSE) is 0.86 for calibration and 0.74 for validation for basin outlet; NSE is respectively 0.65 and 0.82 for calibration, 0.69 and 0.63 for validation of internal gauging stations Albernoa and Entradas. As for storm events, NSE is 0.87 and 0.64 respectively for Storms No.1 (during the calibration period) and No.4 (during the validation period) at basin outlet; it is 0.69 and 0.65 for Storm No.4 respectively at Albernoa and Entradas. The results are satisfactory not only for basin outlet but also for internal gauging stations.     

BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction

Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task that has attracted the attention of many researchers over the last decades. State-of-the-art methods are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, coined BMOG, that significantly boosts the performance of a widely used method based on a Mixture of Gaussians. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update. The complexity of BMOG is kept low, proving its suitability for real-time applications. BMOG was objectively evaluated using the ChangeDetection.net 2014 benchmark. An exhaustive set of experiments was conducted, and a detailed analysis of the results, using two complementary types of metrics, revealed that BMOG achieves an excellent compromise in performance versus complexity.     

A fuzzy classified vector quantizer for image coding

Vector quantization of images raises problems of complexity in codebook search and subjective quality of images. The family of image vector quantization algorithms proposed in this paper addresses both of those problems. The fuzzy classified vector quantizer (FCVQ) is based on fuzzy set theory and consists basically in a method of extracting a subcodebook from the original codebook, biased by the features of the block to be coded. The incidence of each feature on the blocks is represented by a fuzzy set that captures its (possibly subjective) nature. Unlike the classified vector quantizer (CVQ), in the FCVQ a specific subcodebook is extracted for each block to be coded, allowing a better adaptation to the block. The CVQ may be regarded as a special case of the FCVQ. In order to explore the possible correlation between blocks, an estimator for the degree of incidence of features on the block to be coded is included. The estimate is based on previously coded blocks and is obtained by maximizing a possibility; a distribution that intends to represent the subjective knowledge on the feature's possibility of occurrence conditioned to the coded blocks is used. Some examples of the application of a FCVQ coder to two test images are presented. A slight improvement on the subjective quality of the coded images is obtained, together with a significant reduction on the codebook search complexity and, when applying the estimator, a reduction of the bit rate     

Toward a generic evaluation of image segmentation.

Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures.