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This paper presents a special matrix factorization based on sparse representation that detects anomalies in video sequences generated with moving cameras. Such representation is made by associating the frames of the target video, that is a sequence to be tested for the presence of anomalies, with the frames of an anomaly-free reference video, which is a previously validated sequence. This factorization is done by a sparse coefficient matrix, and any target-video anomaly is encapsulated into a residue term. In order to cope with camera trepidations, domain-transformations are incorporated into the sparse representation process. Approximations of the transformed-domain optimization problem are introduced to turn it into a feasible iterative process. Results obtained from a comprehensive video database acquired with moving cameras on a visually cluttered environment indicate that the proposed algorithm provides a better geometric registration between reference and target videos, greatly improving the overall performance of the anomaly-detection system. [ABSTRACT FROM AUTHOR]

In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of abandoned object detection in a cluttered environment using a camera moving along a straight track. The developed system compares captured images to a previously recorded reference video, thus requiring proper temporal alignment and geometric registration between the two signals. A real-time constraint is imposed onto the system to allow an effective surveillance capability in practical situations. In this paper, we propose to deal with the simultaneous detection of objects of different sizes using a multiresolution approach together with normalized cross-correlation and a voting step. In order to develop and properly assess the proposed method we designed a database recorded in a real surveillance scenario, consisting of an industrial plant containing a large number of pipes and rotating machines. Also, we have devised a systematic parameter tuning routine that allows the system to be adapted to different scenarios. We have validated it using the designed database. The obtained results are quite effective, achieving real-time, robust abandoned object detection in an industrial plant scenario. [ABSTRACT FROM AUTHOR]

This paper proposes a method to detect anomalies in videos acquired by a camera mounted on a moving inspection robot. The proposed method is based on a spatio-temporal composition (STC) method, where a dense sampling is used to break the video into small 3D volumes that are used to calculate the probability of the spatio-temporal arrangements. This class of methods has been successfully used for surveillance videos obtained by static cameras. However, when applied to videos recorded by cameras on moving platforms, the STC gives a large number of false detections. In this work, we propose improvements to the present STC method that will alleviate this problem in two ways. First, a two-stage dictionary learning process is performed to allow a more reliable anomaly detection. Second, improved spatio-temporal features are employed. These modified features are extracted after an enhanced temporal filtering that performs a temporal regularization of the video sequence. The proposed approach gives very good results in the identification of anomalies without the need of background subtraction, motion estimation or tracking. The results are shown to be comparable or even superior to those of other state-of-the-art methods using bag-of-video words or other moving-camera surveillance methods. The system is accurate even with no prior knowledge of the type of event to be observed, being robust to cluttered environments, as illustrated by several practical examples. These results are obtained without compromising the performance of the algorithm in the static cameras case. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how visualization factors, such as disparity, mobility, angular resolution, and viewpoint interpolation, influence the quality of experience (QoE) in a stereoscopic multiview environment. In order to do so, we set up a dedicated testing room and conducted subjective experiments. We also developed a framework that emulates a supermultiview environment. This framework can be used to investigate and assess the effects of angular resolution and viewpoint interpolation on the QoE produced by multiview systems, and provide relevant cues as to how the baselines of cameras and interpolation strategies in such systems affect user experience. Aspects such as visual comfort, model fluidity, sense of immersion, and the three-dimensional (3D) experience as a whole have been assessed for several test cases. Obtained results suggest that user experience in an motion parallax environment is not as critically influenced by configuration parameters such as disparity as initially thought. In addition, extensive subjective tests have indicated that while users are very sensitive to angular resolution in multiview 3D systems, this sensitivity tends not to be as critical when a user is performing a task that involves a great amount of interaction with the multiview content. These tests have also indicated that interpolating intermediate viewpoints can be effective in reducing the required view density without degrading the perceived QoE. [ABSTRACT FROM PUBLISHER]

We present a novel method for the design of finite impulse response (FIR) filters with discrete coefficients that belong in the sum of powers-of-two (POT) space. The importance of this class of filters cannot be overstated, given the ever-increasing number of applications for which a specific hardware implementation is needed. Filters that have coefficients that belong to such a class are also referred to as multiplierless filters, given that the operations performed by the filter can all be implemented by using appropriately designed shifts of the input data, making them a perfect choice whenever implementation simplicity and processing speed are the ultimate goal. To produce such a design, we employ a vector successive approximation technique successfully used in data compression that has a very low computational complexity, the Matching Pursuits Generalized BitPlanes algorithm (MPGBP). We derive optimality conditions for the approximation dictionary. We compare filters obtained with the proposed method with those derived in previous works. Based on this comparative analysis, we show that this new and powerful way of producing the filters' coefficients is also among the simplest available in the literature. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the compression of waveforms obtained from measurements of power system quantities and analyze the reasons why its importance is growing with the advent of smart grid systems. While generation and transmission networks already use a considerable number of automation and measurement devices, a large number of smart monitors and meters are to be deployed in the distribution network to allow broad observability and real-time monitoring. This situation creates new requirements concerning the communication interface, computational intelligence and the ability to process data or signals and also to share information. Therefore, a considerable increase in data exchange and in storage is likely to occur. In this context, one must achieve an efficient use of channel communication bandwidth and a reduced need of storage space for power system data. Here, we review the main compression techniques devised for electric signal waveforms providing an overview of the achievements obtained in the past decades. Additionally, we envision some smart grid scenarios emphasizing open research issues regarding compression of electric signal waveforms. We expect that this paper will contribute to motivate joint research efforts between electrical power system and signal processing communities in the area of signal waveform compression. [ABSTRACT FROM PUBLISHER]

This study presents the application of a safe, cost effective, environmental friendly, and efficient technology for the removal of trivalent chromium ions from aqueous solutions, based on the valorisation of a renewable resource, Laminaria digitata seaweed. Insights into trivalent chromium speciation in solution and interaction with the active sites present in the surface of the brown algae were studied. Carboxyl and hydroxyl groups were identified as the major binding sites present in the surface of the biosorbent, in concentrations ( Qmax) of 2.06 ± 0.01 and 1.4 ± 0.7 mmol g −1, and with proton binding parameters ( pK) of 3.28 ± 0.01 and 11 ± 1, respectively. Trivalent chromium uptake at equilibrium conditions was well described at different acidic pH conditions and chromium concentrations, using a model which incorporates trivalent chromium hydrolysis reactions in the aqueous phase and its chemical interactions with the available active sites (carboxyl groups) present in the surface of biosorbent. The distribution profile of trivalent chromium species present in the solution as well as at the binding sites indicated that Cr 3+ and CrOH 2+ exhibit different affinities for the carboxyl groups present in the surface of the biomass according to the pH. A mass transfer kinetics model was applied to describe the kinetics at batch system, being possible to obtain the distribution of CrOH 2+ and Cr 3+ species in solution and at the binding sites. [ABSTRACT FROM AUTHOR]

Multiscale transforms are among the most popular techniques in the field of pixel-level image fusion. However, the fusion performance of these methods often deteriorates for images derived from different sensor modalities. In this paper, we demonstrate that for such images, results can be improved using a novel undecimated wavelet transform (UWT)-based fusion scheme, which splits the image decomposition process into two successive filtering operations using spectral factorization of the analysis filters. The actual fusion takes place after convolution with the first filter pair. Its significantly smaller support size leads to the minimization of the unwanted spreading of coefficient values around overlapping image singularities. This usually complicates the feature selection process and may lead to the introduction of reconstruction errors in the fused image. Moreover, we will show that the nonsubsampled nature of the UWT allows the design of nonorthogonal filter banks, which are more robust to artifacts introduced during fusion, additionally improving the obtained results. The combination of these techniques leads to a fusion framework, which provides clear advantages over traditional multiscale fusion approaches, independent of the underlying fusion rule, and reduces unwanted side effects such as ringing artifacts in the fused reconstruction. [ABSTRACT FROM AUTHOR]

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a pattern-matching-based algorithm for video compression. This algorithm, named multidimensional multiscale parser (MMP)-Video, is based on the H.264/AVC video encoder, but uses a pattern-matching paradigm instead of the state-of-the-art transform-quantization-entropy encoding approach. The proposed method adopts the use of multiscale recurrent patterns to compress both spatial and temporal prediction residues, totally replacing the use of transforms and quantization. Experimental results show that the coding performance of MMP-Video is better than the one of H.264/AVC high profile, especially for medium to high bit-rates. The gains range up to 0.7 dB, showing that, in spite of its larger computational complexity, the use of multiscale recurrent pattern matching paradigm deserves being investigated as an alternative for video compression. [ABSTRACT FROM AUTHOR]

The increasing use of power electronics in power systems causes a high injection of harmonic components which can in turn interfere with utility equipment and customer loads. Therefore, the correct estimation and measurement of harmonics have become an important issue. If the power frequency of the signal is steady and near the nominal value, the discrete Fourier transform (DFT) can be used and good estimation performance is achieved. However, there are considerable power frequency variations on isolated systems such as shipboard power systems, micro-grids and smart-grids. When these variations occur there may be significant errors in the estimates using the DFT. In order to deal with this problem, this work presents a novel technique based on demodulation of the power line signal and subsequent filtering for harmonics estimation. The main features of the proposed harmonic estimation technique are: i) precise and accurate estimation of harmonics of off-nominal frequencies and ii) fast estimation of harmonics (about two cycles of the fundamental component). Simulation results show that the proposed technique performs well in comparison with the DFT and can be a good candidate to replace it in cases where the power frequency is subject to considerable variations. [ABSTRACT FROM PUBLISHER]

This paper presents lossy compression algorithms which build on a state-of-the-art codec, the Set Partitioned Embedded Block Coder (SPECK), by incorporating a lattice vector quantizer codebook, therefore allowing it to process multiple samples at one time. In our tests, we employ scenes derived from standard AVIRIS hyperspectral images, which possess 224 spectral bands. The first proposed method, LVQ-SPECK, uses a lattice vector quantizer-based codebook in the spectral direction to encode a number of consecutive bands that is equal to the codeword dimension. It is shown that the choice of orientation codebook used in the encoding greatly influences the performance results. In fact, even though the method does not make use of a 3-D discrete wavelet transform, in some cases it produces results that are comparable to those of other state-of-the-art 3-D codecs. The second proposed algorithm, DWP-SPECK, incorporates the 1-D discrete wavelet transform in the spectral direction, producing a discrete wavelet packet decomposition, and simultaneously encodes a larger number of spectral bands. This method yields performance results that are comparable or superior to those attained by other 3-D wavelet coding algorithms such as 3D-SPECK and JPEG2000 (in its multi-component version). We also look into a novel method for reducing the number of codewords used during the refinement pass in the proposed methods which, for most codebooks, provides a reduction in rate while following the same encoding path of the original methods, thereby improving their performance. We show that it is possible to separate the original codebook used into two distinct classes, and use a flag when sending refinement information to indicate to which class this information belongs. In summary, given the results obtained by our proposed methods, we show that they constitute a viable option for the compression of volumetric datasets with large amounts of data. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the results of a multiscale pattern-matching-based ECG encoder, which employs simple preprocessing techniques for adapting the input signal. Experiments carried out with records from the Massachusetts Institute of Technology-Beth Israel Hospital database show that the proposed scheme is effective, outperforming some state-of-the-art schemes described in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves rele- vant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types. [ABSTRACT FROM AUTHOR]

In this brief, we present new preprocessing techniques for electrocardiogram signals, namely, dc equalization and complexity sorting, which when applied can improve current 2-D compression algorithms. The experimental results with signals from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database outperform the ones from many state-of-the-art schemes described in the literature. [ABSTRACT FROM AUTHOR]

In this paper, the multidimensional multiscale parser (MMP) is employed for encoding electromyographic signals. The experiments were carried out with real signals acquired in laboratory and show that the proposed scheme is effective, outperforming even wavelet-based state-of- the-art schemes present in the literature in terms of percent root mean square difference × compression ratio. [ABSTRACT FROM AUTHOR]

In this work, we further develop the multidimensional multiscale parser (MMP) algorithm, a recently proposed universal lossy compression method which has been successfully applied to images as well as other types of data, as video and ECG signals. The MMP is based on approximate multiscale pattern matching, encoding blocks of an input signal using expanded and contracted versions of patterns stored in a dictionary. The dictionary is updated using expanded and contracted versions of concatenations of previously encoded blocks. This implies that MMP builds its own dictionary while the input data is being encoded, using segments of the input itself, which lends it a universal flavor. It presents a flexible structure, which allows for easily adding data-specific extensions to the base algorithm. Often, the signals to be encoded belong to a narrow class, as the one of smooth images. In these cases, one expects that some improvement can be achieved by introducing some knowledge about the source to be encoded. In this paper, we use the assumption about the smoothness of the source in order to create good context models for the probability of blocks in the dictionary. Such probability models are estimated by considering smoothness constraints around causal block boundaries. In addition, we refine the obtained probability models by also exploiting the existing knowledge about the original scale of the included blocks during the dictionary updating process. Simulation results have shown that these developments allow significant improvements over the original MMP for smooth images, while keeping its state-of-the-art performance for more complex, less smooth ones, thus improving MMP's universal character. [ABSTRACT FROM AUTHOR]

In this letter, we address rate-distortion-optimum compression of signals from electric power system disturbances, using atomic decompositions. Usually, such optimization is obtained assuming a single dictionary and consists of finding the best compromise between the quantization of the coefficients in the atomic decomposition and its number of terms. Here, several parameterized dictionaries are used instead. This allows the selection of the dictionary leading to the best rate-distortion (R-D) compromise. Distinct dictionaries correspond to different quantizers for the parameters of the atoms. Side information must be transmitted in order to indicate the dictionary employed. The R-D performance in this case depends on a complex interplay between the quantizers of the parameters of the atoms and the coefficient quantizers. Using a training stage, we select a reduced set of parameter and coefficient quantizers that give near-optimum R-D performance. Simulation results show that the proposed scheme indeed achieves near-optimum R-D performance with low computational complexity in the coding stage. [ABSTRACT FROM AUTHOR]

For the compression of stereo images, several techniques employing the concept of disparity compensation have been developed. In this paper, we present a new method for stereo-image compression which is based on a different paradigm. It relies on approximate matching of multiscale recurrent patterns, which is an extension of ordinary pattern matching, where an image block is represented using dilated, contracted, displaced, and deformed versions of blocks already processed. To achieve this, the input image is segmented into variable-sized blocks. Each segment is represented by an element in a dictionary. The segmentation is performed according to a rate x distortion criterion, and the dictionary is updated with the concatenation, contraction, expansion, displacement, and deformation of previously coded elements. Unlike most other methods, in this approach the disparity map is not transmitted. That is, the generation of the error images, as well as their coding and transmission, is not necessary. In brief, the proposed method presents high-quality results at the decoder end, compared to other coding methods for stereo images that transmit the disparity map. [ABSTRACT FROM AUTHOR]

This paper presents coherent representations of electric power systems signals. These representations are obtained by employing adaptive signal decompositions. They provide a tool to identify structures composing a signal and constitute an approach to represent a signal from its identified components. We use the Matching Pursuits algorithm, which is a greedy adaptive decomposition, that has the potential of decomposing a signal into coherent components. The dictionary employed is composed of damped sinusoids in order to obtain signal components closely related to power systems phenomena. In addition, we present an effective method to suppress the pre-echo post-echo artifacts that often appear when using the Matching Pursuits. However, the use of a dictionary of damped sinusoids alone does not ensure that the decomposition will be meaningful in physical terms. To overcome this constraint, we develop a technique leading to efficient coherent damped-sinusoidal decompositions that are closely related to the physical phenomena being observed. The effectiveness of the proposed method for compression of synthetic and natural signals is tested, obtaining high compression ratios along with high signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Recent outstanding results of supervised object detection in competitions and challenges are often associated with specific metrics and datasets. The evaluation of such methods applied in different contexts have increased the demand for annotated datasets. Annotation tools represent the location and size of objects in distinct formats, leading to a lack of consensus on the representation. Such a scenario often complicates the comparison of object detection methods. This work alleviates this problem along the following lines: (i) It provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages; (ii) it examines the most used annotation formats, showing how different implementations may influence the assessment results; and (iii) it provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets. In addition, this work proposes a new metric, also included in the toolkit, for evaluating object detection in videos that is based on the spatio-temporal overlap between the ground-truth and detected bounding boxes. [ABSTRACT FROM AUTHOR]