Traditional image and video enhancement techniques do not consider the subjective preferences of the user, who might be interested in modifying or highlighting specific elements of a scene. In fact, most image or video post-processing techniques are applied to the entire image or frame. In this work, a framework for selective video enhancement is presented. It is based on an adaptive multi-resolution edge enhancement technique performed in the Laguerre–Gauss complex wavelet domain. In the proposed scheme, edges are selectively enhanced or attenuated according to the inputs of the user, taking into account the characteristics of the content, the response of the human visual system, and the masking effects induced by textured background. Two scenarios have been implemented and tested: (i) an interactive video editing system, where the user selects the objects to enhance through a graphical interface and the system automatically propagates the selection through the following video frames belonging to the same shot, (ii) a perceptually-driven technique to improve the quality of RGB plus Depth videos, which uses depth maps as additional features to guide the enhancement.

Light field imaging is increasingly spreading and its applications are reaching a larger number of users. The quality of the rendered content as perceived by the viewers is a key factor for the future development of this technology. Many factors can cause a degradation of the Light Field quality, from data acquisition/creation to the final rendering. To increase the perceived quality of the Light Field content, as usually performed for 2D and 3D content, processing techniques can be applied. In this work, an approach for enhancing Light Field videos is presented. The proposed approach is based on the characteristics of the human visual system and relies on the estimation of depth, motion and saliency. Those features guide a selective enhancement that is implemented in the Laguerre-Gauss Transform domain. The performed tests show the effectiveness of the proposed method.

In this paper, a multi-resolution method for depth estimation from dense image arrays is presented. Recent progress in consumer electronics has enabled the development of low cost hand-held plenoptic cameras. In these systems, multiple views of a scene are captured in a single shot by means of a micro-lens array placed on the focal point of the first camera lens, in front of the imaging sensor. These views can be processed jointly to obtain accurate depth maps. In this contribution, to reduce the computational complexity associated to global optimization schemes based on match cost functions, we make a local estimate based on the maximization of the total log-likelihood spatial density aggregated along the epipolar lines corresponding to each view pair. This method includes the local maximum likelihood estimation of the depth field based on epipolar plane images. To face the potential accuracy losses associated to the ambiguity problem that arises in flat surface regions while preserving bandwidth in correspondence of the edges, we adopt a multi-resolution scheme. In practice, the depth map resolution is reduced in regions where maximizing the higher resolution functional is ill-conditioned. The main benefits of the proposed system are in a reduced computational complexity and a high accuracy of the estimated depth. Experimental results show that the proposed scheme represents a good tradeoff among accuracy, robustness, and discontinuities handling.

The need for effective and reliable surveillance techniques is getting nowadays more and more of primary importance, especially in the actual scenario in which safety and security have become a priority. While classical techniques rely on video-based surveillance systems, such as Close-Circuit television, many studies show that also the audio signal can be effectively used for these purposes. There are many characteristics that make the audio signal particularly suited for this task and, above all, the fact that the analysis of the audio signal can greatly improve thanks to the introduction of automatic classification. Recently, a large focus has been on the use of Deep Neural Networks for classifying audio data and, in this work, we aim to test their performance in the audio surveillance field. In this contribution we propose an algorithm for audio events detection in noisy environments based on the use of deep recurrent neural network. The achieved results show satisfactory and improved performances with respect to state-of-the-art techniques.

In this work, the problem of dynamic video scene creation obtained by combining information extracted from multiple video sequences is considered. The main novelty of the proposed approach relies on the use of aesthetic features for automatically aggregating the inputs from different cameras in a unique video. While prior methodologies have separately addressed the issues of aesthetic feature extraction from videos and video orchestration, in this work we exploit selected features of a scene for automatically selecting the shots being characterized by the best aesthetic score. In order to evaluate the effectiveness of the proposed method, a subjective experiment has been carried out with experts from the audiovisual field. The achieved results are encouraging and show that there is space for improving the performances.

To meet the food demand of the future, farmers are turning to the Internet of Things (IoT) for advanced analytics. In this case, data generated by sensor nodes and collected by farmers on the field provide a wealth of information about soil, seeds, crops, plant diseases, etc. Therefore, the use of high tech farming techniques and IoT technology offer insights on how to optimize and increase yield. However, one major challenge that should be addressed is the huge amount of data generated by the sensing devices, which make the control of sending useless data very important. To face this challenge, we present a Bayesian Inference Approach (BIA), which allows avoiding the transmission of high spatio-temporal correlated data. In this paper, BIA is based on the PEACH project, which aims to predict frost events in peach orchards by means of dense monitoring using low-power wireless mesh networking technology. Belief Propagation algorithm has been chosen for performing an approximate inference on our model in order to reconstruct the missing sensing data. According to different scenarios, BIA is evaluated based on the data collected from real sensors deployed on the peach orchard. The results show that our proposed approach reduces drastically the number of transmitted data and the energy consumption, while maintaining an acceptable level of data prediction accuracy.

Smart city services are enabled by a massive use of Internet of Things (IoT) technologies. The huge amount of sensors, and terminals with a great variety of typologies and applications, requires a secure way to manage them. Capillary networks can be seen as a short range extension of conventional access network in order to efficiently capture the IoT traffic, and are enablers for smart city services. They can include both IP and non-IP devices, and security can become an issue, especially when simple unidirectional communication devices are considered. The main goal of this paper is to analyze security aspects in IoT capillary networks including unidirectional and bidirectional IP or non-IP devices. We propose an algorithm for secure access for uni- and bi-directional devices. The security procedure is based on a secure key renewal (without any exchange in air), considering a local clock time and a time interval of key validity. Following previous work in 2014 by Giuliano et al., in this paper we assess the duration of the validity of the time window, and present extended simulation results in terms of (average) transmission time in a realistic scenario, i.e., including the presence of disturber(s), then providing indications for the setting of the duration of the key validity time window. Finally, we present the benchmark analysis in order to assess the effectiveness of our approach with respect to other existing standards, as well as the security analysis in terms of typical attacks.

This paper investigates a novel communication system architecture for traffic management and data control in railway scenarios. The proposed solution should be integrated in the existing European Railway Traffic Management System/European Train Control System, the most advanced and successful standard even outside the European countries. Our system integrates public land mobile networks that usually offer best-effort packet services, with a quality-of-service (QoS) guaranteed satellite network. It represents a cost-effective substitution for 4G and 5G networks, aiming to replace current well-known GSM-R standard. The coexistence of heterogeneous networks is guaranteed by the multi-path transmission control protocol (TCP) protocol that addresses specific “add and drop” subflow policies and priority handling logics, in order to realize efficient seamless handovers. Providing priority to TCP subflows corresponds to select QoS-guaranteed, and the best effort networks able to fulfill railway requirements. The logic of adding and dropping subflows exploits both a real-time check of the current status of the serving network and “a priori” network performance information. Experimental results have been carried out in both city and harsh environments, where the main performance metrics (i.e., average delay, jitter, and bit rate) have been assessed.

In this paper a multi-resolution depth field estimation algorithm for plenoptic cameras is presented. To face the potential accuracy losses originated from ambiguity problems arising in flat surface regions, still preserving bandwidth in correspondence of edges, a multi-resolution scheme is proposed. The achieved results show that the proposed local optimization method outperforms state of the art more complex global optimization based methods.

To provide advanced services to citizens, Smart City services are enabled by a massive use of Internet of Things (IoT) technologies. The envisaged huge amount of sensors, and terminals with a great variety of typologies and applications, do not allow having a unique way to manage them, requiring for example minimal packaging and presentation overhead for many of them. In general in a network providing Smart City services, both IP and non-IP devices are present. In this paper, we tackle the security issue for non-IP devices able to connect by a short-range with a mediator gateway, forming a capillary access network, which can be seen as a short range extension of conventional access network in order to efficiently capture the IoT traffic. In particular, we propose security algorithms both for uni- and bi-directional terminals, depending on the terminal capabilities. The security algorithms are based on a local key renewal (without any exchange in air), performed just considering the local clock time. Performance are obtained respect to the maximum number of terminals that can be managed by one mediator gateway and the maximum packet delay as a function of the number of terminals in the area.

Template protection is an issue of paramount importance in the design of biometric recognition systems. In this paper we present a biometric cryptosystem applied to iris biometrics, where template security is guaranteed by means of a framework inspired by the digital modulation paradigm. Specifically, the properties of modulation constellations and turbo codes with soft-decoding are exploited to design a system with high performance in terms of both verification rates and security, even while dealing with a biometrics characterized by a high intra-class variability such as the iris. The effectiveness of the proposed approach is evaluated by performing tests on the Interval subset of the CASIA-IrisV4 database.

Telecommunications are strategic for train control system operations. The European ERTMS/ETCS system for high speed trains control utilizes dedicated GSM-R mobile network(s). The costs for deployment and maintenance of GSM-R network are not sustainable for the extension of train control to low/medium traffic lines. This paper investigates on the possibility of replacing dedicated mobile radio infrastructures with the radio network integrating one (or more) existing PLMNs and satellite as backup. A methodology for performance assessment is presented and applied to the study of network availability and operational costs. Availability can be improved by allowing train to intelligently select the public network(s) time by time. Operational costs are dictated by the satellite component which may become essential in rural areas. Looking at the European scenario, a discussion on the extension of the Euroradio protocol to operate over multi-bearer mobile network is also presented.

In this paper we present a localization technique for tracking and positioning services in self-organizing hybrid networks, where both indoor and outdoor scenarios coexist. In the proposed framework, outdoor anchor nodes act as reference nodes for estimating the position of mobile nodes, which are moving in a neighboring indoor region. The mobile nodes share some location information for estimating their own position by using flooding communication scheme. Communications between mobile nodes are allowed by IEEE 802.16e technology. The outdoor reference nodes are equipped with Global Positioning Service network interface card. The cooperation among mobile and reference nodes, and the self-organizing network behavior guarantee fast positioning estimate. The accuracy of localization measurement - in terms of position and speed uncertainty - is assessed through the Dilution of Precision factor. Moreover, to minimize position and speed estimation errors of each node, an Extended Kalman Filtering technique is adopted. Simulation results demonstrate the effectiveness of the proposed approach.

Recent years have seen the rapid spread of biometric technologies for automatic people recognition. However, security and privacy issues still represent the main obstacles for the deployment of biometric-based authentication systems. In this paper, we propose an approach, which we refer to as BioConvolving, that is able to guarantee security and renewability to biometric templates. Specifically, we introduce a set of noninvertible transformations, which can be applied to any biometrics whose template can be represented by a set of sequences, in order to generate multiple transformed versions of the template. Once the transformation is performed, retrieving the original data from the transformed template is computationally as hard as random guessing. As a proof of concept, the proposed approach is applied to an on-line signature recognition system, where a hidden Markov model-based matching strategy is employed. The performance of a protected on-line signature recognition system employing the proposed BioConvolving approach is evaluated, both in terms of authentication rates and renewability capacity, using the MCYT signature database. The reported extensive set of experiments shows that protected and renewable biometric templates can be properly generated and used for recognition, at the expense of a slight degradation in authentication performance.

A markerless computer vision technique specifically designed to track natural elements on the human body surface is presented. The method implements the estimate of translation, rotation, and scaling by means of a maximum likelihood approach carried out in the Gauss-Laguerre transform domain. The approach is particularly suitable for human movement analysis in clinical contexts, where kinematics is at present performed by means of marker-based systems. Specific drawbacks of these latter systems, such as the burden of time for marker placement and the intrinsic intrusive nature, would be removed by the proposed method. Experimental results in terms of tracking performance are obtained by analyzing video sequences capturing the execution of the sit-to-stand task in two groups of young and elderly volunteers. The results are compared with clinical studies that used marker-based systems, and are particularly encouraging for a future extension of the approach to other motor tasks and to predict scores obtained from the physical performance batteries that are widely and regularly used by clinicians and physical therapists.

In this paper a location-based algorithm is proposed for managing soft mobile-controlled vertical handover between wireless systems. The case of a dual-mode terminal equipped with UMTS and IEEE 802.11 network interface cards is analysed in detail, and an novel approach for optimizing goodput and limit the so-called ping-pong effect is defined. The novel algorithm is based on a preliminary handover initiation phase triggered on the basis of mobile node location. Handover is then carried out by following a goodput estimation phase allowed by a transient of by casting during soft handovers. Experimental results provide an assessment on the use of location information to drive handover decisions.

In this paper a method for providing additional information for error concealment during the transmission of a video file over an error-prone channel is presented. The transmission of the additional information is performed without increasing the bandwidth occupation, by hiding this information into the video itself at the encoder side. The considered transmission system is the DVB-H system. The performance of the method has been investigated by simulations and experimental tests in order to evaluate the perceived quality of the processed video and the robustness of the data hiding method against the H.264/AVC coding.

This paper proposes a business model founded on Bayes decision test for video-call billing. This model is based on the end-to-end Quality of Service (QoS) obtained exploiting the tracing watermarking procedure recently introduced in literature. In particular, we employ the watermark embedded in each transmitted video-sequence to monitor the QoS of the communication link and then to adequately fare the user depending on the real received video-quality. We need to know only the MSE of the received watermark to estimate the end-to-end QoS of the communication link. Numerical results are conducted to characterize the effectiveness of the proposed method, using two different business scenarios, and confirm the validity of such approach. Therefore, the network operator is able to implement an adaptive billing strategy, depending on the effective received quality and maximizing the profit.

Tracing watermarking has been recently proposed as a technique to provide a blind measure of the quality of service of the communication link, focusing on multimedia communication scenarios. 3G communication scenarios, where each customer transmits both voice and MPEG-4 video sequences in real time, have been considered. Typical video-phone call consists of close shots of speakers, framing their face in the foreground. For such particular application, a novel colour space (YST) is adopted to minimise the perceptual distortions on face coding introduced by watermarking. In the YST space, the luminance component (Y) is the same as in conventional YUV space, whereas the vectors S and T lie within the chrominance (UV) plane. The S (skin) component is a linear combination of U and V obtained as the ‘average’ chrominance estimated from an assorted set human faces to achieve a reasonable generalisation. The third component, T, is defined as orthogonal to the YS plane. The results show the benefits obtained in digital watermarking by the new representation against the conventional approach. In fact, the sensitivity of the YST representation outperforms the conventional one in terms of objective (mean square error, peak signal-to-noise ratio) and subjective (video-quality metrics) indicators.

In this paper we propose a new method for natural video textures synthesis, using a three-dimensional (3-D) extended self-similar (ESS) fractal model. The autocorrelation functions (ACFs) of original texture increments are estimated, and a synthetic 3-D-ESS process whose increments have the same ACFs of the corresponding given ones is generated using a 3-D incremental Fourier synthesis algorithm. Experimental results for the analysis and synthesis of natural video textures are provided

The present paper is devoted to the introduction of a novel technique to select keypoints from digital images and build representative and distinctive descriptors. The algorithm performs a multiresolution image analysis in the Laguerre Gauss transformed domain and collects in a local descriptor the transformed coefficients at multiple scales, of the keypoint’s representative pattern. The rotation invariance of the Circular Harmonics and the multiscale approach make the system more robust than other descriptors to match patterns related by affine transformations. 1

In natural images, luminance changes occur both on object contours and on textures. Often, the latter are stronger than the former, thus standard edge detectors fail in isolating object contours from texture. To overcome this problem, we propose a multiresolution contour detector motivated by biological principles. At each scale, texture is suppressed by using a bipolar surround inhibition process. The binary contour map is obtained by a contour selection criterion that is more effective than the classical hysteresis thresholding. Robustness to noise is achieved by Bayesian gradient estimation.

The determination of 3D structure from images is one of the most active field in computer vision. This paper gives a contribution presenting a symmetric stereo matching algorithm, based on a bidirectional dynamic programming scanline optimization. Consistent matches are detected by inspecting the images scanlines with a complex exponential weighting window and then analyzing matching score map into a bidirectional optimization framework. Results are presented running the algorithm on stereo pair freely available on the Web, in order to numerically validate the performance.

Many studies have shown that color representation is very relevant in image processing and communications techniques. In this contribution, we propose a new color space, based on the "skin" component, in order to minimize the perceptual distortions introduced by digital watermarking. We have therefore defined a new color space, called YST, in which the brightness component is the same of YUV. Conversely, the S (skin) component has been obtained estimating the average values corresponding to a set of different colors of human faces. The criterion for this selection was to achieve a reasonable degree of generalization. The third component, T, is automatically identified by the Gram-Schmidt procedure in order to have an orthogonal color space. Analytical results are used to show the benefits obtained in digital watermarking by the new representation as well as in terms of peak signal-to-noise ratio and video quality metrics.

A novel video watermarking system operating in the three-dimensional discrete wavelet transform (3D DWT) is here presented. Specifically the video sequence is partitioned into spatio-temporal units and the single shots are projected onto the 3D DWT domain. This paper focuses on the definition of a perceptual mask, derived from the 3D wavelet coefficients, which allows to trade off between the mark robustness and its imperceptibility. The proposed mask takes into account the spatio-temporal frequency content, the variance, and the luminance of the 3D subbands where the mark is embedded. The embedding is additively performed in the 3D DWT domain by weighing the mark through the defined mask. Finally, the inverse 3D DWT (IDWT) is performed thus obtaining the marked video shot. The imperceptibility of the proposed technique along with its robustness has been observed through experimental results.

In this paper, we present a no-reference video quality metric that blindly estimates the quality of a video. The proposed approach makes use of a data hiding technique to embed a fragile mark into perceptually important areas of the video frame. To estimate the importance of an area, we take into account three perceptual features that are known to attract visual attention: motion, contrast, and color. At the receiver, the mark is extracted from the perceptually important areas of the decoded video. Then, a quality measure of the video is obtained by computing the degradation of the extracted mark. Simulation results indicate that the proposed video quality metric outperforms standard peak signal to noise ratio (PSNR) in estimating the perceived quality of a video. Additionally, results from a subjective experiment show that the metric output values increase monotonically with the mean annoyance scores gathered from the human observers.

In this paper, a new methodology for embedding data in a video sequence is presented. To guarantee the imperceptibility of the embedded data, we propose a method to select frame regions that are considered perceptually non relevant. For each frame a salience analysis is performed based on features that are relevant to the human vision system. In particular, the local contrast, the color and the areas of motion have been considered. By weighting all these feature at once, an importance map is built to drive the sequent embedding procedure. Subjective experiment results show that the artifacts caused by this localized embedding procedure are considered less annoying than if the embedding is performed on the whole frame

Noncoherent techniques can be adopted for pseudonoise code synchronization in the presence of random phase. Differentially coherent methods conversely operate with linear phase shifts, such as when a systematic frequency error occurs. This letter presents a method exploiting local phase coherence (inspired by the Pisarenko estimation), allowing global frequency estimation in the presence of random phase noise. Its accuracy is numerically compared with noncoherent and differential techniques for the initial stage of wideband code-division multiple-access code synchronization.

Large collections of image documents are today accessible via communication networks. Cost effective retrieval of the wanted images needs convenient browsing tools. In this contribution a technique for image selection on the basis of key details is illustrated. The technique is useful for instance for assisted queries to digital artwork collections, tele-sensing images, aerial photographs, astronomic databases, etc. The basic feature of the method is the capability of detecting and locating anyway rotated details with reasonable computational costs. In this work a criterion for proper choice of detail reference points is illustrated.

In this letter, we propose a multiplicative watermarking method operating in the ridgelet domain. We employ the directional sensitivity and the anisotropy of the ridgelet transform (RT) in order to obtain a sparse image representation, where the most significant coefficients represent the most energetic direction of an image with straight edges. Therefore, given a natural image, the associated edge image is obtained by means of a filter bank designed using the circular harmonic functions, then the edge image is partitioned into small blocks in order to deal with straight edges. Finally, the RT is performed for each block, the most relevant coefficients are selected, and eventually the watermark is embedded. Robustness and transparency are proven by experimental results.

In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space.

Usual approaches to localization, i.e., joint estimation of position, orientation and scale of a bidimensional pattern employ suboptimum techniques based on invariant signatures, which allow for position estimation independent of scale and orientation. In this paper a Maximum Likelihood method for pattern localization working in the Gauss-Laguerre Transform (GLT) domain is presented. The GLT is based on an orthogonal family of Circular Harmonic Functions with specific radial profiles, which permits optimum joint estimation of position and scale/rotation parameters looking at the maxima of a "Gauss-Laguerre Likelihood Map." The Fisher information matrix for any given pattern is given and the theoretical asymptotic accuracy of the parameter estimates is calculated through the Cramer Rao Lower Bound. Application of the ML estimation method is discussed and an example is provided.

Multimedia data hiding by digital watermarking is usually employed for copyright protection purposes. In this contribution, a new application of watermarking is presented. Specifically, watermarking is here employed as a technique for testing the quality of service in multimedia mobile communications. A fragile known watermark is embedded in a MPEG-like host data video transport stream using a spread-spectrum technique to avoid visual interference. Like a tracing signal, a (known) tracing watermark tracks the (unknown) information stream that follows the same communication link. The detection of the tracing watermark allows dynamically evaluating the effective quality of the provided video services, depending on the whole physical layer (including the employed image co/decoder). The performed method is based on the mean-square-error between estimated and actual watermarks. The devised technique has been usefully applied to typical scenarios of mobile wireless multimedia communication systems, in presence of multipath channel and interfering users.

In this paper a model based texture classification procedure robust to sample rotation is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a classical moment invariants based technique to classify the ACF and the resulting classification procedure is thus inherently rotation invariant. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while reducing the size of the feature space and the computational burden.

A comparison between an objective quality measure and the perceived mean annoyance values of watermarked videos is presented. A psychophysical experiment has been performed to measure the detection threshold and mean annoyance values of several watermarked videos, using two different marks. The results of this experiment were then compared with an objective quality measure, obtained through a tracing watermarking system. An estimation of the detection threshold of the watermarked videos was found.

Third generation cellular systems (IMT2000/UMTS) are expected to provide a large number of innovative services with negotiated end-to-end quality of service (QoS). An object-based system for quality of service assessment is presented to evaluate blindly the quality of a multimedia communication link by means of an unconventional use of semi-fragile digital watermarking. Specifically, a known watermark is hidden into each single object of a MPEG-4 video using a spread spectrum technique. In this fashion, the object and the embedded watermark follow the same communication link. In fact, the degradations endured by the (known) watermark allows the degradations suffered, at the receiver's side, by the original (unknown) object to be inferred. The quality of service evaluation is based on the calculation of the mean square error between the estimated and the actual watermarks. Experiments show the effectiveness of the proposed method.

In this contribution two solutions for the management of cellular intranet, based on the mobile IP and cellular IP protocols integration are investigated. The first solution adopts a centralized architecture built over the gateway and the home agent. It is most suited for security needs and client/server traffic. The second solution utilizes the mobile IP with routing optimization for macro mobility management. It offers optimized routing, speeds-up the handoff procedures, supports real time traffic and is therefore oriented toward the quality of service.

Video databases are very demanding systems as far as mass storage requirements and computational resources necessary to perform common database operations, such as browsing and retrieval, are required. These operations can be simplified both in terms of the computational complexity and of processing time by performing them on an ensemble of frames, called key frames, representative of the content units (shots) in which a video can be segmented. An adaptive key frames extraction method based on a wavelet based multiresolution analysis in a perceptually uniform color space is presented. Experimental results that show the effectiveness of the proposed technique to select key frames summarizing the video's content, are finally provided.

The problem of image texture stochastic modeling by means of the 2D Wold decomposition is addressed. Specifically, the 2D Wold decomposition additively separates a texture into a deterministic and an indeterministic component. Since these components are shown to be statistically orthogonal, for textures having a distribution that significantly deviates from Gaussianity, it is possible, in principle, to establish some link between the two components. The aim of this contribution is to show how the indeterministic component can be predicted from the deterministic one by means of suitable nonlinear schemes. The hierarchical structure of the 2D Wold decomposition is thus generalized to deal with the non-Gaussian behavior of most of the natural textures of interest.

In natural scenes, still images as well as sequences, backgrounds, and objects' surfaces usually have a textural structure. Therefore, in order to efficiently code images it is crucial to investigate the texture compression problem. In this paper, a perceptually lossless, synthesis-by-analysis texture coding method is presented. The proposed approach is model based; the parameters of the model consist of a binary excitation signal and the parsimonious representation of the reconstruction filter. The estimated parameters, which allow to one synthesize, at the decoder site, a texture that is perceptually indistinguishable from the original one, are then compressed using a lossless strategy, which is based on a fast binary wavelet transformation specifically tailored to binary images. The proposed method leads to very good perceptual results superior to those of existing techniques.

An unsupervised color texture synthesis-by analysis method is described. The texture is reproduced to appear perceptually similar to a given prototype by copying its statistical properties up to the second order. The synthesized texture is obtained at the output of a single-input three-output nonlinear system driven by a realization of a white Gaussian random field. Significant complexity reduction is gained by exploiting the rank deficiency of the cross power spectral density matrix of the color texture samples.

In this paper a video coder based on a spatiotemporal multiresolution pyramid generated by a 3D separable wavelet transform (WT) is presented. The aim of the proposed coding scheme is to provide an effective scalability of the user profile by organizing the full resolution encoded bit stream into substreams, one for each temporal layer of the pyramid, which are then partitioned into substreams at increasing spatial resolution. This allows to simultaneously distribute, at different time and space resolutions, the same video to multiple users accessing the videoconference services through channels of different capacity, without multiple encoding.

An innovative unsupervised color texture, model based, synthesis method is presented. It aims to reproduce a color texture perceptually similar to a given prototype. To achieve this task, we reconstruct the texture on a stochastic base, matching the prototype's statistics up to the second order to the synthesized texture ones. The model employed allows to replace the multiple input multiple output filters, which have to be used in the general case, with more simple structures, leading to a dramatical computational complexity reduction without affecting the results quality.

A twin stage texture synthesis-by-analysis method is presented. It aims to approximate first- and second-order distributions of the texture, accordingly to the Julesz conjecture. In the first stage, the binary textural behavior of a given prototype is represented by means of a hard-limited Gaussian process. In the second stage, the texture is synthesized by passing the binary hard-limited Gaussian process through a linear filter followed by a zero memory histogram equalizer.

This correspondence illustrates the optimal detector and slope estimator of straight patterns. In particular, it is recognized that the output of the likelihood processor, constituted by the Radon transform and a whitening filter, provides a sufficient statistic for both problems of signal detection as well as orientation and offset estimation.

A new technique for unsupervised texture synthesis by analysis is presented. It employs a stochastic model of textured fields based on white Gaussian fields passed through a nonlinear system composed of two linear filters connected by a hard limiter. The identification of the texture models is performed using a Bussgang blind deconvolution algorithm and exploiting of the Van Vleck rule for determining the filters parameters. After a theoretical discussion of the method typical examples are provided.

The normalized autocorrelation function of a Gaussian process may be recovered from second order moments of their polarity, through the arcsin law. By analogy, it is possible to calculate the normalized autocorrelation function of a circularly complex Gaussian process from the knowledge of moments of its instantaneous phase. In the present paper, two estimators of the normalized autocorrelation function based on the phase only are presented. Their theoretical accuracy is evaluated and compared to the accuracy of the direct estimate.>

The Marcum's Q-function is extended to complex arguments. In particular, a set of modified generalized Q-functions for imaginary values of one or both arguments are defined and their properties are investigated. An extended probabilistic interpretation for the modified Q-functions is given appealing to a proposed generalization of the concept of random variable. Complex pseudo-random variables, pseudo-events, and pseudo-probability measures are defined, which formally obey the usual rules of composition of probability measures of independent events. The modified Q-functions are then used to derive the Rayleigh multivariate multidimensional distribution function and the error probability for partially coherent detection of equicorrelated signals.>

The performance and the sensitivity of the neural correlator based on integral pulse frequency modulation (IPFM) are analyzed. The performance of the neural correlator is evaluated and shown to match many psychophysical test results. The behavior of the neural coder is expressed in terms of a stochastic nonlinear law. An analytical optimality condition for the mean-spike firing rate is derived. Numerical comparison between optimal and neurophysiological values shows that natural systems meet the optimality condition.>

A novel, suboptimal, nonlinear recursive algorithm for the estimation of filtered Markov chains from noisy observations is presented. The expression of the estimator is formally identical to the Kalman filter solution when the error covariance matrix is replaced by the conditional covariance matrix. Starting from the Martingale-difference (MD) representation theorem, a recursive expression for the conditional covariance matrix that can be reasonably approximated by means of the Markov property is derived. Thus the obtained estimator is suboptimal in the sense that the conditional covariance matrix is evaluated only approximately.>