In the last few years, the Cultural Heritage field has posed its attention over image processing techniques, in particular for the diagnostic of art works. Many different images of the same painting are taken, in different parts of the light spectrum; these different images have to be fused together, to get an augmented image showing much more details and information which are only visible in some sub-parts of the spectrum singularly. In this paper, we pose the attention over the compulsory step accomplished just before the "fusion" of such images, i.e. the registration step; in particular, we present an automatic registration technique, based on the computation of mutual information. By means of the registration, it is possible to exactly align the different images, which is the preliminary step to obtain a useful and correct augmented image. These techniques can be also applied to other areas, and in particular to remote sensing images.

In art diagnostics it is often needed to compare and integrate different sets of information, coming from different sources, and stored in different images. In order to successfully integrate these data, images corresponding to the same areas need to be registered, that is a geometrical transformation that aligns points in one picture with corresponding points in another picture needs to be found and applied. In this paper we present an automatic registration technique for multispectral images, based on the computation of mutual information. We applied this method to obtain RGB images from multispectral data, and derive a (undersampled) spectral signature per pixel of the painting.

Abstract Advances in electronic imaging over recent years have encouraged the development of new tools for cultural heritage applications. In this paper a software application called ArtShop is described, containing some tools for artwork image restoration, developed during several years of research at the Image and Communications Laboratory of the University of Florence. Copyright © 2003 John Wiley & Sons, Ltd.

We propose near-lossless digital watermarking for copyright protection of remote sensing images. In particular, we show that, by forcing a maximum absolute difference between the original and watermarked scene, the near-lossless paradigm makes it possible to decrease the effect of watermarking on remote sensing applications to be carried out on the images. As an example, the effect of near-lossless watermarking on image classification is analyzed.

The peculiarities of digital map images are exploited to develop a watermarking algorithm which is robust against geometric distortions. Robustness against geometric attacks is achieved through text-based image normalization. First, text is extracted from the to-be-marked map, then text orientation and size are exploited to normalize the image geometry prior to watermark insertion. Watermarking is performed by means of any of the existing algorithms ensuring good robustness against image processing tools. At the decoder side, text is extracted again from the map and used to normalize image geometry. Owing to the robustness of text features with respect to common image manipulations, and to the likely spreading of text all across the digital map, the proposed system exhibits an excellent robustness, as is witnessed by the experimental results reported.

Progress in electronic imaging over the last years has encouraged the development of new tools for cultural heritage applications. In particular, the areas of painting preservation and restoration have attracted the interest of researchers working in the field of image processing. A virtual tool for painting restoration, which simulates the actual restoration carried out in the restoration laboratory, is presented; in particular, the proposed techniques allow the virtual recovery and filling of the areas of a painting which are damaged by lacunas.

A new multichannel filter, the vector Huber filter (VHF) is proposed which, by relying on multivariate estimation theory permits to simultaneously remove impulsive and Gaussian noise from color images, while preserving edges and other fine image details. The new filter is obtained by extending the classical gray-scale Huber filter to the multichannel case. By relying on robust estimation theory, the rationale behind the development of the new filter is discussed. An approximated version of the vector Huber filter (the A-VHF filter), is also introduced to reduce the computational burden and making filtering practical. The validity of the A-VHF filter is confirmed by the preliminary experimental results shown in the paper.

For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean norm is presented which is applied to vector median filtering.

This paper describes a computational method for estimating the body reflectance function of color surfaces. The experimental apparatus consists of a vision system having seven spectral channels whose wavelength sensitivities cover the visible spectrum. The estimation of the spectral-reflectance function is based on finite-dimensional linear models. In order to use digitized imaged data to evaluate the surface reflectance, a procedure for bypassing the problem of unknown illuminant spectral-power distribution was devised. For the simple objects utilized (color checker charts), it is found that an accuracy greater than 97% can be achieved.

In the last few years, digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data against unauthorized uses. In the field of image watermarking, research has been mainly focused on grey-scale image watermarking, whereas the extension to the color case is usually accomplished by marking the image luminance, or by processing each color channel separately. In this paper, a DCT domain technique expressly devised for watermarking of color images is presented, which exploits the characteristics of the human visual system and the correlation between the RGB image channels. Experimental results are presented to demonstrate the validity of the new approach with respect to algorithms operating on image luminance only.

Calibration of the output device used for the reproduction of digital colour images (a color CRT in most cases) can either be achieved through conventional techniques involving mathematical modeling of the CRT, or through a novel neural-network-based scheme, introduced in this work. A Java-based system for CRT remote calibration is also presented, which allows the user to get rid of the computational burden necessary to train the neural network, or to estimate the parameters of the CRT model by relying on a set of measurements of the colours displayed by the CRT. Measurements can be avoided as well, by storing calibration data relative to a wide variety of CRTs and by using them to calibrate monitors with similar characteristics.

The interest in image watermarking techniques has rapidly grown during the years. Two requirements needed to be satisfied to use watermarking techniques for copyright protection are: unperceivability and robustness against image processing algorithms and forgery attacks. In particular, it is widely accepted that the exploitation of the characteristics of the human visual system should greatly help in satisfying both these requirements. Some solutions to the problem of building some perceptual masks for better hiding watermarks embedded in the full-frame DCT domain are presented. The results support the validity of the approach.

The interest in algorithms aimed at the estimation of object motion and aimed at simultaneous motion based image segmentation is rapidly increasing. Possible applications of such techniques can be found in many different fields, ranging from machine vision to video coding. In this paper an algorithm, based on least squares (LS), for the estimation of optical flow, and for the motion based segmentation of image sequences is described.

Digital watermarking has been proposed as a viable solution to the need of copyright protection and authentication of multimedia data in a networked environment, since it makes it possible to identify the author, owner, distributor or authorized consumer of a document. In this paper a new watermarking technique to add a code to digital images is presented; the method operates in the frequency domain embedding a pseudo-random sequence of real numbers in a selected set of DCT coefficients. Watermark casting is performed by exploiting the masking characteristics of the human visual system, to ensure watermark invisibility. The embedded sequence is extracted without resorting to the original image, so that the proposed technique represents a major improvement to methods relying on the comparison between the watermarked and original images. Experimental results demonstrate that the watermark is robust to most of the signal processing techniques and geometric distortions.

In the field of video coding the issues of backward prediction and standards conversion have focused an increasing attention towards techniques for an effective estimation of the true interframe motion. The problem of restoration of motion vector-fields computed by means of a standard block matching algorithm is addressed. The restoration must be carried out carefully by exploiting both the spatial correlation of the vector-field, and the significance of the obtained vectors as measures of the reliability of the previous estimation step. A novel approach matching both the above requirements is presented. Based on the theory of vector-median filters an adaptive scheme is developed and results are discussed.

The unsupervised detection of an unknown number of straight lines in digital imagery is addressed. Based on possibilistic clustering an algorithm is proposed which does not require any assumption about the number of straight lines present in the edge map. Three major modifications are introduced with respect to existing clustering-based algorithms: the use of possibilistic clustering; a more sophisticated analysis of the clusters, including the possibility of rejecting non linear clusters; a bottom up strategy to evaluate how many straight lines the image contains. The effectiveness of the proposed scheme is proved by validating it against real world imagery.

The use of traffic monitoring techniques based on image processing algorithms for supervising urban vehicle flows could be very useful. Classical inductive loops can only compute traffic density on a single lane but are unable to estimate, for example, the behaviour of the vehicle flow at a crossroad. In this paper a system that estimates the turning rates at an urban crossroad by processing the sequence of images taken by a videocamera is presented. Block matching motion estimation, segmentation, and moving object tracking techniques are used. The good results obtained are presented and their relation to the camera position is discussed.

In this comment, we report a difficulty with the-application of the possibilistic approach to fuzzy clustering (PCM) proposed by Keller and Krishnapuram (1993). In applying this algorithm we found that it has the undesirable tendency to produce coincidental clusters. Results illustrating this tendency are reported and a possible explanation for the PCM behavior is suggested.

The progress of digital electroencephalography gave rise to the problem of EEG data recording. In the paper a DPCM scheme for EEG signal compression is discussed. In particular the performance of a class of predictors based on recurrent neural networks is presented. The training strategy is accurately described and the results of a comparison with some other classical linear and static neural predictors are given. The proposed recurrent neural predictor demonstrates to be competitive with the others in offering good performance at a very low computational cost.

For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean norm is presented which is applied to vector median filtering.

Modern manufacturing systems call for full-rate automated inspection of produced samples. A vision-based intelligent perception system (IPS) is presented making automated inspection of chicken meat feasible. The IPS analyzes RGB images framing the chickens after the slaughtering and plucking process and detects defects such as burns, hematomas and blisters, along with other relevant features. The vision module, which is the core of the system, operates by first extracting the chicken body from the background, then it segments the body into its anatomic subparts. Defective areas are identified by means of morphological reconstruction. Finally, defects are classified by comparing their features against the defect description contained in a reference database.

A system for coding video sequences at a low rate is presented. The system is very effective in coding sequences where motion is restricted to a not large area of each frame. Nevertheless knowledge based techniques are not used for not reducing the generalization power of the coding method. Techniques based on segmentation algorithms are used to encode both the displacement vector field and the prediction error image. Such techniques permit one to effectively distribute coding information in those areas of the image where it is required. Great care is given to a good estimation of motion of the scene objects: this is to reduce the amount of information to be coded in the prediction error image.>

The vector median filter has good filtering capabilities; nevertheless, its huge computational complexity significantly limits its practical usability. A vector median filter based on a fast approximation of the Euclidean norm is presented. The proposed algorithm couples computational and filtering effectiveness, and it is well suited for hardware implementation. Theoretical and experimental results regarding both approximation error and speed improvement prove the validity of the proposed algorithm.>

The architecture of an intelligent perception system (IPS) is analyzed, and the main problems related to its practical implementation are discussed. Particular attention has been devoted to the data fusion function, which initiates the process and gives information on the actual situation with the associated uncertainties; the hypotheses function, which takes into consideration the uncertainties in the correlated data and creates the best explanations of the fused data; the option function, which creates various response alternatives for each hypothesis simulating the resulting effects; and the response function, which realizes the planned actions so producing new stimuli and initializing other processing cycles. The architecture of the IPS is presented.>

Scene analysis with processing of multiple frames is considered for automatic recognition and tracking of moving objects. The importance of taking into account information about the objects in the design of flexible systems is emphasized. This goal is reached through an object-oriented approach to problem-solving. It is shown that abstract data type and inheritance relationship definitions over real-world entities can be efficiently used for object classification purposes. This has been verified in a traffic monitoring application. An efficient algorithmic solution that satisfies the real-time constraints of the problem is proposed.>

Some special digital operators and recognition techniques are presented for digital image processing. Two complete digital systems are described. The first fast and relatively simple system uses non-linear smoothing and adaptive thresholding as a pre-processing and inertial invariant components to recognize moving objects. The second more complex system uses non-linear smoothing, edge detection and noise spike reduction as a pre-processing and FFT of the boundary distances from the centroid to recognize and track moving objects. Experiments of recognition and tracking of mechanical objects are reported.

In this paper a complete communication system simulation package is described, which includes digital as well as analog operations to be performed on the signal to be transmitted. The package simulates the transmitter, the communication channel, and the receiver. The digital transmission simulation considers data communication systems with digital filtering, source and channel coding performing the operations of transmitter coding and receiver decoding. The two coding operations can be realized both separately and independently and in a strictly connected or "integrated" form. The analog transmission simulation includes the modulator, the transmitter pulse shaping filter, the communication channel, the receiving filter, and the demodulator. This section is very general, so that it allows the simulation of both analog and digital transmissions. Simulation results are presented regarding some combinations of the following techniques: predictive source coding, block codes able to correct random and burst errors, and modulation techniques such as AM,M-level PSK, FSK, and MSK. In particular, the paper presents the results of the analyses of an integrated source-channel coding applied to digital transmissions and of a system transmitting both voice and data for a VHF communication link between ground and aircraft for air traffic control applications.

An improved structure for an envelope detection multifrequency receiver based on digital nonrecursive quadrature filters is proposed. The receiver utilizes a property of digital nonrecursive quadrature filters to reduce the required computational complexity by a nominal factor of 2 with a negligible hardware increase. The application to the tone detector implementation for two CCITT standard signaling systems is considered, showing the low computational and structural complexity required by the proposed technique. The required multiplication rate is reduced by more than twice with respect to other conventional approaches.

A new 3×3 edge operator is presented, based on a suitable classification of binary configurations and corresponding decision table. It is shown how, setting the 8 pixels around the central one in a 3×3 block to binary level through comparison-decisions, the resulting 256 configurations are classified in two groups: those corresponding to estimate the central pixel as part of an edge or boundary and the other ones. The fast implementation of the operator through the direct comparison with the reference table for the 256 configurations is described and experimental results obtained by using a microprocessor system are presented.

In this paper we present a class of burst-error-correcting binoid codes derived from Samoylenko's codes. These codes, at high code rate, seem to be very useful for a not-too-noisy transmission channel when the encoding-decoding operations are performed by means of a general purpose computer.