Electronic Imaging

We propose a supervised approach to detect falls in home environment using an optimised descriptor adapted to real-time tasks. We introduce a realistic dataset of 191 videos, a new metric allowing to evaluate fall detection performance in a video stream, and an automatically optimised set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named ST HF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms and moments of order 0, 1 and 2). We study the combinations of usual transformations of the features (Fourier Transform, Wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using Support Vector Machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol which enables performance to be improved by updating the training in the current location with normal activities records.

In this paper we propose a solution for view synthesis of scenes presenting highly non-Lambertian objects. While Image- Based Rendering methods can easily render diffuse materials given only their depth, non-Lambertian objects present non-linear displacements of their features, characterized by curved lines in epipolar plane images. Hence, we propose to replace the depth maps used for rendering new viewpoints by a more complex “non- Lambertian map” describing the light field?s behavior. In a 4D light field, diffuse features are linearly displaced following their disparity, but non-Lambertian feature can follow any trajectory and need to be approximated by non-Lambertian maps. We compute those maps from nine input images using Bezier or polynomial interpolation. After the map computation, a classical Image- Based Rendering method is applied to warp the input images to novel viewpoints.

In this study, we investigate a VR simulator of a forestry crane used for loading logs onto a truck, mainly looking at Quality of Experience (QoE) aspects that may be relevant for task completion, but also whether there are any discomfort related symptoms experienced during task execution. A QoE test has been designed to capture both the general subjective experience of using the simulator and to study task performance. Moreover, a specific focus has been to study the effects of latency on the subjective experience, with regards to delays in the crane control interface. A formal subjective study has been performed where we have added controlled delays to the hand controller (joystick) signals. The added delays ranged from 0 ms to 800 ms. We found no significant effects of delays on the task performance on any scales up to 200 ms. A significant negative effect was found for 800 ms added delay. The Symptoms reported in the Simulator Sickness Questionnaire (SSQ) was significantly higher for all the symptom groups, but a majority of the participants reported only slight symptoms. Two out of thirty test persons stopped the test before finishing due to their symptoms.

Digital watermarking is the enabling technology to prove ownership on copyrighted material, detect originators of illegally made copies, monitor the usage of the copyrighted multimedia data and analyze the spread spectrum of the data over networks and servers. Embedding of unique customer identification as a watermark into data is called fingerprinting to identify illegal copies of documents. Basically, watermarks embedded into multimedia data for enforcing copyrights must uniquely identify the data and must be difficult to remove, even after various media transformation processes. Digital fingerprinting raises the additional problem that we produce different copies for each customer. Attackers can compare several fingerprinted copies to find and destroy the embedded identification string by altering the data in those places where a difference was detected. In our paper we present a technology for combining a collusion-secure fingerprinting scheme based on finite geometries and a watermarking mechanism with special marking points for digital images. The only marking positions the pirates can not detect are those positions which contain the same letter in all the compared documents, called intersection of different fingerprints. The proposed technology for a maximal number d of pirates, puts enough information in the intersection of up to d fingerprints to uniquely identify all the pirates.

Very few image processing applications have dealt with x-ray luggage scenes in the past. Concealed threats in general, and low-density items in particular, pose a major challenge to airport screeners. A simple enhancement method for data decluttering is introduced. Initially, the method is applied using manually selected thresholds to progressively generate decluttered slices. Further automation of the algorithm, using a novel metric based on the Radon transform, is conducted to determine the optimum number and values of thresholds and to generate a single optimum slice for screener interpretation. A comparison of the newly developed metric to other known metrics demonstrates the merits of the new approach. On-site quantitative and qualitative evaluations of the various decluttered images by airport screeners further establishes that the single slice from the image hashing algorithm outperforms traditional enhancement techniques with a noted increase of 58% in lowdensity threat detection rates.

In this paper we address the issue of producing automatic video abstracts in the video indexing context of animated movies. For a quick browse of the movie's visual content, we create first a storyboard-like summary, which follows the movie events by providing one key frame for each particular scene. To capture the shot visual activity, we use histograms of cumulative inter-frame distances and key frames are adapted to histogram mode distribution. Secondly, for a "sneak peak" of the movie's exciting action content, we propose a trailer-like video skim, which aims at providing only the most interesting parts of the movie. Our method is based on a relatively standard approach, thus highlighting action through the analysis of the movie rhythm and visual activity information.

Unmanned Aerial Vehicles (UAVs) gain popularity in a wide range of civilian and military applications. Such emerging interest is pushing the development of effective collision avoidance systems which are especially crucial in a crowded airspace setting. Because of cost and weight limitations associated with UAVs' payload, the optical sensors, simply digital cameras, are widely used for collision avoidance systems in UAVs. This requires moving object detection and tracking algorithms from a video, which can be run on board efficiently. In this paper, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify moving object candidates in the background subtracted image through deep learning classifier trained on manually labeled datasets. For each moving object candidates, we find spatio-temporal traits through optical flow matching and then prune them based on their motion patterns compared with the background. Kalman filter is applied on pruned moving objects to improve temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results demonstrate that our algorithm can effectively detect and track small UAVs with limited computing resources.

In the unsharp masking approach for image enhancement, a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version. The method is simple, but it suffers from two serious drawbacks. First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas. Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images. In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges. On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification. A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters. This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters. Computer simulations have verified the superior results obtained using these filters. In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods. © 1996 SPIE and IS&T.

This book covers the classical methods in image processing and introduces many recent applications. The described applications span a broad range of fields, including medical and dental imaging, multimedia search and retrieval, and tracking and security.

As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possible relationships between student clickstream behaviors and their course performance. We employ higher-order network and structural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evaluation. Finally, we discuss the limitation and extension of this work.

INVESTIGACIÓN Normas ISO para la gestión de los documentos electrónicos: buenas prácticas para la gestión documental en las empresas Resumen: Las normas ISO pueden ayudar a las organizaciones a implementar buenas prácticas en un sistema de gestión de documentos electrónicos. En la última década se han publicado diferentes normas que desde distintas perspectivas proponen requisitos que las organizaciones deben observar para la buena gestión de los documentos. En esta ponencia se analizan estas normas, y qué temas tratan para ayudar a los gestores y directivos de las empresas a asegurar un buen sistema de gestión. Se indican también, en las conclusiones, las carencias que no son bien desarrolladas en las normas ISO. Palabras Clave: <Gestión de documentos electrónicos><Normas ISO><Normas AENOR><Sistema de Gestión de Documentos; Digitalización> ISO standards for the management of electronic records : dusky practices for document management in enterprises Abstract ISO standards can help to organizations implement best practices in a system of the electronic records management. During the last decade have been published different standards from various perspectives proposed requirements that organizations must observe for the proper management of records. In this paper we discuss these standards and what issues trying to help managers and business managers to ensure good management system. Deficiencies that are not well developed in the ISO standards are also indicated in the conclusions.

Before going into the details of various image synthesis algorithms, it is worth considering their general aspects, and establishing a basis for their comparison in terms of eciency, ease of realization, image quality etc., because it is not possible to understand the specic steps, and evaluate the merits or drawbacks of dierent approaches without keeping in mind the general objectives. This chapter is devoted to the examination of algorithms in general, what has been called algorithmics after the excellent book of D.

Palette images are widely used on the World Wide Web (WWW) and in game-cartridge applications. Many images used on the WWW are stored and transmitted after they are compressed losslessly with the standard graphics interchange format (GIF), or portable network graphics (PNG). Well-known 2-D compression schemes, such as JPEG-LS and JPEG-2000, fail to yield better compression than GIF or PNG due to the fact that the pixel values represent indices that point to color values in a look-up table. To improve the compression performance of JPEG-LS and JPEG-2000 techniques, several researchers have proposed various reindexing algorithms. We investigate various compression techniques for color palette images. We propose a new technique comprised of a traveling salesman problem (TSP)-based reindexing scheme, Burrows-Wheeler transformation, and inversion ranks. We show that the proposed technique yields better compression gain on average than all the other 1-D compressors and the reindexing schemes that utilize JPEG-LS or JPEG-2000.

Halftoned separations of individual colorants, typically cyan, magenta, yellow, and black, are overlaid on a print substrate in typical color printing systems. Displacements between these separations, commonly referred to as "interseparation misregistration", can cause objectionable color shifts in the prints. We study this misregistration-induced color shift for periodic clustered-dot halftones using a spatiospectral model for the printed output that combines the Neugebauer model with a periodic lattice representation for the individual halftones. Using Fourier analysis in the framework of this model, we obtain an analytic characterization for the conditions for misregistration invariance in terms of colorant spectra, periodicity of the individual separation halftones, dot shapes, and misregistration displacements. We further exploit the framework in a hybrid analytical-numerical simulation that allows us to obtain quantitative estimates of the color shifts due to misregistration, thereby providing a characterization for these shifts as a function of the optical dot gain, halftone periodicities, spot shapes, and interseparation misregistration amounts. We present simulation results that demonstrate the impact of each of these parameters on the color shift and demonstrate qualitative agreement between our approximation and experimental data.

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated methods have become increasingly popular in creating fake images. In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. We extract co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Experimental results on two diverse and challenging GAN datasets comprising more than 56,000 images based on unpaired image-to-image translations (cycleGAN [1]) and facial attributes/expressions (StarGAN [2]) show that our approach is promising and achieves more than 99% classification accuracy in both datasets. Further, our approach also generalizes well and achieves good results when trained on one dataset and tested on the other.

Spectral characterization involves building a model that relates the device dependent representation to the reflectance function of the printed color, usually represented with a high number of reflectance samples at different wavelengths. Look-up table-based approaches, conventionally employed for colorimetric device characterization cannot be easily scaled to multispectral representations, but methods for the analytical description of devices are required. The article describes an innovative analytical printer model based on the Yule-Nielsen Spectral Neugebauer equation and formulated with a large number of degrees of freedom in order to account for dot-gain, ink interactions, and printer driver operations. To estimate our model's parameters we use genetic algorithms. No assumption is made concerning the sequence of inks during printing, and the printers are treated as RGB devices (the printer-driver operations are included in the model). We have tested our characterization method, which requires only about 130 measurements to train the learning algorithm, on four different inkjet printers, using different kinds of paper and drivers. The test set used for model evaluation was composed of 777 samples, uniformly distributed over the RGB color space.

The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital camera: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipelines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accuracy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Color equalization algorithms exhibit a variety of behaviors described in two differing types of models: Gray World and White Patch. These two models are considered alternatives to each other in methods of color correction. They are the basis for two human visual adaptation mechanisms: Lightness Constancy and Color Constancy. The Gray World approach is typical of the Lightness Constancy adaptation because it centers the histogram dynamic, working the same way as the exposure control on a camera. Alternatively, the White Patch approach is typical of the Color Constancy adaptation, searching for the lightest patch to use as a white reference similar to how the human visual system does. The Retinex algorithm basically belongs to the White Patch family due to its reset mechanism. Searching for a way to merge these two approaches, we have developed a new chromatic correction algorithm, called Automatic Color Equalization (ACE), which is able to perform Color Constancy even if based on Gray World approach. It maintains the main Retinex idea that the color sensation derives from the comparison of the spectral lightness values across the image. We tested different performance measures on ACE, Retinex and other equalization algorithms. The results of this comparison are presented.

A simple power-logarithm histogram modification operator is proposed to enhance digital image contrast. First, a logarithm operator reduces the effect of spikes and transforms the image histogram into a smoothed one that approximates a uniform histogram while retaining the relative size ordering of the original bins. Then, a power operator transforms this smoothed histogram into one that approximates the original input histogram. Contrast enhancement is then achieved by using the cumulative distribution function of the resulting histogram in a standard lookup table-based histogram equalization procedure. The method is computationally efficient, independent of image content, and (unlike most existing contrast enhancement algorithms) does not suffer from the occurrence of artifacts, overenhancement and unnatural effects in the processed images. Performance comparisons with several state-of-the-art contrast enhancement algorithms show that the proposed method achieves appreciable contrast enhancement under diverse conditions. Toet and Wu: Efficient contrast enhancement through log-power histogram modification Mean value (over all 13 test images) of eight different contrast enhancement metrics (AMBE, H, EME, IEM, MSSIM and its luminance, contrast, and structural subcomponents MSSIM_L, MSSIM_C and MSSIM_S) for each of the seven contrast enhancement methods (THE, BEASF, BPDFHE, CD, NMHE, DPL, and IPL) investigated in this study. In each subplot, the contrast enhancement methods are arranged from left to right in order of increasing performance (the rightmost method has best performance). The graph for H also shows the mean value for the original input images. The proposed methods (DPL and IPL) are represented with filled symbols to enable easy comparison. The error bars represent the standard error of the mean. Toet and Wu: Efficient contrast enhancement through log-power histogram modification Fig. 13 Mean computing time (s) over all 13 test images for each of the seven contrast enhancement methods investigated in this study. The methods are arranged from left to right in order of increasing computing time. The proposed methods (DPL and IPL) are represented with filled symbols to enable easy comparison. The error bars represent the standard error of the mean.

When skin areas like faces and hands are imaged under natural environments their color appearance is frequently affected by variations in illumination intensity and chromaticity. In color-based skin tracking and detection, changing intensity is often dismissed either with the use of normalized, intensity-invariant color coordinates or by additionally modeling possible skin intensities. Chromaticity variations are rarely considered although they are common in practice. In most approaches considering chromaticity the experiments are done with a small or undefined variation range. It is difficult to compare different approaches and assess their applicability range for this reason. To improve the situation, we evaluate the performance of four state-of-the-art methods under drastic but practically common illumination changes. The effect of illumination chromaticity for skin is clearly defined, and based on it we draw conclusion about the performance of these approaches.

We propose an optical image encryption algorithm based on Arnold transform and gyrator transform. The amplitude and phase, which are the outputs of gyrator transform, are separated into several sub-images. Arnold transform is introduced for scrambling the data of the sub-images. The random spectrum composed of the scrambled sub-images is transformed by gyrator transform. An iterative structure of the algorithm is designed for enhancing the security of the encryption algorithm. The parameters of gyrator transforms and separating scheme serve as the key of the encryption method. The encryption process can be implemented by an electro-optical setup. Some numerical simulations have been given to demonstrate the security and validity of this algorithm. © 2011 SPIE and IS&T.

© 2006 Springer Science+ Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+ Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), ...

Abstract. In recent work, we introduced some topological notions for grayscale images based on a cross-section topology. In particular, the notion of destructible point, which corresponds to the classical notion of simple point, allows us to build operators that simplify a grayscale image while preserving its topology. In this paper, we introduce new notions and operators in the framework of the crosssection topology. In particular, the notion of the-destructible point allows us to selectively modify the topology, based on a local contrast ...

We propose a supervised approach to detect falls in home environment using an optimised descriptor adapted to real-time tasks. We introduce a realistic dataset of 191 videos, a new metric allowing to evaluate fall detection performance in a video stream, and an automatically optimised set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named ST HF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms and moments of order 0, 1 and 2). We study the combinations of usual transformations of the features (Fourier Transform, Wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using Support Vector Machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol which enables performance to be improved by updating the training in the current location with normal activities records.

Abstract. We present a new approach for computer-generated integral photography (IP) based on ray tracing, for the reconstruction of high quality photorealistic 3-D images of increased complexity. With the proposed methodology, all the optical elements of a single stage IP capturing setup are physically modeled for the production of real and virtual orthoscopic IP images with depth control. This approach is straightforward for translating a computer-generated 3-D scene to an IP image, and constitutes a robust methodology for developing modules that can be easily integrated in existing ray tracers. An extension of this technique enables the generation of photorealistic 3-D videos [integral videography (IV)] and provides an invaluable tool for the development of 3-D video processing algorithms.

The book is valuable not only to the conservationist, but also to any content providers interested in understanding the imaging techniques provided for the acquisition and presentation of this kind of data. I believe that as the Internet moves to the next level, many of the topics ...

After having carefully evaluated your article titled "Computation less Radon based Palmprint Characterization" and taken the referees' advice into consideration, the editors came to the conclusion that your paper is suitable for publication in our Journal. In order to save time, the referees communicated their opinion to us verbally. As part of our evaluation process, we normally ask the opinion of two referees who are experts in the relevant field of research. The paper is also read by the editor. If both of the referees and an editor concur in their view, their decision is final. We consult a third referee if there is a difference of opinion. In order to expedite the proceeding, which is one of the objectives of the journal, we do not require a full report on the paper from the referees. The choice of referees and the fact that we require a consensus view between the referees and an editor ensures, however, that the process is as fair as possible. We will include your article in the forthcoming issues of the journal upon receipt of the publication fee.

Many research studies have demonstrated that gait can serve as a useful biometric modality for human identification at a distance. Traditional gait recognition systems, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised performance. We investigate the problem of selecting a subset of the most relevant gait features for improving gait recognition performance. This is achieved by discarding redundant and irrelevant gait features while preserving the most informative ones. Motivated by our previous work on feature subset selection using genetic algorithms (GAs), we propose using GAs to select an optimal subset of gait features. First, features are extracted using kernel principal component analysis (KPCA) on spatiotemporal projections of gait silhouettes. Then, GA is applied to select a subset of eigenvectors in KPCA space that best represents a subject's identity. Each gait pattern is then represented by projecting it only on the eigenvectors selected by the GA. To evaluate the effectiveness of the selected features, we have experimented with two different classifiers: k nearest-neighbor and Naïve Bayes classifier. We report considerable gait recognition performance improvements on the Georgia Tech and CASIA databases.

Color interpolation solutions drastically influence the quality of the whole image generation pipeline, so they must guarantee the rendering of high quality pictures avoiding typical artifacts such as blurring, zipper effects and false colors. Moreover, demosaicing should avoid emphasizing typical artifacts of real sensors data, such as noise and green imbalance effect, which would be further accentuated by the subsequent steps of the processing pipeline. In this paper we propose a new adaptive algorithm, which decides the interpolation technique to apply to each pixel, according to its neighborhood analysis. Edges are effectively interpolated through a directional filtering approach which interpolates the missing colors selecting the suitable filter depending on the edge orientation. Regions close to edges are interpolated through a simpler demosaicing approach. Thus flat regions are identified and heavily low pass filtered in order to eliminate some residual noise and to minimize the annoying green imbalance effect. Finally an effective false colors removal algorithm is used as a post-processing step, in order to eliminate residual color errors. The experimental results show how sharp edges are preserved whereas undesired zipper effects are reduced, improving the edge resolution itself and obtaining superior image quality.

We present a new image compression technique called \DjVu " that is speci cally geared towards the compression of high-resolution, high-quality images of scanned documents in color. This enables fast transmission of document images over low-speed connections, while faithfully reproducing the visual aspect of the document, including color, fonts, pictures, and paper texture.

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. We argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by one-class support vector machine (SVM) is bounded by a closed hyper sphere, we propose a tracking method utilizing one-class SVMs that adopt histograms of oriented gradient and 2bit binary patterns as features. Thus, it is called the one-class SVM tracker (OCST). Simultaneously, an efficient initialization and online updating scheme is proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods that tackle the problem using binary classifiers on providing accurate tracking and alleviating serious drifting.

We present a comparison between two color equalization algorithms: Retinex, the famous model due to Land and Mc-Cann, and Automatic Color Equalization (ACE), a new algorithm recently presented by the authors. These two algorithms share a common approach to color equalization, but different computational models. We introduce the two models focusing on differences and common points. An analysis of their computational characteristics illustrates the way the Retinex approach has influenced ACE structure, and which aspects of the first algorithm have been modified in the second one and how. Their interesting equalization properties, like lightness and color constancy, image dynamic stretching, global and local filtering, and data driven dequantization, are qualitatively and quantitatively presented and compared, together with their ability to mimic the human visual system.

The proposed new spectral reflection model enhances the classical Clapper-Yule model by taking into account the fact that proportionally more incident light through a given colorant surface is reflected back onto the same colorant surface than onto other colorant surfaces. It comprises a weighted mean between a component specifying the part of the incident light that exits through the same colorant as the colorant from which it enters (Saunderson corrected Neugebauer component) and a component specifying the part of the incident light whose emerging light components exit from all colorants (Clapper-Yule component). We also propose models for taking into account ink spreading, a phenomenon that occurs when printing an ink halftone in superposition with one or several solid inks. The ink-spreading model incorporates nominal-to-effective surface coverage functions for each of the different ink superposition conditions. A system of equations yields the effective ink surface coverages of a color halftone as a weighted mean of the ink surface coverages specific to the different superposition conditions. The new spectral reflection prediction model combined with the ink-spreading model yields excellent spectral reflection predictions for clustered-dot color halftones printed on an offset press or on thermal transfer printers. © 2005 SPIE and IS&T.

We present a robust region segmentation method based on a pseudo-distance map (PDM) that uses a watershed algorithm as a segmentation tool. The PDM is a regularized version of a Euclidean distance map (EDM) directly computed from the edge-strength function (ESF) of an input ...

An implementation for parametric snakes used for object tracking is proposed via generalized deterministic annealing (GDA). Given an arbitrary energy functional that quantifies the quality of the contour solution, GDA computes the snake position by approximating the solution given by stochastic simulated annealing. First, the Markov chain representing the solution space for the snake position is broken into N smaller, local Markov chains representing the position of each discrete snake sample. At each annealing temperature, GDA directly approximates the stationary distribution of the local Markov chains using a mean field approximation for neighboring snake sample positions, and the final distribution reveals the solution. In contrast to the typical implementation via gradient descent, annealing methods can avoid suboptimal local solutions and can be used to compute snakes that are effective in the presence of severe noise and distant initial positions. Unlike simulated annealing, GDA does not utilize random moves to slowly locate a high quality solution and is thus appropriate for time critical applications. In the paper, synthetic experiments (on 231 images) are provided that compare the edge localization performance of snakes computed by GDA, simulated annealing and gradient descent for conditions of varying noise and varying initial snake position. The effectiveness of GDA is also demonstrated in a challenging real-data application (on 910 images) in which white blood cells are tracked from video microscopy.

In this article, several practical algorithms are proposed to support content-based video analysis, modeling, representation, summarization, indexing, and access. First, a multilevel video database model is given. One advantage of this model is that it provides a reasonable approach to bridging the gap between low-level representative features and high-level semantic concepts from a human point of view. Second, several model-based video analysis techniques are proposed. In order to detect the video shots, we present a novel technique, which can adapt the threshold for scene cut detection to the activities of variant videos or even different video shots. A seeded region aggregation and temporal tracking technique is proposed for generating the semantic video objects. The semantic video scenes can then be generated from these extracted video access units (e.g., shots and objects) according to some domain knowledge. Third, in order to categorize video contents into a set of semantic clusters, an integrated video classification technique is developed to support more efficient multilevel video representation, summarization, indexing, and access techniques.

We propose a novel scanner characterization approach for applications requiring color measurement of hardcopy output in calibration, characterization, and diagnostics applications. The method is advantageous for common practical color printing systems that use more than the minimum of three colorants necessary for subtractive color reproduction; printing with cyan (C), magenta (M), yellow (Y), and black (K) is the most prevalent example we use in our description. The proposed method exploits the fact that for the scenarios in consideration, in addition to the scanner RGB values for a scanned patch, the CMYK control values used to print the patch are also available and can be exploited in characterization. An indexed family of 3D scanner characterizations is created, each characterization providing a mapping from scanner RGB to CIELAB for a fixed value of K, the latter constituting the index for the characterization. Combined together, the family of 3D characterizations provides a single 4D characterization that maps scanner RGB obtained from scanning a patch and the K control value used for printing the patch to a colorimetric CIELAB measurement for the patch. A significant improvement in the robustness of the method to variations in printing is obtained by modifying the K index to utilize the scanned output for a black-only patch printed with the corresponding K value instead of directly utilizing the control K value used at the printer. Results show that the proposed 4D scanner characterization technique can significantly outperform standard 3D approaches in the target applications. © 2007 SPIE and IS&T.

Halftoned separations of individual colorants, typically cyan, magenta, yellow, and black, are overlaid on a print substrate in typical color printing systems. Displacements between these separations, commonly referred to as "interseparation misregistration", can cause objectionable color shifts in the prints. We study this misregistration-induced color shift for periodic clustered-dot halftones using a spatiospectral model for the printed output that combines the Neugebauer model with a periodic lattice representation for the individual halftones. Using Fourier analysis in the framework of this model, we obtain an analytic characterization for the conditions for misregistration invariance in terms of colorant spectra, periodicity of the individual separation halftones, dot shapes, and misregistration displacements. We further exploit the framework in a hybrid analytical-numerical simulation that allows us to obtain quantitative estimates of the color shifts due to misregistration, thereby providing a characterization for these shifts as a function of the optical dot gain, halftone periodicities, spot shapes, and interseparation misregistration amounts. We present simulation results that demonstrate the impact of each of these parameters on the color shift and demonstrate qualitative agreement between our approximation and experimental data.

To solve the image deblurring problem, most of the recently proposed non-blind deblurring approaches focus on regularizing the solutions by adding image priors, such as sparse prior, piece-wise smooth prior (TV prior) and so on. This paper, however, presents a new term, which can be viewed as a supplement to the data fidelity term, to improve the non-blind deblurring result. The new term is based on the Relative Error in Frequency Domain (REFD) which can suppress the ringing artifacts and preserve the image details. And then, a non-blind image deconvolution algorithm is proposed via introducing the REFD term into the image deconvolution model. Furthermore, we extend this non-blind image deconvolution approach to the field of blind image deblurring. Finally, the experimental results on both simulated and real-life images demonstrate the performance and efficiency of our proposed algorithm when compared to some other state-of-the-art algorithms.

We present a modification of the new edge-directed interpolation method that eliminates the prediction error accumulation problem by adopting a modified training window structure, and extending the covariance matching into multiple directions to suppress the covariance mismatch problem. Simulation results show that the proposed method achieves remarkable subjective performance in preserving the edge smoothness and sharpness among other methods in the literaturé. It also demonstrates consistent objective performance among a variety of images.

We present a comparison between two color equalization algorithms: Retinex, the famous model due to Land and Mc-Cann, and Automatic Color Equalization (ACE), a new algorithm recently presented by the authors. These two algorithms share a common approach to color equalization, but different computational models. We introduce the two models focusing on differences and common points. An analysis of their computational characteristics illustrates the way the Retinex approach has influenced ACE structure, and which aspects of the first algorithm have been modified in the second one and how. Their interesting equalization properties, like lightness and color constancy, image dynamic stretching, global and local filtering, and data driven dequantization, are qualitatively and quantitatively presented and compared, together with their ability to mimic the human visual system.