The process of adding high-density information onto printed material enables and improves interesting hardcopy document applications, such as: security, authentication, physical-electronic round tripping, item-level tagging as well as consumer/product interaction. This investigation on robust and high capacity print codes aims to maximize information payload in a given printed page area, subject to robustness to distortions originated by printing and scanning processes and also to degradations introduced by user manipulation of printed documents. The novel approach includes statistical print-and-scan channel characterization, designing of robust segmentation, unsupervised Bayesian color classification with expectation-maximization algorithm for parameters estimation of a mixture of Gaussians model and design of error correction codes. Results illustrate the performance evaluated under real channel and distortions conditions. High payload is achieved with sufficient robustness to distortions resulting of regular office hardcopy document handling: print-and-scan channel and user manipulation.

Adding high-density information to printed materials enables and improves interesting hardcopy document applications involving security, authentication, physical-electronic round tripping, item-level tagging, and consumer/product interaction. This investigation of robust and high capacity print codes aims to maximize information payload in a given printed page area, subject to robustness to channel errors including distortions introduced by the printing and scanning processes and also due to the usual degradations introduced by user manipulation of printed documents. The novel approach includes statistical print-and-scan channel characterization, designing of robust segmentation using visual cues, unsupervised Bayesian color classification with expectation-maximization algorithm for parameters estimation of a mixture of Gaussians model and design of error correction codes. Results illustrate the performance evaluated under real channel and distortions conditions. High payload is achieved with sufficient robustness to distortions resulting of regular office hardcopy document handling: print-and-scan channel and user manipulation.

This paper investigates hardcopy watermarking based on the embedding of transparent logos into documents. Grayscale logos are embedded into specific locations of the text document aiming communication over the print-and-scan (PS) channel. The authenticating message is encoded into the document by selecting the relative position of the logos. At the detector, the logo positions are estimated by correlation and an optimal detection threshold is derived considering a print-scan channel model. An error-rate analysis is provided for the method. By employing character segmentation, the technique can be also combined with other popular text hardcopy watermarking techniques. The method is verified to be robust for the PS channel with high transparency. Results illustrate the validity of the analyses and the applicability of the method to practical documents.

We derive a novel method to determine the parameters for regularized super-resolution problems. The proposed approach relies on the Joint Maximum a Posteriori (JMAP) estimation technique. The classical JMAP technique provides solutions at low computational cost, but it may be unstable and presents multiple local minima. We propose to stabilize the JMAP estimation, while achieving a cost function with an unique global solution, by assuming a gamma prior distribution for the hyperparameters. The resulting fidelity is similar to the quality provided by the best methods such as the Evidence, which are computationally expensive. Experimental results illustrate the low complexity and stability of the proposed method.

In this paper we propose a new image event detection method for identifying flood in videos. Traditional image based flood detection is often used in remote sensing and satellite imaging applications. In contrast, the proposed method is applied for retrieval of flood catastrophes in newscast content, which present great variation in flood and background characteristics, depending on the video instance. Different flood regions in different images share some common features which are reasonably invariant to lightness, camera angle or background scene. These features are texture, relation among color channels and saturation characteristics. The method analyses the frame-to-frame change in these features and the results are combined according to the Bayes classifier to achieve a decision (i.e. flood happens, flood does not happen). In addition, because the flooded region is usually located around the lower and middle parts of an image, a model for the probability of occurrence of flood as a function of the vertical position is proposed, significantly improving the classification performance. Experiments illustrated the applicability of the method and the improved performance in comparison to other techniques.

This paper proposes the use of higher order statistical moments in document image processing to improve the performance of systems which transmit side information through the print and scan channel. Examples of such systems are multilevel 2-D bar codes and certification via text luminance modulation. These systems print symbols with different luminances, according to the target side information. In previous works, the detection of a received symbol is usually performed by evaluating the average luminance or spectral characteristics of the received signal. This paper points out that, whenever halftoning algorithms are used in the printing process, detection can be improved by observing that third and fourth order statistical moments of the transmitted symbol also change, depending on the luminance level. This work provides a thorough analysis for those moments used as detection metrics. A print and scan channel model is exploited to derive the relationship between the modulated luminance level and the higher order moments of a halftone image. This work employs a strategy to merge the different moments into a single metric to achieve a reduced detection error rate. A transmission protocol for printed documents is proposed which takes advantage of the resulting higher robustness achieved with the combined detection metrics. The applicability of the introduced document image analysis approach is validated by comprehensive computer simulations.

This paper improves the use of text color modulation (TCM) as a reliable text document data hiding method. Using TCM, the characters in a document have their color components modified (possibly unperceptually) according to a side message to be embedded. This work presents a detection metric and an analysis determining the detection error rate in TCM, considering an assumed print and scan (PS) channel model. In addition, a perceptual impact model is employed to evaluate the perceptual difference between a modified and a non-modified character. Combining this perceptual model and the results from the detection error analysis it is possible to determine the optimum color modulation values. The proposed detection metric also exploits the orientation characteristics of color halftoning to reduce the error rate. In particular, because color halftoning algorithms use different screen orientation angles for each color channel, this is used as an effective feature to detect the embedded message. Experiments illustrate the validity of the analysis and the applicability of the method.

This paper proposes a new coding method that increases significantly the signal-to-watermark ratio in document watermarking algorithms. A possible approach to text document watermarking is to consider text characters as a data structure consisting of several modifiable features such as size, shape, position, luminance, among others. In existing algorithms, these features can be modified sequentially according to bit values to be embedded. In contrast, the solution proposed here uses a positional information coding approach to embed information. Using this approach, the information is related to the position of modified characters, and not to the bit embedded on each character. This coding is based on combinatorial analysis and it can embed more bits in comparison to the usual methods, given a distortion constraint. An analysis showing the superior performance of positional coding for this type of application is presented. Experiments validate the analysis and the applicability of the method.

This paper uses higher order statistical moments to improve the detection in systems which transmit side information through the print and scan channel, such as 2-D bar codes and text luminance watermarking. When these systems print symbols using halftoning, symbol detection can be performed by evaluating second, third, and fourth order statistical moments of the transmitted symbol, in addition to the average luminance level. A print and scan channel model is described. The relationship between the modulated luminance and the higher order moments of a halftone image is analyzed. The detection error rate is reduced by merging the different moments into a single metric. Experiments validate the analyses and the applicability of the method.

This paper proposes improvements to the code division modulation and multiplexing (CDM) spread spectrum watermarking. The improved CDM technique exploits all the available information at the embedded and determines the required energy for the spreading sequences to achieve a specified bit error probability. This approach considers an additive noise in the channel and all the interferences from the host signal, cross-correlation between spreading sequences, perceptual masking and interferences from collusion attacks. We provide results to illustrate the improvements achieved and also comparisons with traditional CDM watermarking.

This paper analyzes the use of text halftone modulation (THM) as a text hardcopy watermarking method. Using THM, text characters in a document have their luminances modified from the standard black to a gray level generated with a given halftone screen, according to a message to be transmitted. The application of THM has been discussed in ((R. Villan et al., 2006), (K. Matsuit and K. Tanaka, 1994)). In this paper, a spectral metric is proposed to detect the embedded message. Based on this metric, an error rate analysis of halftone modulation is presented considering the effects of the print and scan channel. Experiments validate the analysis and the applicability of the method.

This work presents an efficient post-segmentation method for separating text from the background in document images. For this task, this paper proposes the use of textured patterns to represent text in documents, instead of the standard black. It is shown that, in poor quality documents, text segmentation is more efficient when the characters in the document are represented in a halftoned gray level prior to printing. This occurs because the halftoning process induces statistical characteristics that help the text to be distinguished from noise or background. A typical case are noisy printed and scanned documents. Experiments validate the analysis and the applicability of the segmentation method. An important application for the method is in the postal service, where letters have their addresses segmented for automatic sorting.

This work presents a new class of algorithms for super-resolution (SR) of image sequences. This class of algorithms estimates simultaneously all frames of a sequence by employing an iterative minimization of a regularized cost function. Similarly to other SR techniques, the proposed approach exploits the correlation among the frames of the sequence. This correlated information helps to improve the resolution of the captured images. By employing the motion information only in the prior term of the cost function, the proposed method achieves a better fidelity and more robust performance, when compared to other methods. This original approach provides a fidelity equivalent to the obtained by the simultaneous methods, while achieving a lower computational complexity. The isolation of the motion equations in the prior term allows an increased control over the motion errors, and as a consequence, provides an improved robustness. The performance of the proposed method is discussed and compared with other methods in the literature. In the comparative experiments, it is considered both the minimization over the Euclidean norm, which is employed to achieve low computational complexity, and the minimization over the Huber norm or norm, which is used to produce images with sharp edges and higher robustness to large motion errors.

In this paper we propose a new method to embed hidden data in office-like documents, presenting robustness to the print-scan channel. The method slightly modulates the luminance of the documents' characters and symbols, being this change unnoticeable to the human eye, yet detectable by a scanner. It is based on pulse amplitude modulation communications, using "character shaped" pulses instead of traditional pulses, using text spacing to avoid intersymbol interference. We present an error analysis for the method, along with a new print-scan channel model for text documents. The analyses and applicability of the method are validated by experiments

In this work, we propose a new robust and edge-preserving super-resolution algorithm to simultaneously estimate all frames of a sequence. The new algorithm is based on the regularized super-resolution approach. In contrast to other multi-frame super-resolution algorithms, the proposed algorithm does not include the motion in the observation model. Instead, transformations caused by the motion are used in the prior model to produce a sequence with improved quality and smoothness in the motion trajectory. We use a Huber norm in the prior term to achieve an algorithm robust to outliers in the motion model while avoiding blurring of edges. The proposed method is significantly more robust than other simultaneous super-resolution methods. We provide results to illustrate the performance of the algorithm.

In this work, we propose an informed positional coding scheme by extending the position based watermarking approach (Borges et al. (2003)). In contrast to the traditional watermarking approaches where the information is carried by the watermark itself, in the proposed positional approach, the information is related to the position of the embedded watermarks in the image. Moreover, we propose an optimization criterion to select the best among a few available positions to embed a watermark to represent information and also the best watermark from a codebook of watermarks. Results illustrate the better robustness and transparency performance when compared to the traditional spread spectrum techniques. Moreover, we show that the method presents better robustness to collusion attacks, as illustrated by experiments.

This paper proposes an informed spread spectrum watermarking technique for the embedding of multi-bits into images using blind detection. The new technique exploits the information available at the embedder to eliminate all the interferences from the host, patterns and perceptual masking. A statistical analysis leads to the minimum embedding strength required by each pattern to achieve a specified bit error probability for a given additive noise distortion. As a result, each message bit will present the same degree of robustness. Experimental results illustrate the performance achieved for a set of typical valumetric attacks.

In this work, we propose a new super-resolution algorithm to simultaneously estimate all frames of a video sequence. The new algorithm is based on the Bayesian maximum a posteriori estimation. In contrast to other multi-frame super-resolution algorithms, the proposed algorithm does not include the motion in the observation model. Instead, transformations caused by the motion are used as a prior information in order to achieve smoothness in the motion trajectory. The proposed algorithm provides lower computational complexity than the traditional super-resolution algorithms when several frames need to be restored. The new algorithm is also robust to motion errors and outliers. We provide results to illustrate the superiority of the new method.

In this paper we present a practical blind detection system for multi-bit watermarking. In contrast to Hernandez et al. (1999), we propose a deterministic embedding scheme that does not depend on statistical assumptions about the original image. The proposed system can be considered an extension of Cox et al. (2000) for multi-bit messages using code division multiplexing. By informed embedding, using the knowledge of the original image and the detection scheme, the proposed system assures total embedding efficiency. We analyze the impact of valumetric distortion sources on the ability to recover the watermark message and propose a simple parameter to control system robustness. Experimental results obtained for spatial domain watermarking illustrate the efficiency of the proposed system.

A new approach based on morphological operators is presented for identification of individuals by segmentation and analysis of the iris. Several morphological operators are developed to segment the iris region from the eye image and also to highlight chosen iris patterns. The extracted features are used to represent and uniquely characterize the iris. In order to properly extract the desired patterns, we also propose an algorithm to produce skeletons with unique paths among end-points. This new representation, obtained by the proposed morphological operators is stored for identification purposes. Results are presented to illustrate the efficiency of the identification system. The proposed system was derived to present low complexity implementation and low storage requirements.

A non-iterative post-processing enhancement technique is proposed for images degraded by either the JPEG-DCT or the JPEG-LS (LOCO) lossy coding algorithm. A degraded image is classified into active and smooth regions. A distance transform is applied to the resulting classification, and is used to determine the size and order of a Bezier surface patch. These Bezier blending surfaces, built with Bernstein polynomials, provide an interesting representation for the image. This approach mitigates the quantization noise while preserving strong edges and textures. Results illustrate the significant visual improvement achieved with a computational complexity of O(n).

The proposed novel lossy image compression approach represents an image as segments comprised of variable-sized right-angled triangles. The recursive triangular partitioning proposed is shown to be more efficient than square partitioning. A novel and economic blending model (similar to Bezier polynomials) is applied to represent each triangular surface. A framework to design blending surfaces for triangular regions is presented. This economic model allows coefficient (control point) sharing among neighbor triangles. Sharing results in blockiness reduction as compared to block-based techniques. The technique is specially appealing to images with smooth transitions. Compression and visual quality results compare favorably against a wavelet codec using decomposition into seven bands. As an alternative, a greedy algorithm based on priority queues is proposed to further reduce the entropy of the control point bitstream. This optimization step achieves better performance in a rate-distortion R-D sense when compared to uniform quantization of the control points.

Summary form only given. We post-process images compressed by lossy JPEG algorithms (DCT and LOGO), following decompression, using Bezier polynomials for enhancement. Both JPEG algorithms, namely the DCT-based JPEG and the near-lossless baseline JPEG-LS (aka LOGO), employ spatial quantization. The DCT-based version introduces an artifact known as mosaic or "blockiness" by coarsely quantizing the DC coefficients. By quantizing the prediction errors, JPEG-LS introduces a "banding" artifact specially noticeable for quantization coarser than "8-off". The human visual system (HVS) is specially sensitive to degradations on smooth regions in the image. We propose a post-processing algorithm based on blending for enhancement of the degraded smooth regions due to quantization. In this work, we use Bezier polynomials of variable orders. Enhancement is obtained by blending each pixel in low-activity regions so the artifacts are reduced. The coefficients of the Bezier polynomials for the various orders used (from 3rd to 7th order) are precomputed and stored in look-up tables for speed. Active pixels, detected by using a threshold of 3/2 of the quantization step size, are left alone. The threshold is theoretically justified, avoids the problem of threshold determination on ordinary segmentation, and excludes quantization noise. In order to avoid over-smoothing of large low activity regions, the Bezier polynomials' order used to blend each pixel P/sub o,j/ is variable. Results are presented for lossy JPEG and JPEG-LS images for very low bit-rates.