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.

An oblique lattice system (OLS) is defined by two groups of parallel lines in a rectangle, and some properties of the system are mentioned. From the property of OLS that are constrained to rectangle sizes, it is shown that the system provides good mathematical treatments of lattices. Next, a simple algorithm to design integral lattice-based halftone masks by the OLS is introduced. In the algorithm, the "uniform balanced numbering" concept for general halftone masks is decomposed into a simple "local and global numberings" concept for integral lattice-based masks. The OLS is used to realize the local and global numberings concept and to know conditions of halftone masks to design. As a result, we can easily achieve halftone masks based on oblique lattices that realize the required conditions (mask size, resolution, line angle, etc.) and good image quality.

Fusion of texture and color is to simulate color texture images that are perceptually very close to the actual ones. There are three computational models, i.e., gray-to-color mapping (GCM), color-to-color mapping (CCM), and dichromatic-based (DICH) models. The CCM model is extended to three methods, namely, CCM-RGB, CCM-LCH, and CCM-lαβ, when it is applied in different color spaces. The DICH model contains two methods: DICH-GC and DICH-CC, considering the original image can be either gray scale or color. The color fidelity of these six methods is comparatively investigated in terms of image similarity between simulated and target images.

Imaging algorithms often require reliable methods to evaluate the quality effects of the visual artifacts that digital processing brings about. We adopt a no-reference objective method for predicting the perceived quality of images in a deterministic fashion. JPEG coding provides a significant and interesting case study. To enhance the coherence of the quality estimates with respect to the empirical evidence of the perceptual phenomenon, the system parameters are adjusted using subjective scores obtained from human assessors. Principal component analysis is first used to assemble a set of objective features that best characterize the information in image data. Then a neural network, based on the circular backpropagation (CBP) model, associates the selected features with the corresponding predictions of quality ratings and reproduces the scores process of human assessors. The neural model enables one to decouple the process of feature selection from the task of mapping features into a quality score. Results on a public database for an image-quality experiment involving JPEG-compressed images and comparisons with existing objective methods confirm the approach effectiveness.

The hidden Markov tree (HMT) model is a novel statistical model for image processing on wavelet domain. The HMT model captures the persistence property of wavelet coefficients, but lacks the clustering property of wavelet coefficients within a scale. We propose the contextual hidden Markov tree (CHMT) model to enhance the clustering property of the HMT model by adding extended coefficients associated with wavelet coefficients. The extended coefficients do not change the wavelet tree structure but enhance the intrascale dependencies of the HMT model. Hence, the training scheme of the HMT model can be modified to estimate the parameters of the CHMT model. In experiments, the proposed CHMT model produces almost better results than the HMT model produces for image denoising. Furthermore, the CHMT model requires fewer iterations of training than the HMT model to achieve the same denoised results.

Conventional pointwise adaptive contrast enhancement is very effective in adjusting the contrast of medical images with large regional brightness differences. However, the pointwise algorithm is computationally intensive, thus limiting its practical application. Two existing stepwise algorithms cut down the computational overhead by adopting regional interpolation and averaging techniques respectively. Based on these two stepwise algorithms, we describe an adaptive contrast enhancement in which a new kind of variable image filter suitable for array multiplication is employed. Experiments show that the new algorithm can avoid the generation of blocking effects and effectively reduce image distortion in enhancement. Moreover, the speed of the algorithm can be adjusted according to different requirements.

An automated multiscale segmentation approach for color images is presented. The scale-space stack is generated using the Perona-Malik diffusion approach and the watershed algorithm is employed to produce the regions at each scale. A minima-linking process by downward projection is carried out over the successive scales, and a region dissimilarity measure—combining scale, contrast, and homogeneity—is subsequently estimated on the finer scale (localization scale). The dissimilarity measure is estimated as a function of two different features, i.e., the dynamics of contours and the relative entropy of color region distributions, combined by means of a fuzzy-rule-based system. A region-merging process is also applied to the localization scale to produce the final regions. To validate the performance of the proposed multiscale segmentation, qualitative and quantitative results are provided in comparison to its single-scale counterpart. We also deal with the topic of localization scale selection. This stage is critical for the final segmentation results and can be used as a preprocessing step for higher level computer vision applications as well. A preliminary study of localization scale selection techniques is carried out. A scale selection method that originates from the evolution of the probability distribution of a region homogeneity measure across the generated scales is proposed next. The proposed algorithm is finally compared to a previously reported approach to indicate its efficiency.

We develop techniques for the implementation of motion estimation. Optical flow estimation has been proposed as a preprocessing step for many high-level vision algorithms. Gradient-based approaches compute the spatio-temporal derivatives, differentiating the image with respect to time and thus computing the optical flow field. Horn and Schunck's method in particular is considered a benchmarking algorithm of gradient-based differential methods, useful and powerful, yet simple and fast. They formulated an optical flow constraint equation from which to compute optical flow, which cannot fully determine the flow but can give the component of the flow in the direction of the intensity gradient. An additional constraint must be imposed, introducing a supplementary assumption to ensure a smooth variation in the flow across the image. The brightness derivatives involved in the equation system were estimated by Horn and Schunck using first differences averaging. Gradient-based methods for optical flow computation can suffer from unreliability of the image flow constraint equation in areas of an image where local brightness function is nonlinear or where there are rapid spatial or temporal changes in the intensity function. Little and Verri suggested regularization to help the numerical stability of the solution. Usually this takes the form of smoothing of the function or surface by convolving before the derivative is taken. Smoothing has the effects of suppressing noise and ensuring differentiability of discontinuities. The method proposed is a finite element method, based on a triangular mesh, in which diffusion is added into the system of equations.

A constant false alarm rate (CFAR) detection algorithm and a threshold selection algorithm are adapted and developed for use in multiband-image small-target detection. While it is often difficult to predict the spectral signatures of targets, the shape of the target may be known. This detection algorithm exploits geometric target features and spectral differences between the target and the surrounding area. The detection algorithm is derived from a general statistical model of the data with most emphasis on the background. The utility of CFAR algorithms is that the selection of a detection threshold can be made independently of image intensity. However, varied applications of the algorithms show that detection values are dependent on the scene adherence to the model. Achieving a CFAR in applications is very difficult. The threshold for a desired number of false alarms fluctuates with differing backgrounds. By appropriately mapping observations to the model, an automatic threshold selection algorithm is shown. Combining the CFAR-detection algorithm with the threshold selection algorithm produces a reliable constant false alarm rate.

Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wall.

The identification of the region of interest (ROI) in collimated x-ray images remains an important, open problem. The x-ray scattering present in all practical cases complicates the identification of a clearly defined ROI. Such scattering degrades image quality by producing a blurring of the boundaries delineating the ROI as well as corrupting the underlying gray-scale histogram. The construction of a proper identification scheme to locate the ROI in x-ray images will result in x-rays with improved visual clarity. A robust two-stage method of nonlinear preprocessing in the spatial domain to enhance the ROI boundary followed by extraction in the projection domain is proposed as an efficient means of ROI identification. The method proves effective for a wide variety of x-ray images taken with varying dosages. The results of simulations run in comparison with commonly used snake-based methods, which attempt to identify arbitrary ROIs using force on control points, show the proposed method yields better success rates in high- and low-SNR cases.

Spectral (color) and spatial (shape) features available in pictures are two significant sources of information for content-based retrieval of image databases. The developed adaptive shape transform approach originated from the premise that a two-dimensional (2-D) shape can be recovered from a set of Radon-transform-based projections. For search consistency, it is necessary to identify the region(s) of interest (ROI) before applying the Radon transform to the shape query. ROIs are detected automatically by means of saliency-map-based segmentation. The Radon transform packs the shape information of a 2-D object along the projection axes of known orientation, and generates a series of one-dimensional (1-D) functions from color channels for projection angles ranging from 0 to 180 deg. The optimal number of projections for a particular shape is determined by imposing the Kullback-Leibler divergence (KLD) distance measure as the similarity metric between the query and database images. The Radon transforms with the shortest and longest lengths yield the most distinctive shape attributes for the object classes being queried and enable the feature space to be invariant to translation and rotation in the spatial plane. The proposed algorithm is tested on a wide range of color images with complex shaped objects and different spatial resolutions. The KLDs between two images are calculated in the longest and shortest directions of the Radon transform, and then are summed together to find the similarity measure between the query and database pictures. Experimental results are close to those that a human observer expects. Further, the method is quite robust and it can account for high image noise (Shao and Celenk, 2001).

Coded as conventional color-map addresses, a color-mapped image can be decoded only when a full color map address is available. We present the first known scheme that adds progression capability in spatial resolution as well as color resolution, enabling more flexible progression of color-mapped images. During coding, approximate pixel colors are keyed with truncated versions of the original color-map addresses. Subsequently, the coded image is transmitted by interleaving the per pixel address bit order for color resolution refinement, and a spatial pixel order for spatial resolution refinement. The interleaving is subject to user control through spatial-color progression rate control (SCPRC), a novel means for biasing the progression to accelerate either spatial resolution or color resolution for more informative early views of the image being transmitted.

Interest in multiresolution techniques for image processing and coding is increasing steadily. An attractive instance is pyramid decomposition schemes due to their low computational complexity, simple parallel implementation, and ability to produce acceptable color images at moderate data rates. We present an approach combining a modified pyramidal algorithm and mathematical morphology for color image coding applications denoted as the adaptive morphological pyramid coding (AMPC) algorithm. The proposed AMPC pyramidal strategy involves (1) building a multilevel pyramid based on a developed adaptive morphological filter that preserves the significant details (without shape bias) and does not require very high computational cost, and then (2) the resulting pyramid levels (images) are applied to suitable coding techniques including an adaptive 3-D vector quantization (AVQ). The proposed technique gives an average peak SNR (PSNR) in the range 46.78 to 50.7 dB when no additional coding is applied. When AVQ and differential pulse code modulation (DPCM) are applied to the pyramid levels an average PSNR varies in the range 22.2 to 37.8 dB) at bit rates in the range 0.07 to 1.51 bpp is obtained. When compared with JPEG/JPEG2000, embedded zero-tree wavelet (EZW) coding and set partition in hierarchical trees (SPIHT), the proposed algorithm provides comparable reconstruction quality.

We perform an empirical evaluation of the lossy compression properties of the JPEG2000 standard for watermarking and fingerprinting purposes. The JPEG2000 standard is used as a basic tool for determining both how and where the embedded watermark should be placed in the image. The original image is slightly modified to generate a similar image (but indistinguishable by the observer), and the mark is embedded in the pixels presenting differences between both images. The reconstruction process uses the original and the modified images to detect the embedded mark in any possible attacked image, so the watermarking scheme is nonblind. Previous experiments show that the properties of the resulting watermarking scheme depend strongly on the transformation stage characteristics of the lossy image compression system. Several parameters related to the JPEG2000 standard are tested, in addition to the compression ratio determined by the desired bit rate: the wavelet transform, block size, and the number of levels of decomposition for each block. These parameters affect not only the capacity but also the robustness of the watermarking scheme, which depends on the number of differing pixels between the original image and the slightly modified one, and the distribution of such differences. For evaluation purposes, we use the Stirmark benchmark and the classical image corpus set for lossy image compression. We compare the impact on system performance of each of the JPEG2000 standard parameters for several kinds of attacks, namely, filtering (including sharpening); JPEG lossy compression; cropping; row and column removal; and a combination of rotation, cropping, and scaling. The false positive rate is also studied. Results show that the proposed watermarking scheme based on the JPEG2000 standard is robust against most of the classical image manipulation operation, and therefore, is suitable for watermarking purposes.

We present a method to include watermarks in printed images. With accurate printer calibration, in theory, the same color under different gray component replacement (GCR) strategies should look the same, under specific viewing conditions. We spatially vary the GCR along the image in a manner that is not perceptible, and we employ an estimation method to detect such changes. The choice of GCR for a given pixel (or region) comprises an additional information channel that embeds a watermark or hidden information. The challenge is how to detect which GCR was used and that is our focus. For that, we estimate the RGB value of each pixel and the CMYK values intended to be put onto the paper by scanning the printed page. With that information, we can estimate which GCR strategy was used in a given region and retrieve the watermark message. Instead of focusing on a particular watermarking scheme, we are concerned only with the practical aspects of producing a spatially varying GCR and of robustly estimating which GCR strategy was used at a region. Promising GCR detection results are shown to illustrate the method's potential to watermark printed images.

We propose a new compression-based algorithm that combines compression using lattice vector quantization (LVQ) in the wavelet domain and data hiding. It deals with two major constraints. (1) compression must not degrade the embedded mark and (2) watermarking must not affect coding performances. Our approach is based on a new multidimensional dead zone that enables us to maintain a sparse representation of the quantized wavelet coefficients, as well as a constant-entropy insertion method. Furthermore, an algorithm to estimate the quantization parameters is proposed. It leads to both variable rate encoding and bit rate adapted data hiding.

We introduce a new secret sharing scheme, where the novelty lies in the use of a fixed angle segmentation technique to create circular shadow images called shares. In this way, the shares can be stacked in different angles to reveal different secret messages. The participants must be in possession of both the shares and the stacking angles, which creates the additional degree of protection.

Visual cryptography is a powerful technique that combines the notions of perfect ciphers and secret sharing in cryptography with that of raster graphics. A binary image can be divided into shares that can be stacked together to approximately recover the original image. Unfortunately, it has not been used much primarily because the decryption process entails a severe degradation in image quality in terms of loss of resolution and contrast. Its usage is also hampered by the lack of proper techniques for handling gray-scale and color images. We develop a novel technique that enables visual cryptography of color as well as gray-scale images. With the use of halftoning and a novel microblock encoding scheme, the technique has a unique flexibility that enables a single encryption of a color image but enables three types of decryptions on the same ciphertext. The three different types of decryptions enable the recovery of the image of varying qualities. The physical transparency stacking type of decryption enables the recovery of the traditional visual cryptography quality image. An enhanced stacking technique enables the decryption into a halftone quality image. Finally, a computation-based decryption scheme makes the perfect recovery of the original image possible. Based on this basic scheme, we establish a progressive mechanism to share color images at multiple resolutions. We extract shares from each resolution layer to construct a hierarchical structure; the images of different resolutions can then be restored by stacking the different shared images together. Thus, our technique enables flexible decryption. We implement our technique and present results.

This PDF file contains the editorial “Book Review: 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications” for JEI Vol. 14 Issue 03