Image Modeling

A review of the field of lightness perception from Helmholtz to the present shows the most adequate theories of lightness perception to be the intrinsic image models. Nevertheless, these models fail on 2 important counts: They contain no anchoring rule, and they fail to account for the pattern of errors in surface lightness. Recent work on both the anchoring problem and the problem of errors has produced a new model of lightness perception, one that is qualitatively different from the intrinsic image models. The new model, which is based on a combination of local and global anchoring of lightness values, appears to provide an unprecedented account of a wide range of empirical results, both classical and recent, especially the pattern of errors. It provides a unified account of both illumination-dependent failures of constancy and background-dependent failures of constancy, resolving a number of longstanding puzzles. We present a new theory of how the visual system assigns lightness values-or perceived black, white, or gray values-to various regions of the retinal image. The theory grew out of two problems facing what are probably the most advanced models of lightness perception: the intrinsic image models.

... stego data aiming at disrupting the watermark detection and of resolving copyrights, taking into account the statistics of the embedded watermark and exploiting features of the human visual system. In the second part of the paper we propose a “second generation benchmark”. ...

Knowledge of the spatial distribution of transmission field B 1 + and reception sensitivity maps is important in high-field (≥3 T) human magnetic resonance (MR) imaging for several reasons: these include post-acquisition correction of intensity inhomogeneities, which may affect the quality of images; modeling and design of radiofrequency (RF) coils and pulses; validating theoretical models for electromagnetic field calculations; testing the compatibility with MR environment of biomedical implants. Moreover, inhomogeneities in the RF field are an essential source of error for quantitative MR spectroscopy. Recent studies have also shown that B 1 + and reception sensitivity maps can be used for direct calculation of tissue electrical parameters and for estimating the local specific absorption rate (SAR) in vivo.

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the

A survey of current literature and corporate identity manuals may create the impression that corporate identity consists solely of visual identity cues. In this paper the view if! explored that corporate identity consists of both visual and behavioural cues. Most cqrporations strive towards a positive corporate image. This can be attained only by taking into account also such aspects as customer service and employee behaviour, and not just creating attractive buildings, unifbrms, logos and slogans. An analysis of selected literature and the results of four indq>endcrnt studies are rq>orted to support the prpposal of a Corporate Image Model fhat needs further research and refmement.

Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. These have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median fllter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by the test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive medianfllter (SAMF), is based on the detection of the size of the impulse noise.

We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries'' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.

Diffraction-enhanced imaging (DEI) is an analyser-based x-ray imaging method that produces separate images depicting the projected x-ray absorption and refractive properties of an object. Because the imaging model of DEI does not account for ultra-small-angle x-ray scattering (USAXS), the images produced in DEI can contain artefacts and inaccuracies in medical imaging applications. In this work, we investigate an extended DEI method for concurrent reconstruction of three images that depict an object's projected x-ray absorption, refraction and USAXS properties. The extended DEI method can be viewed as an implementation of the recently proposed multiple-image radiography paradigm. Validation studies are conducted by use of computer-simulated and synchrotron measurement data.

This paper presents several aspects of the application of regularization theory in image restoration. This is accomplished by extending the applicability of the stabilizing functional approach to 2-D ill-posed inverse problems. Image restoration is formulated as the constrained minimization of a stabilizing functional. The choice of a particular quadratic functional to be minimized is related to the a priori knowledge regarding the original object through a formulation of image restoration as a maximum a posteriori estimation problem. This formulation is based on image representation by certain stochastic partial differential equation image models. The analytical study and computational treatment of the resulting optimization problem are subsequently presented. As a result, a variety of regularizing filters and iterative regularizing algorithms are proposed. A relationship between the regularized solutions proposed and optimal Wiener estimation is also identified. The filters and algorithms proposed are evaluated through several experimental results.

We apply an AM-FM surface albedo model to analyze the projection of surface patterns viewed through a binocular camera system. This is used to support the use of modulation-based stereo matching where local image phase is used to compute stereo disparities. Local image phase is an advantagous feature for image matching, since the problem of computing disparities reduces to identifying local phase shifts between the stereoscopic image data. Local phase shifts, however, are problematic at high frequencies due to phase wrapping when disparities exceed 6. We meld powerful multichannel Gabor image demodulation techniques for multiscale (coarse-to-fine) computation of local image phase with a disparity channel model for depth computation. The resulting framework unifies phasebased matching approaches with AM-FM surface/image models. We demonstrate the concepts in a stereo algorithm that generates a dense, accurate disparity map without the problems associated with phase wrapping.

We propose an evolving scheme to detect slow as well as fast moving objects in a video sequence. The proposed scheme employ both spatio-temporal and temporal segmentation to obtain the video object plane and hence detection. We propose a compound Markov random field model as the a priori image model that takes into account the spatial distribution of the current frame, temporal frames and the edge maps of the temporal frames. The spatio-temporal segmentation is cast as a pixel labeling problem and the labels are the MAP estimates. The MAP estimates of a frame are obtained by a hybrid algorithm. The spatial segmentation of a given frame evolves to generate the spatial segmentation of the subsequent frames. The evolved spatial segmentation together with the temporal segmentation produces the Video Object Plane (VOP) and hence detection. Our scheme does require the computation of spatio-temporal segmentation of the initial frame thus speeding up the whole process. The results of the proposed scheme are compared with JSEG method are found to be better in terms of the misclassification error.

This paper is concerned with robust models for representing images. The robust methods in image models are also applied to some important image processing situations such as segmentation by texture and image restoration in the presence of outliers. We consider a non-symmetric half plane (NSHP) autoregressive image model, where the image intensity at a point is a linear combination of the intensities of the eight nearest points located on one quadrant of the coordinate plane, plus an innovation process. Robust estimation algorithms for dierent outlier processes in causal autoregressive models are developed. These algorithms are based on robust generalized M (GM) estimators. Theoretical properties of the robust estimation algorithms are presented. The robust estimation algorithm for causal autoregressive models is applied to image restoration. The restoration method based on robust image model cleans out the outliers without involving any blurring of the image. Experimental results show that the quality of images restored by the model-based method is superior to the images restored by other conventional methods. Ó

In this paper, we present a new fast algorithm for twodimensional (2-D) linear adaptive filtering using the fast Chandrasekhar equations. Using the analogy between the multichannel linear model and the 2-D one, we transform an image into multichannel sequence and we extend the fast Chandrasekhar adaptive multichannel filtering algorithm to the 2-D case i.e. image filtering. The performance of the new 2-D adaptive filter is tested by using this filter to estimate the coefficients of a 2-D Moving Average (2 -D MA) model of an unknown system. Furthermore, an application on adaptive noise cancellation of images is proposed throw a 2 -D adaptive noise canceller based on the 2 -D Chandrasekhar fast algorithm. Simulation results prove the superiority of the new 2 -D Chandrasekhar filter comparing to similar approaches for image model identification.

The problem of image deblurring in the presence of salt and pepper noise is considered. Standard image deconvolution algorithms, that are designed for Gaussian noise, do not perform well in this case. Median type filtering is a common method for salt and pepper noise removal. Deblurring an image that has been preprocessed by median-type filtering is however difficult, due to the amplification (in the deconvolution stage) of median-induced distortion. A unified variational approach to salt and pepper noise removal and image deblurring is presented. An objective functional that represents the goals of deblurring, noiserobustness and compliance with the piecewise-smooth image model is formulated. A modified L 1 data fidelity term integrates deblurring with robustness to outliers. Elements from the Mumford-Shah functional, that favor piecewise smooth images with simple edge-sets, are used for regularization. Promising experimental results are shown for several blur models.

We consider the problem of calibrating a highly generic imaging model, that consists of a non-parametric association of a projection ray in 3D to every pixel in an image. Previous calibration approaches for this model do not seem to be directly applicable for cameras with large fields of view and non-central cameras. In this paper, we describe a complete calibration approach that should in principle be able to handle any camera that can be described by the generic imaging model. Initial calibration is performed using multiple images of overlapping calibration grids simultaneously. This is then improved using pose estimation and bundle adjustment-type algorithms. The approach has been applied on a wide variety of central and non-central cameras including fisheye lens, catadioptric cameras with spherical and hyperbolic mirrors, and multi-camera setups.

Cultural heritage documentation projects very often combine different acquisition methods as tacheometry, photogrammetry and terrestrial laser scanning. In this paper we compare these three methods for recording of heritage buildings. Our test object is the medieval castle of Haut-Andlau (Alsace, France), documented in 2006-2008. This castle is of particular interest for our investigations since its architecture is the result of different construction styles completed over the centuries. Old buildings are rarely regularly shaped and most of their facades are made of very complex curved structures. The documentation of the castle became a true challenge, regarding the constraints implied on the one hand by the steep and abrupt terrain surrounding the castle and on the other hand by the dense and broadening vegetation close to the castle. It became rapidly obvious that a unique technique would not overcome the vegetation and relief obstacles. The main part of the data capture was based on TLS. For completion of the laser scanner data at occluded building parts and for accuracy assessment, stereoscopic as well as multi-image models have been captured. Advantages and disadvantages of the different methods will be discussed in this paper. A quality assessment leads to the analysis of the models derived from each technique and to a crucial experience for further campaigns.

This paper addresses the problem of category-level image classification. The underlying image model is a graph whose nodes correspond to a dense set of regions, and edges reflect the underlying grid structure of the image and act as springs to guarantee the geometric consistency of nearby regions during matching. A fast approximate algorithm for matching the graphs associated with two images is presented. This algorithm is used to construct a kernel appropriate for SVM-based image classification, and experiments with the Caltech 101, Caltech 256, and Scenes datasets demonstrate performance that matches or exceeds the state of the art for methods using a single type of features.

In this paper, first we clarify the differences between Contact Edge Roughness (CER) and Line Edge Roughness (LER) emanating from the circular form of the contact edge. Then, we investigate the effects of these differences on the characterization of the spatial and frequency aspects of CER: We define the heightheight correlation function for a circular contact hole, use its shape to understand low-frequency deformations of contact holes from the ideal circle, generate model contact holes with predetermined CER and deformations, and finally study the relation between CER and Critical Dimension (CD) variation of the contact holes on a wafer.

Project New technologies and reconnaissance and historical- geography and toponymy research of Toplica “land” aims to research the territory of the Toplica land, making the list of all localities dating between prehistory to the 16th century. Some of those are known archeological sites with material remains, and the others devastated throughout the history and witnessed today only by local tradition or toponymy. Both detailed reconnaissance and historical- geography research are conducted through extensive terrain examinations. Besides listing of localities, research team is working on gathering and sorting toponymical information. New information will profoundly extend and expand the understanding of this region. Simultaneously, new technologies used within this project would broaden the classic way of researching and thus supplement it with a new element - a possibility of making a precise map of all localities and cultural- historical monuments. All localities, known as well as unknown and undefined, will be mapped and their locations will be presented by the positions gathered from GPS. In addition, localities will be presented in a digital form through numerous photographs and films.
For the purpose of the project, two packets were tested: Photoshop and Photosynth, both on the material collected at localities of Toplica land. Testing Photoshop and Photosynth produced impression that Photoshop is more complex for using (with its numerous functions). It is good in image analysis and processing. Photosynth, on the other hand, has created very good 3D effect with its geometric topology of the pictures. Photosynth makes dynamic photos/videos, and allows exploring of places, objects, and events in detail unlike any other media; therefore, it is useful for archeological heritage, as well as all tangible (material) heritage.

In order to tackle problems such as shadowthrough and bleed-through, a novel defect model is developed which generates physically damaged document images. This model addresses physical degradation, such as aging and ink seepage. Based on the diffusive nature of the physical defects, the model is designed using virtual diffusion processes. Then, based on this degradation model, a restoration method is proposed and used to fix the bleed-through effect in double-sided document images using the reverse diffusion process. Subjective and objective evaluations are performed on both the degradation model and the restoration method. The experiments show promising results on both real and generated data.

Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Median-based approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradient-based edge-preserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L 1 norm, and elements from the Mumford-Shah functional are used for regularization. We show that the Mumford-Shah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results.

Multivariate analysis and calibration in spectroscopy have been widely used in many fields of applications in recent years. The number of components to use in such applications for sufficiently describing the spectra is dependent on the stability of positions of the peaks of interest. Unwanted shifts give rise to a more complex model, i.e., more components. Various kinds of shift correction can be applied as preprocessing tools to cope with these effects. In this paper we describe a general con-Ž. cept of how to model intensity peak height and position as separate phenomena. We described methods and theory from image modeling, and apply them on one-dimensional signals such as traditional Raman spectroscopy.

An appropriate image representation induces some good image treatment algorithms. Hypergraph theory is a theory of finite combinatorial sets, modeling a lot of problems of operational research and combinatorial optimization. Hypergraphs are now used in many domains such as chemistry, engineering and image processing. We present an overview of a hypergraph-based picture representation giving much application in picture manipulation, analysis and restoration: the Image Adaptive Neighborhood Hypergraph (IANH). With the IANH it is possible to build powerful noise detection an elimination algorithm, but also to make some edges detection or some image segmentation. IANH has various applications and this paper presents a survey of them.

Super-resolution (SR) of facial images from video suffers from facial expression changes. Most of the existing SR algorithms for facial images make an unrealistic assumption that the "perfect" registration has been done prior to the SR process. However, the registration is a challenging task for SR with expression changes. This paper proposes a new method for enhancing the resolution of low-resolution (LR) facial image by handling the facial image in a non-rigid manner. It consists of global tracking, local alignment for precise registration and SR algorithms. A B-spline based Resolution Aware Incremental Free Form Deformation (RAIFFD) model is used to recover a dense local non-rigid flow field. In this scheme, low-resolution image model is explicitly embedded in the optimization function formulation to simulate the formation of low resolution image. The results achieved by the proposed approach are significantly better as compared to the SR approaches applied on the whole face image without considering local deformations.

We study efficient and reliable methods of capturing and sparsely representing anisotropic structures in 3D data. As a model class for multidimensional data with anisotropic features, we introduce generalized three-dimensional cartoon-like images. This function class will have two smoothness parameters: one parameter β controlling classical smoothness and one parameter α controlling anisotropic smoothness. The class then consists of piecewise C β -smooth functions with discontinuities on a piecewise C α -smooth surface. We introduce a pyramid-adapted, hybrid shearlet system for the three-dimensional setting and construct frames for L 2 (R 3 ) with this particular shearlet structure. For the smoothness range 1 < α ≤ β ≤ 2 we show that pyramid-adapted shearlet systems provide a nearly optimally sparse approximation rate within the generalized cartoon-like image model class measured by means of non-linear N -term approximations.

... Samy Ait-Aoudia, Ramdane Mahiou, Elhachemi Guerrout ESI - Ecole nationale Supérieure en Informatique, BP 68M, O-Smar 16270 Algiers, Algeria [email protected], [email protected], [email protected] ... Potts model The Potts model is a generalization of the Ising model. ...

Lithographic masks are an important and increasingly complex part of systems for advanced optical and extreme ultraviolet (EUV) lithography. They introduce polarization and phase effects with a significant impact on the entire system performance. Rigorous electromagnetic field (EMF) modeling of the mask is indispensable for a predictive simulation of lithographic processes. This paper describes several alternative mask models and their integration into a lithography simulation framework. Several examples demonstrate the relevance of an accurate mask diffraction modeling for the prediction of the lithographic process performance.

In this paper we propose a character segmentation method for multispectral images of ancient documents. Due to the low quality of the images the main idea of this study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov Random Field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. Since the stroke properties and the Gaussian parameters for the imaging model are evaluated automatically the proposed segmentation method requires no training phase. We compared the method to state of the art character segmentation methods and demonstrate the effectiveness of combining spectral and spatial features for the segmentation of characters in multispectral images.

The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CG's) to adjust the highfrequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, we present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using our formulation, it can be shown that conventional ACE's use linear functions to compute the new CG's. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of our new algorithm.

Image processing methods (segmentation) are presented in connection with a modeling of image structure. An image is represented as a set of primitives, characterized bv their type, abstraction level, and a list of attributes. Entities (regions for example) are then described as a subset of primitives obeying particular rules. Image segmentation methods are discussed, according to the associated image modeling level. Their potential efficacity is compared, when applied to cytologic image analysis.

The main aim of this paper is to employ the total variation (TV) inpainting model to superresolution imaging problems. We focus on the problem of reconstructing a high-resolution image from several decimated, blurred and noisy low-resolution versions of the high-resolution image. We propose a general framework for multiple shifted and multiple blurred low-resolution image frames which subsumes several well-known superresolution models. Moreover, our framework allows an arbitrary pattern of missing pixels and in particular missing frames. The proposed model combines the TV inpainting model with the framework to formulate the superresolution image reconstruction problem as an optimization problem. A distinct feature of our model is that in regions without missing pixels, the reconstruction process is regularized by TV minimization whereas in regions with missing pixels or missing frames, they are reconstructed automatically by means of TV inpainting. A fast algorithm based on fixed-point iterations and preconditioning techniques is investigated to solve the associated Euler-Lagrange equations. Experimental results are given to show that the proposed TV superresolution imaging model is effective and the proposed algorithm is efficient. .sg (A.M. Yip).

This paper deals with blind separation of images from noisy linear mixtures with unknown coefficients, formulated as a Bayesian estimation problem. This is a flexible framework, where any kind of prior knowledge about the source images and the mixing matrix can be accounted for. In particular, we describe local correlation within the individual images through the use of Markov random field (MRF) image models. These are naturally suited to express the joint pdf of the sources in a factorized form, so that the statistical independence requirements of most independent component analysis approaches to blind source separation are retained. Our model also includes edge variables to preserve intensity discontinuities. MRF models have been proved to be very efficient in many visual reconstruction problems, such as blind image restoration, and allow separation and edge detection to be performed simultaneously. We propose an expectation-maximization algorithm with the mean field approximation to derive a procedure for estimating the mixing matrix, the sources, and their edge maps. We tested this procedure on both synthetic and real images, in the fully blind case (i.e., no prior information on mixing is exploited) and found that a source model accounting for local autocorrelation is able to increase robustness against noise, even space variant. Furthermore, when the model closely fits the source characteristics, independence is no longer a strict requirement, and cross-correlated sources can be separated, as well.

Finding correspondences between images by template matching is a common problem in image understanding. Although a variety of solutions have been proposed, most of them rely on the arbitrary choice of a template and a matching function. Often, different cost functions lead to different results, and the choice of a good cost for a specific application remains an art. Statistical models on the other hand, allow us to derive optimal learning and matching algorithms from modeling assumptions using likelihood maximization principles. The key contribution of this paper is the development of a statistical framework for learning what function to optimize from training examples. We present a family of statistical models for grayscale images, which allow us to derive optimal template-matching algorithms. The intensity at each pixel is described by a random variable whose distribution is encoded by a deformable template. Firstly, we assume the intensity distribution to be Gaussian and derive an intensity-matching algorithm, which is a generalization of the classical sum-of-squared differences. Then, we introduce a hidden segmentation variable in the probabilistic model and derive a segmentation-matching algorithm that can handle photometric variations. Both models are exemplified on the automatic detection of anatomical landmarks in brain Magnetic Resonance Images.

This paper highlights recent advances in image compression aided by 3-D geometry information. As two examples, we present a model-aided video coder for efficient compression of head-and-shoulder scenes and a geometry-aided coder for 4-D light fields for image-based rendering. Both examp les illustrate that an explicit representation of 3-D geometry is advintageous if many views of the same 3-D object or scene have to be encoded. Waveform-coding and 3-D model-based coding can be combined in a rate-distortion framework, such that the generality of waveform coding and the efficiency of 3-D models are available where needed.

We describe two broad classes of useful and physically meaningful image models that can be used to construct novel smoothing constraints for use in the regularized image restoration problem. The two classes, termed piecewise image models (PIM's) and local image models (LIM's), respectively, capture unique image properties that can be adapted to the image and that reflect structurally significant surface characteristics. Members of the PIM and LIM classes are easily formed into regularization operators that replace differential-type constraints. We also develop an adaptive strategy for selecting the best PIM or LIM for a given problem (from among the defined class), and we explain the construction of the corresponding regularization operators. Considerable attention is also given to determining the regularization parameter via a cross-validation technique, and also to the selection of an optimization strategy for solving the problem. Several results are provided that illustrate the processes of model selection, parameter selection, and image restoration. The overall approach provides a new viewpoint on the restoration problem through the use of new image models that capture salient image features that are not well represented through traditional approaches.

Computer vision attention processes assign variable hypothesized importance to different parts of the visual input and direct the allocation of computational resources. This non-uniform allocation might help accelerate the image analysis process. This paper proposes a new bottom-up attention mechanism. Rather than taking the traditional approach, which tries to model human attention, we propose a validated stochastic model to estimate the probability that an image part is of interest. We refer to this probability as saliency and thus specify saliency in a mathematically well-defined sense. The model quantifies several intuitive observations, such as the greater likelihood of correspondence between visually similar image regions and the likelihood that only a few of interesting objects will be present in the scene. The latter observation, which implies that such objects are (relaxed) global exceptions, replaces the traditional preference for local contrast. The algorithm starts with a rough pre-attentive segmentation and then uses a graphical model approximation to efficiently reveal which segments are more likely to be of interest. Experiments on natural scenes containing a variety of objects demonstrate the proposed method and show its advantages over previous approaches.