Recaptured photo detection using specularity distribution

Detection of planar surfaces in a generic scene is difficult when the illumination is complex and less intense, and the surfaces have non-uniform colors (e.g., a movie poster). As a result, the specularity, if appears, is superimposed with the surface color pattern, and hence the observation of uniform specularity is no longer sufficient for identifying planar sur-faces in a generic scene as it does under a distant point light source. In this paper, we address the problem of planar sur-face recognition in a single generic-scene image. In partic-ular, we study the problem of recaptured photo recognition as an application in image forensics. We discover that the specularity of a recaptured photo is modulated by the micro-structure of the photo surface, and its spatial distribution can be used for differentiating recaptured photos from the origi-nal photos. We validate our findings in real images of generic scenes. Experimental results show that there is a distinguish-able feature of natural scene and recaptured images. Given the definition of specular ratio as the percentage of specularity in the overall measured intensity, the distribution of specular ra-tio image’s gradient of natural images is Laplacian-like while that of recaptured images is Rayleigh-like. Index Terms — Image forensics, recaptured photo detec-tion, dichromatic reflectance model, specularity 1

Influence of a discontinuity on the spectral and fractal analysis of one-dimensional data

The analysis of a data area or segment containing steep transitions between regions with different textures (for example a cloud and its background) leads to addressing the problem of discontinuities and their impact on texture analysis. In that purpose, an original one-dimensional analytical model of spectrum and roughness function has been worked out, with a discontinuity between two fractal regions, each one specified by its average &micro;, standard deviation &sigma;, spectral index &beta; and Hurst exponent <i>H</i>. This has the advantage of not needing the generation of a fractal structure with a particular algorithm or random functions and clearly puts into evidence the role played by the average in generating spectral poles and side lobes. After validation of the model calibration, a parametric study is carried out in order to understand the influence of this discontinuity on the estimation of the spectral index &beta; and the Hurst parameter <i>H</i>. It shows that for a pure &micro;-gap, <i>H</i> is well estimated everywhere, though overestimated, and &beta; is overestimated in the anti-correlation range and saturates in the correlation range. For a pure &sigma;-gap the retrieval of <i>H</i> is excellent everywhere and the behaviour of &beta; is better than for a &micro;-gap, leading to less overestimation in the anti-correlation range. For a pure &beta;-gap, saturation degrades measurements in the case of raw data and the medium with smaller spectral index is predominant in the case of trend-corrected data. For a pure <i>H</i>-gap, there is also dominance of the medium with smaller fractal exponent

Multi-texture image segmentation

Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification

Models for Motion Perception

As observers move through the environment or shift their direction of gaze, the world moves past them. In addition, there may be objects that are moving differently from the static background, either rigid-body motions or nonrigid (e.g., turbulent) ones. This dissertation discusses several models for motion perception. The models rely on first measuring motion energy, a multi-resolution representation of motion information extracted from image sequences. The image flow model combines the outputs of a set of spatiotemporal motion-energy filters to estimate image velocity, consonant with current views regarding the neurophysiology and psychophysics of motion perception. A parallel implementation computes a distributed representation of image velocity that encodes both a velocity estimate and the uncertainty in that estimate. In addition, a numerical measure of image-flow uncertainty is derived. The egomotion model poses the detection of moving objects and the recovery of depth from motion as sensor fusion problems that necessitate combining information from different sensors in the presence of noise and uncertainty. Image sequences are segmented by finding image regions corresponding to entire objects that are moving differently from the stationary background. The turbulent flow model utilizes a fractal-based model of turbulence, and estimates the fractal scaling parameter of fractal image sequences from the outputs of motion-energy filters. Some preliminary results demonstrate the model\u27s potential for discriminating image regions based on fractal scaling

Color graph representation for structural analysis of tissue images

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 71-82.Computer aided image analysis tools are becoming increasingly important in automated cancer diagnosis and grading. They have the potential of assisting pathologists in histopathological examination of tissues, which may lead to a considerable amount of subjectivity. These analysis tools help reduce the subjectivity, providing quantitative information about tissues. In literature, it has been proposed to implement such computational tools using different methods that represent a tissue with different set of image features. One of the most commonly used methods is the structural method that represents a tissue quantifying the spatial relationship of its components. Although previous structural methods lead to promising results for different tissue types, they only use the spatial relations of nuclear tissue components without considering the existence of different components in a tissue. However, additional information that could be obtained from other components of the tissue has an importance in better representing the tissue, and thus, in making more reliable decisions. This thesis introduces a novel structural method to quantify histopathological images for automated cancer diagnosis and grading. Unlike the previous structural methods, it proposes to represent a tissue considering the spatial distribution of different tissue components. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their end points. Subsequently, a new set of structural features is extracted from these “color graphs” and used in the classification of tissues. Experiments conducted on 3236 photomicrographs of colon tissues that are taken from 258 different patients demonstrate that the color graph approach leads to 94.89 percent training accuracy and 88.63 percent test accuracy. Our experiments also show that the introduction of color edges to represent the spatial relationship of different tissue components and the use of graph features defined on these color edges significantly improve the classification accuracy of the previous structural methods.Altunbay, DoğanM.S

Fractal Analysis of Microstructural and Fractograpghic Images for Evaluation of Materials

Materials have hierarchically organized complex structures at different length scales. Quantitative description of material behaviour is dependent on four fundamental length scales [1], which are of concern to materials scientists. These are (1) nano scale, 1-103 nm, (2)micro scale, 1-10 3 μm, (3) macro scale, 1-103mm, and (4) global size scale, 1-106 m. While the nano scale corresponds to, often, highly ordered atomic structures, the global size scale relates geophysical phenomena and large man made engineering structures. Micro scale and macro scale correspond to size of material samples used in laboratories, for designing and for fabrication of miniature to small machineries

Image understanding and feature extraction for applications in industry and mapping

Bibliography: p. 212-220.The aim of digital photogrammetry is the automated extraction and classification of the three dimensional information of a scene from a number of images. Existing photogrammetric systems are semi-automatic requiring manual editing and control, and have very limited domains of application so that image understanding capabilities are left to the user. Among the most important steps in a fully integrated system are the extraction of features suitable for matching, the establishment of the correspondence between matching points and object classification. The following study attempts to explore the applicability of pattern recognition concepts in conjunction with existing area-based methods, feature-based techniques and other approaches used in computer vision in order to increase the level of automation and as a general alternative and addition to existing methods. As an illustration of the pattern recognition approach examples of industrial applications are given. The underlying method is then extended to the identification of objects in aerial images of urban scenes and to the location of targets in close-range photogrammetric applications. Various moment-based techniques are considered as pattern classifiers including geometric invariant moments, Legendre moments, Zernike moments and pseudo-Zernike moments. Two-dimensional Fourier transforms are also considered as pattern classifiers. The suitability of these techniques is assessed. These are then applied as object locators and as feature extractors or interest operators. Additionally the use of fractal dimension to segment natural scenes for regional classification in order to limit the search space for particular objects is considered. The pattern recognition techniques require considerable preprocessing of images. The various image processing techniques required are explained where needed. Extracted feature points are matched using relaxation based techniques in conjunction with area-based methods to 'obtain subpixel accuracy. A subpixel pattern recognition based method is also proposed and an investigation into improved area-based subpixel matching methods is undertaken. An algorithm for determining relative orientation parameters incorporating the epipolar line constraint is investigated and compared with a standard relative orientation algorithm. In conclusion a basic system that can be automated based on some novel techniques in conjunction with existing methods is described and implemented in a mapping application. This system could be largely automated with suitably powerful computers