Vol. 19 (2010): Abstracts of Papers
Wang X.-Y., Sun Y.-F.:
A Color- and Texture-based Image Segmentation Algorithm.
MGV vol. 19, no. 1, 2010, pp. 3-18.
Image segmentation is a classic inverse problem which consists in obtaining a compact, region-based description of the image scene by decomposing it into meaningful or spatially coherent regions sharing similar attributes. Because a color image can provide more perceptual information, color image segmentation is being paid more and more attention. In this paper, we propose a new approach to color image segmentation that is based on low-level features of color and texture. The approach is aimed at segmentation of natural scenes where the color and texture of each segment do not typically exhibit uniform statistical characteristics. Firstly, local color composition is described in terms of spatially adaptive dominant colors by using the Gibbs random field, and the color image is segmented into regions according to the local color composition. Secondly, the texture characteristics of the grayscale component are described by utilizing the Steerable filter, and the grayscale component of color image is cut into flat regions and non-flat regions. Thirdly, the local color composition and texture characteristics are combined to obtain an overall crude segmentation. Finally, an elaborate border refinement procedure is used to obtain accurate and precise border localization by appropriately combining color-texture features with the Normalized Cuts. The experimental results demonstrate that the color image segmentation results of the proposed approach exhibit favorable consistency in terms of human perception.
Key words: Image segmentation, Gibbs random field, Steerable filter, Normalized Cuts.
Youssef B. A.:
Image Segmentation Using Streamlines Analogy.
MGV vol. 19, no. 1, 2010, pp. 19-31.
This paper presents a novel method for digital image segmentation based on the analogy between streamlines in fluid dynamics and isophote lines in digital images. The segmentation problem is reformulated so that the image intensity corresponds to the stream function for a two-dimensional, incompressible flow, and image intensity gradients are represented as the fluid velocity vector. Segmentation is effected by computing the streamlines by solving a coupled system of ordinary differential equations using a fourth-order Runge-Kutta method. Selection of the initial starting point for segmentation is based on color homogeneity in terms of local color gradient, and on variance. The effectiveness of the developed segmentation method is demonstrated through a number of case studies, ranging from gray level to colored images.
Key words: Image segmentation, streamlines, stream function.
Roy K., Bhattacharya P.:
Iris Recognition Using Genetic Algorithms and Asymmetrical SVMs.
MGV vol. 19, no. 1, 2010, pp. 33-62.
With the increasing demand for enhanced security, iris biometrics-based personal identification has become an interesting research topic in the field of pattern recognition. While most state-of-the-art iris recognition algorithms are focused on preprocessing iris images, important new directions have been identified recently in iris biometrics research. These include optimal feature selection and iris pattern classification. In this paper, we propose an iris recognition scheme based on Genetic Algorithms (GAs) and asymmetrical Support Vector Machines (SVMs). Instead of using the whole iris region, we elicit the iris information between the collarette and the pupillary boundaries to suppress effects of eyelids and eyelashes occlusions, and pupil dilation, and to minimize the matching error. To select the optimal feature subset together with increasing the overall recognition accuracy, we apply GAs with a new fitness function. The traditional SVMs are modified into asymmetrical SVMs to handle: (1) highly unbalanced sample proportion between two classes, and 2) different types of misclassification error that lead to different misclassification losses. Furthermore, the parameters of SVMs are optimized in order to improve the generalization performance. The proposed technique is computationally effective, with recognition rates of 97.80% and 95.70% on the Iris Challenge Evaluation (ICE) and the West Virginia University (WVU) iris datasets, respectively.
Key words: Biometrics, iris recognition, asymmetrical support vector machines, collarette area localization, genetic algorithms.
Surfaces Filling Polygonal Holes with G1 Quasi G2 Continuity.
MGV vol. 19, no. 1, 2010, pp. 63-96.
Two constructions of surfaces filling polygonal holes in piecewise B-spline bicubic surfaces with tangent plane continuity are described. The filling surfaces are obtained by minimization of functionals which impose penalty on curvature discontinuities. One of the functionals is a quadratic form, while the other functional is defined with a parameterization-independent formula. The resulting surfaces may be used instead of class G2 surfaces in practical applications; the penalty approach enables simplification of the construction, and reduction in the degree of patches filling the hole from (9,9) to (5,5) without any visible quality degradation. The notion of class Gn quasi Gm surfaces, i.e. class Gn surfaces optimized to approximate class Gm surfaces, is proposed.
Key words: Filling polygonal holes, tangent plane continuity, curvature continuity, compatibility conditions, surface shape optimization, Ritz method.
Liu S., Li J.:
Preserving Zeros in Surface Construction using Marching Cubes.
MGV vol. 19, no. 1, 2010, pp. 97-123.
In surface construction, existing marching cubes (MC) methods require sample values at cell vertices to be non-zero after thresholding, or modify them otherwise. The modification may introduce problems in the constructed surface, such as topological changes, representation errors, and preference for positive or negative values. This paper presents a generalized MC algorithm. It constructs surface patches by exploiting cycles in cells without changing the sample values at vertices, and thus allows cell vertices with zero sample values to lie on the constructed surface. The simulation results show that the proposed Zero-Crossing MC method preserves better topologies of implicit surfaces that pass through cell vertices, and represents the surfaces more accurately. Its efficiency is comparable to existing MC methods in constructing surfaces.
Key words: Surface construction, Marching cubes, Zero-crossing.
Fast Object Detection Using Steiner Tree.
MGV vol. 19, no. 2, 2010, pp. 127-142.
We propose an approach to speed-up object detection, with an emphasis on settings where multiple object classes are detected. Our method uses a segmentation algorithm to select a small number of image regions on which to run a classifier. Compared to the classical sliding window approach, a significantly smaller number of rectangles is examined, which yields significantly faster object detection. Further, in the multiple object class setting, we show that the computational cost of segmentations can be amortized across objects classes, resulting in an additional speedup. At the heart of our approach is reduction to a directed Steiner tree optimization problem, which we solve approximately in order to select the segmentation algorithm parameters. The solution gives a small set of segmentation strategies that can be shared across object classes. Compared to the sliding window approach, our method results in two orders of magnitude fewer regions considered, and significant (10-15x) computational time speedups on challenging object detection datasets (LabelMe and StreetScenes) while maintaining comparable detection accuracy.
Key words: Object, Detection, Recognition, Steiner tree.
Own H. S.:
Improvement in Image Denoising Technique Based on Dual -Tree Wavelet Transform and Multiresolution Local Contrast Entropy.
MGV vol. 19, no. 2, 2010, pp. 143-157.
The paper proposes an improvement in image denoising using Dual-Tree Complex Wavelet Transform (DT-CWT). Depending on the probability distribution of the noise in the wavelet coefficients, an adaptive threshold estimation algorithm is introduced. The threshold enables the proposed algorithm to adapt to unknown smoothness of the noisy images. The algorithm looks at the local contrast entropy of a complex wavelet coefficient instead of its magnitude in order to remove the noise from the image. Simulation results show improved performance of our image denoising method compared to other popular denoising algorithms, such as VisuShrink, Wiener2, ProbShrink, and our previous work based on DWT.
Key words: Image Denoising, Dual Tree Complex Wavelet, Multiresolution Local Contrast Entropy.
Gedda M., Öfverstedt L.-G., Skoglund U., Svensson S.:
Image Processing System for Localising Macromolecules in Cryo-Electron Tomography.
MGV vol. 19, no. 2, 2010, pp. 159-184.
A major challenge in today's molecular biology research is to understand the interaction between proteins at the molecular level. Cryo-electron tomography (ET) has come to play an important role in facilitating objective qualitative experiments on protein structures and their interaction. Various protein conformation structures can be qualitatively analysed as complete galleries of proteins are captured by ET. To facilitate fast and objective macromolecular structure analysis procedures, image processing has become a crucial tool. This paper presents an image processing system for localising individual proteins from in vitro samples imaged by ET. We have evaluated the system using simulated data as well as experimental data.
Key words: Fuzzy set, watershed segmentation, distance transform, proteins.
Smietanski J., Tadeusiewicz R.:
Discriminatory Power of Co-Occurrence Features in Perfusion CT Prostate Images.
MGV vol. 19, no. 2, 2010, pp. 185-199.
This paper presents an algorithm used to improve the effectiveness of early prostate cancer (PCa)detection. The necessity for using such a computational method lies in the fact that although perfusion computed tomography (p-CT) is considered a good technique for the detection of early PCa, the p-CT prostate images are very difficult to interpret manually by radiologists.
We hereby propose a methodology for computational analysis of p-CT prostate images based on textural coefficients derived from co-occurrence matrices and their 21 coefficients. The selection of only a few of the considered features ensures the necessary balance between matching set of already known images and new, not yet clear cases.
The proposed algorithm for automatic differentiation of the healthy area of the image from the cancerous region was tested on a set of 59 prostate images. Although the results were not entirely satisfactory (86% correct recognitions), this method may be considered as the base for the development of a better algorithm.
Key words: prostate cancer, perfusion computed tomography, medical image analysis, pattern recognition.
Wood B.A., Lee J.K., Maskey M., Newman T.S.:
Higher Order Approximating Normals and Their Impact on Isosurface Shading Accuracy.
MGV vol. 19, no. 2, 2010, pp. 201-221.
Two alternatives to the standard (central differencing) method for estimating normals of Marching Cubes isosurfaces are considered. The methods are based on higher order approximations of dataset gradients. Of primary concern here are the effects of these methods on rendering quality, which is evaluated here through pixel-by-pixel comparisons of typical-fidelity isosurfaces versus high-fidelity rendering achievable from analytically derived formulae. The evaluations also consider effects of noise on isosurface rendering quality for renderings based on standard versus higher order gradients.
Key words: Volume Visualization, Isosurfaces, Marching Cubes, Shading & Illumination.
Kit D.L.H., Suandi S.A.:
Reconstruction of 3D Surface From 2D Images Using Five Lighting Sources.
MGV vol. 19, no. 2, 2010, pp. 223-242.
This paper proposes a novel method for reconstructing and acquiring 3D geometry information on an inspected surface based on 2D images. In this research, multiple light sources and a single camera setup are used to capture several 2D images in order to reconstruct the a 3D surface model. The approach consists of two major stages to obtain the 3D geometry information: (1) the shade of the images will be used to get the surface gradient which will finally be used to construct the surface gradient map, and (2) the shadow of the object will be approximated in order to reconstruct the step height (edge) of the object.
Key words: Surface gradient mapping, shadow correction, five light sources illumination setting.
Special Issue on Image Databases
Special Issue Editor: J.L. Kulikowski.
Nikos Papadakis, Nikos Doulamis and Anastasios Doulamis:
Hierarchical Graph-Based Media Content Representation for Real Time Search in Large Scale Multmedia Databases.
MGV vol. 19, no. 3, 2010, pp. 247-263.
This paper presents a system architecture and the appropriate algorithms for confidential searching of digital multimedia libraries. The proposed scheme uses the Middleware service layer that allows pre-processing of raw content with the technology owned by the Search Engine, without compromising the security of the original architecture in any way. The specific search algorithms described are a hierarchical graph structure algorithm for preprocessing, and a backtracking search algorithm that achieves good real-time performance (speed, and precision-recall values) under the given security constraints.
Key words: Multimedia Databases, image process, computer vision.
A. Lisowska, T. Kaczmarzyk:
JCURVE --- Multiscale Curve Coding via Second Order Beamlets.
MGV vol. 19, no. 3, 2010, pp. 265-281.
The paper presents an algorithm JCURVE for compression of binary images with linear or curvilinear features, which is a kind of generalization of the JBEAM coder. The proposed algorithm is based on second order beamlet representation, where second order beamlets are defined as hierarchically organized segments of conic curves. The algorithm can compress images in both a lossy and losless way, and it is also progressive. The experiments performed on benchmark images have shown that the proposed algorithm significantly outperforms the known JBIG2 standard and the base JBEAM algorithm both in losless and lossy compression. It is characterized, additionally, by the same time complexity as JBEAM, namely O(N2log2 N) for image of size N × N pixels.
Key words: Image compression, second order beamlets, curve coding.
W.W. Koczkodaj, A. Przelaskowski and K.T. Szopinski:
Medical Knowledge Mining from Image Data – Synthesis of Medical Image Assessments for Early Stroke Detection.
MGV vol. 19, no. 2, 2010, pp. 283-.
The key issue of this study is synthesis of medical images and expert knowledge for early detection of a medical condition, such as stroke or cancer. Such synthesis is a missing link for making decisions during the diagnostic process. Knowledge mining in image databases can be enhanced by computing the relative importance of image features using pairwise comparisons. Computed weights can be systematically used for synthesis of various image features present in the same or different images.
Key words: knowledge mining, image data, pairwise comparisons, inconsistency analysis, early stroke detection.
Kulikowski J.L., Przytulska M.:
Visual Retrieval of Documents Based on Their Multi-Aspect Utility Assessment.
MGV vol. 19, no. 3, 2010, pp. 299-320.
Differences between textual and visual documents retrieval problems are described. It is shown that retrieval of visual documents in experimental data bases requires assessment of image utility and taking it into consideration. A definition of multi-aspect image usefulness measure is proposed. A multi-aspect measure of similarity of images based on their quantitative and/or qualitative features is also proposed. The general concept is illustrated by examples of using morphological spectra as a source of parameters useful in the assessing similarity of some classes of biomedical images. The basic structure and properties of an Image Analysis and Selection System (IASS) are presented as an example of practical realization of the visual documents retrieval methods.
Key words: visual documents retrieval, image utility measure, image similarity measure, morphological spectra, analysis of textures.
Pietka B.D., Dulewicz A., Jaszczak P., Kupis P.:
Application of a Pathomorphological Image Database in Computer-Aided Cytological Examinations.
MGV vol. 19, no. 3, 2010, pp. 321-337.
The paper deals with the problem of early detection of bladder cancer based on non-invasive, voided urine cytological investigations. In spite of the diagnostic potential of the method for discovering malignancy associated changes in cells before they start to form a tumour, cytological tests seem to be underestimated by physicians, as there is a common view that their sensitivity, especially in early stages of the cancer, is relatively low. We depict here just one, but significant, direction of our work that aims to support the cytopathologist in making the diagnosis more accurate and reliable. The key idea relies on classification of adaptive smear objects by searching for similar patterns in a flexible pathomorphological image database using content-based image retrieval technology (CBIR).
Key words: bladder cancer, digital cytology, image processing, content-based image retrieval.
Laganiére R., Kangni F.:
Orientation and Pose estimation of Panoramic Imagery.
MGV vol. 19, no. 3, 2010, pp. 339-363.
In a database of geo-referenced images, determining the exact position of each panorama is an important step in order to ensure the consistency of visual information. This paper addresses the problem of camera pose recovery from spherical (360o) panoramas. The 3D information is extracted from a database of panoramic images sparsely distributed over a scene of interest. We present a two-stage algorithm to recover the position of omni-directional cameras using pair wise essential matrices. First, all rotations with respect to the world frame are found using an incremental bundle adjustment procedure, thus achieving what we call panorama alignment. Full camera positions are then computed using bundle adjustment. During this step, the previously computed panorama orientations, used to feed the global optimization process, can be further refined. Results are shown for indoor and outdoor panorama sets.
Key words: omni-directional panorama; pose estimation; bundle adjustment; structure from motion.
Karasulu B., Balli S.:
Image Segmentation Using Fuzzy Logic, Neural Networks and Genetic Algorithms: Survey and Trends.
MGV vol. 19, no. 4, 2010, pp. 367-409.
Image segmentation is a fundamental process employed in many applications of pattern recognition, video analysis, computer vision and image understanding in order to allow further image content exploitation in an efficient way. It is often used to partition an image into separate regions. As recent trends in image segmentation show, the use of artificial and/or computational intelligence (AI and/or CI) techniques has become more popular as an alternative to the conventional techniques. In this paper, we present an extensive and comprehensive review of the image processing area for advanced researchers. This study introduces the theoretical fundamentals of image segmentation using AI and/or CI techniques based on fuzzy logic (FL), genetic algorithm (GA) and artificial neural networks (ANN). Besides, this survey examines the applications of these techniques in different image segmentation areas. In the literature, these techniques are used as an interpretation tool for segmentation. In our study, these tools are focused on because of their capabilities, such as robustness, segmentation accuracy and low computational costs. Moreover, we review 56 remarkable studies from the last decade (i.e., the years between 2001 and 2010), which involve different image segmentation approaches using FL, GAs, ANNs and hybrid intelligent systems (HISs). In our state-of-the-art survey, the comparison of the reviewed papers in related categories is made based on both the corresponding properties of segmentation as well as performance evaluation of the related method proposed in a given reviewed paper. The results and recent trends are also discussed.
Key words: image segmentation, neural networks, fuzzy logic, genetic algorithms, clustering.
Pawar V. N., Talbar S. N.:
Hybrid Machine Learning Approach for Object Recognition: Fusion of Features and Decisions.
MGV vol. 19, no. 4, 2010, pp. 411-428.
Object recognition is considered to be a predominant basic issue in computer vision. It is a challenging issue against inconsistent illumination, partial occlusion, changing background and shifting viewpoint, because considerable variations are exhibited by diversified real world patterns. The virtue of feature fusion lies in its reliability and capability for object recognition in terms of actual redundancy and complementary information. In this paper, we have developed an efficient hybrid approach using scale invariant features and machine learning techniques for object recognition. We extract the scale invariant features, namely color, shape and texture of the objects, separately with the aid of suitable feature extraction techniques. Then, we integrate the color, shape and texture features of the objects at the feature level, so as to improve the recognition performance. The fused feature set serves as a pattern for the forthcoming processes involved in the developed approach. Subsequently, we hybridize the process of object recognition by combining the pattern recognition algorithms like Support Vector Machine, Discriminant Canonical Correlation, and Locality Preserving Projections. Obviously, with three different pattern recognition algorithms employed, we are likely to get three distinct or identical results enumbered with false positives. So in order to reduce the number of false positives, we devise a decision module based on Neural Networks that takes in the match percentage from the chosen pattern recognition algorithms, and then decides the recognition result based on those match values. Our approach is evaluated on the Amsterdam Library of Object Images collection, a large collection of object images containing 1000 objects recorded under various imaging circumstances. The experimental results clearly demonstrate that our approach significantly outperforms the state-of-the-art methods for combining color, shape and texture features. The developed method is shown to be effective under a wide variety of imaging conditions. Finally, we employ empirical evaluation to evaluate our approach with the aid of an accuracy estimation method, such as k-fold cross validation.
Key words: Object Recognition, Feature Extraction, Support Vector Machine (SVM), Discriminant Canonical Correlation (DCC), Locality Preserving Projections (LPP), Neural Network (NN).
Koprowski R., Wrobel Z., Zieleznik W.:
Ultrasound Image Analysis in Hashimoto's Disease.
MGV vol. 19, no. 4, 2010, pp. 429-437.
The paper presents the diagnostics of parenchyma echogenicity and organ dimensions in thyroid examinations in case of the Hashimoto's disease using image processing methods. In case of discovering focal changes within the thyroid, a method for their pathology evaluation is suggested. The detector proposed operates fully automatically; using the information on the image texture it detects an artery in the image, which plays the role of a reference point, and based on it – detects the area of interest. }
Key words: USG, image processing, Hashimoto.
Sharma A., Sharma R.K., Kumar R.:
HMM-based Online Handwritten Gurmukhi Character Recognition.
MGV vol. 19, no. 4, 2010, pp. 439-449.
This paper presents a hidden Markov model-based online handwritten character recognition for Gurmukhi script. We discuss a procedure to develop a hidden Markov model database in order to recognize Gurmukhi characters. A test with 60 handwritten samples, where each sample includes 41 Gurmukhi characters, shows a 91.95\% recognition rate, and an average recognition speed of 0.112 seconds per stroke. The hidden Markov model database has been developed in XML using 5330 Gurmukhi characters. This work shall be useful to implement a hidden Markov model in online handwriting recognition and its software development.
Key words: Online handwriting recognition, Preprocessing, Feature extraction, Hidden Markov models.
Ying J., Zhang X.:
A Double-Circle Algorithm for Ore Classification.
MGV vol. 19, no. 4, 2010, pp. 451-462.
This paper proposes a double-circle algorithm to classify ore stockpiles according to their particle size distribution. The algorithm is particularly suitable for yard automation systems in large iron and steel works, since its result can be used directly as a reliable basis for stackers and reclaimers controller. The paper explains the concept as well as related method, which consists of four steps to detect ore granularity and classes. A series of experiments in industrial environments proved that this novel algorithm improves the reliability of ore classifiers, compared with the classic ones.
Key words: Particle Size Distribution, Ore Classification, Morphologic Granulation, Planar Subdivision, Double-circle, Computer Vision.
Huang F., Torii A., Klette R.:
Geometries of Panoramic Images and 3D Vision.
MGV vol. 19, no. 4, 2010, pp. 463-477.
Over the recent years, the image sensor technology has provided tools for wide-angle and high-resolution 3D recording, analysis and modeling of static or dynamic scenes, ranging from small objects, such as artifacts in a museum, to large-scale 3D models of castles or 3D city maps, also allowing real time 3D data acquisition from a moving platform, e.g. in vision-based driver assistance. More recently, due to the rapidly evolving and improving stereoscopic display technology, many of these panoramic image applications have started to contribute to stereo visualization, thus increasing realistic and immersive appearances. This paper introduces a methodology for stereo panorama acquisition and provides detailed technologies of mapping between different forms of panoramic images. Image examples illustrate the potential for projects in arts, science and technology.
Key words: Panoramic imaging, stereo panorama imaging, omnidirectional viewing.
New Books Notes
MGV vol. 19, no. 4, 2010, pp. 479-480. Katarzyna Stapor: Pattern classification methods in computer vision (in Polish).
Published by Wydawnictwo Naukowe PWN, Warszawa 2011.
New Books Notes
MGV vol. 19, no. 4, 2010, pp. 481-482. Wojciech S. Mokrzycki: Introduction to Visual Information Processing. I. Perception, Acquisition, Visualization (in Polish).
Published by AOW EXIT, Warsaw 2010.