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  • 1
    Publication Date: 2011-10-20
    Description:    This paper presents a new approach to estimate two- and three-dimensional affine transformations from tomographic projections. Instead of estimating the deformation from the reconstructed data, we introduce a method which works directly in the projection domain, using parallel and fan beam projection geometries. We show that any affine deformation can be analytically compensated, and we develop an efficient multiscale estimation framework based on the normalized cross correlation. The accuracy of the approach is verified using simulated and experimental data, and we demonstrate that the new method needs less projection angles and has a much lower computational complexity as compared to approaches based on the standard reconstruction techniques. Content Type Journal Article Category Original Paper Pages 1-16 DOI 10.1007/s00138-011-0376-2 Authors René Mooser, Electronic/Metrology/Reliability Laboratory, Swiss Federal Laboratories for Materials Testing and Research (EMPA), Ueberlandstr. 129, 8600 Duebendorf, Switzerland Fredrik Forsberg, Division of Experimental Mechanics, Luleå University of Technology, 97187 Luleå, Sweden Erwin Hack, Electronic/Metrology/Reliability Laboratory, Swiss Federal Laboratories for Materials Testing and Research (EMPA), Ueberlandstr. 129, 8600 Duebendorf, Switzerland Gábor Székely, Computer Vision Laboratory, ETH Zurich, Sternwartstr. 7, 8092 Zurich, Switzerland Urs Sennhauser, Electronic/Metrology/Reliability Laboratory, Swiss Federal Laboratories for Materials Testing and Research (EMPA), Ueberlandstr. 129, 8600 Duebendorf, Switzerland Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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    Topics: Computer Science
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  • 2
    Publication Date: 2011-06-15
    Description:    In this paper, we have presented a new method for computing the best-fitted rectangle for closed regions using their boundary points. The vertices of the best-fitted rectangle are computed using a bisection method starting with the upper-estimated rectangle and the under-estimated rectangle. The vertices of the upper- and under-estimated rectangles are directly computed using closed-form solutions by solving for pairs of straight lines. Starting with these two rectangles, we solve for the best-fitted rectangle iteratively using a bisection method. The algorithm stops when the areas of the fitted rectangles remain unchanged during consecutive iterations. Extensive evaluation of our algorithm demonstrates its effectiveness. Content Type Journal Article Pages 1-9 DOI 10.1007/s00138-011-0348-6 Authors D. Chaudhuri, IAC, DEAL, Raipur Road, Dehradun, 248001 Uttarakhand, India N. K. Kushwaha, IAC, DEAL, Raipur Road, Dehradun, 248001 Uttarakhand, India I. Sharif, IAC, DEAL, Raipur Road, Dehradun, 248001 Uttarakhand, India A. Samal, Department of Computer Science and Engineering, University of Nebraska, Lincoln, NE 68588, USA Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 3
    Publication Date: 2011-06-15
    Description:    We propose using machine learning techniques to analyze the shape of living cells in phase-contrast microscopy images. Large scale studies of cell shape are needed to understand the response of cells to their environment. Manual analysis of thousands of microscopy images, however, is time-consuming and error-prone and necessitates automated tools. We show how a combination of shape-based and appearance-based features of fibroblast cells can be used to classify their morphological state, using the Adaboost algorithm. The classification accuracy of our method approaches the agreement between two expert observers. We also address the important issue of clutter mitigation by developing a machine learning approach to distinguish between clutter and cells in time-lapse microscopy image sequences. Content Type Journal Article Pages 1-15 DOI 10.1007/s00138-011-0345-9 Authors Diane H. Theriault, Department of Computer Science, Boston University, 111 Cummington St., Boston, MA 02215, USA Matthew L. Walker, Department of Biology, Boston University, 111 Cummington St., Boston, MA 02215, USA Joyce Y. Wong, Department of Biomedical Engineering, Boston University, 111 Cummington St., Boston, MA 02215, USA Margrit Betke, Department of Computer Science, Boston University, 111 Cummington St., Boston, MA 02215, USA Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 4
    Publication Date: 2011-06-21
    Description:    Humans decide how to carry out a spontaneous interaction with an object by using the whole geometric information obtained from their eyes. The aim of this paper is to present how our object representation model MWS (Adán in Comput Vis Image Underst 79:281–307, 2000 ) can help a robot manipulator to make a single and reliable interaction. The contribution of this paper is particularly focused on the grasp synthesis stage. The main idea is that the grasping system, through MWS, can use non-strict-local features of the contact points to find a consistent grasping configuration. The Direction Kernels (DK) concept, which is integrated into the MWS model, is used to define a set of candidate contact-points and interaction regions. The set of DK is a global feature which represents the principal normal vectors of the object and their relative weight in a three-connectivity mesh model. Our method calculates the optimal grasp points (which are ordered according to the quality function) for two-finger grippers, whilst maintaining the requirements of force closure and safety of the grasp. Our strategy has been extensively tested on real free-shape objects using a 6 DOF industrial robot. Content Type Journal Article Pages 1-20 DOI 10.1007/s00138-011-0351-y Authors Antonio Adán, Dpto Ingeniería E.E.A.C, Universidad de Castilla La Mancha, Ciudad Real, Spain Andrés S. Vázquez, Dpto Ingeniería E.E.A.C, Universidad de Castilla La Mancha, Ciudad Real, Spain Pilar Merchán, Escuela de ingenierías Industriales, Universidad de Extremadura, Badajoz, Spain Ruben Heradio, Dpto de Ingeniería de Software y Sistemas Informáticos, UNED, Madrid, Spain Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 5
    Publication Date: 2011-06-25
    Description:    This paper describes a novel system for mobile robot localization in an indoor environment, using concepts like homography and matching borrowed from the context of stereo and content-based image retrieval techniques (CBIR). To deal with variations with respect to viewpoint and camera positions, a group of points of interest (POI) is extracted to represent the image for robust matching. To cope with illumination changes, we propose to produce a contrast image for each video frame by using the root mean square strategy, thus all the POIs are extracted from the corresponding contrast images to provide perceptually consistent measurement of image content. To achieve effective image matching, modeling of robot behavior for model constrained matching is proposed, where normalized cross correlation is employed for local matching to determine corresponding POI pairs followed by homography based global optimization using RANSAC. Meanwhile, application of specific constraints also helps to exclude irrelevant frames in the training set to further improve the efficiency and robustness. The proposed approach has been successfully applied to the Robot Vision task for the ImageCLEF workshop, and the experimental results have fully demonstrated the high-quality performance of our approaches in terms of both precision and robustness. The system and approach outlined in this paper was ranked the second best in the optional task group in ImageCLEF 2009. In addition to demonstrating the merits of our approach in isolation, we also illustrate the benefits of our proposed approach in comparison with other submissions. Content Type Journal Article Pages 1-17 DOI 10.1007/s00138-011-0350-z Authors Yue Feng, Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, King’s College London, London, UK Jinchang Ren, Centre for excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow, G1 1XW UK Jianmin Jiang, Digital Media and Systems Research Institute, University of Bradford, Bradford, UK Martin Halvey, Glasgow Interactive Systems Group (GIST), University of Glasgow, Glasgow, UK Joemon M. Jose, Multimedia Information Retrieval Group, University of Glasgow, Glasgow, UK Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 6
    Publication Date: 2011-05-07
    Description:    The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com , provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs. Content Type Journal Article Pages 1-15 DOI 10.1007/s00138-011-0337-9 Authors Rehan Ali, Department of Radiation Physics, Stanford University, 875 Blake Wilbur Drive, CC-G206, Stanford, CA 94305, USA Mark Gooding, Mirada Medical Ltd, Innovation House, Mill Street, Oxford, OX2 0JX UK Tünde Szilágyi, Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision Lab, University of Oxford, Parks Road, Oxford, OX1 3PJ UK Borivoj Vojnovic, Gray Institute for Radiation Oncology and Biology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7QD UK Martin Christlieb, Gray Institute for Radiation Oncology and Biology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7QD UK Michael Brady, Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision Lab, University of Oxford, Parks Road, Oxford, OX1 3PJ UK Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 7
    Publication Date: 2011-05-05
    Description:    Lack of concentration in a driver due to fatigue is a major cause of road accidents. This paper investigates approaches that can be used to develop a video-based system to automatically detect driver fatigue and warn the driver, in order to prevent accidents. Ocular cues such as percentage eye closure (PERCLOS) are considered strong fatigue indicators; thus, accurately locating and tracking the driver’s eyes is vital. Tests were carried out based on two approaches to track the eyes and estimate PERCLOS: (1) classification approach and (2) optical flow approach. In the first approach, the eyes are tracked by finding local regions, the state (open or closed) of the eyes in each image frame is estimated using a classifier, and thereby the PERCLOS is calculated. In the second approach, the movement of the upper eyelid is tracked using a newly proposed simple eye model, which captures image velocities based on optical flow, thereby the eye closures and openings are detected, and then the eye states are estimated to calculate PERCLOS. Experiments show that both approaches can detect fatigue with reasonable accuracy, and that the classification approach is more accurate. However, the classification approach requires a large amount of suitable training data. If such data are unavailable, then the optical flow approach would be more practical. Content Type Journal Article Pages 1-22 DOI 10.1007/s00138-011-0321-4 Authors Rajinda Senaratne, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia Budi Jap, Department of Medical and Molecular Biosciences, University of Technology Sydney, Sydney, NSW 2007, Australia Sara Lal, Department of Medical and Molecular Biosciences, University of Technology Sydney, Sydney, NSW 2007, Australia Arthur Hsu, Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC 3050, Australia Saman Halgamuge, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia Peter Fischer, Signal Network Technology Pty Ltd, Lane Cove, Sydney, NSW 1595, Australia Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 8
    Publication Date: 2011-10-17
    Description:    In this paper, a new attempt has been made in the area of tool-based micromachining for automated, non-contact, and flexible prediction of quality responses such as average surface roughness ( R a ), tool wear ratio (TWR) and metal removal rate (MRR) of micro-turned miniaturized parts through a machine vision system (MVS) which is integrated with an adaptive neuro-fuzzy inference system (ANFIS). The images of machined surface grabbed by the MVS could be extracted using the algorithm developed in this work, to get the features of image texture [average gray level ( G a )]. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optimal optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process prediction and control. The main objective of this study is to design an ANFIS for estimation of R a , TWR, and MRR in micro-turning process. Cutting speed ( S ), feed rate ( F ), depth of cut ( D ), G a were taken as input parameters and R a , TWR, MRR as the output parameters. The results obtained from the ANFIS model were compared with experimental values. It is found that the predicted values of the responses are in good agreement with the experimental values. Content Type Journal Article Category Original Paper Pages 1-14 DOI 10.1007/s00138-011-0378-0 Authors S. Palani, Department of Mechanical Engineering, Mount Zion College of Engineering and Technology, Pudukkottai, Tamilnadu, India U. Natarajan, Department of Mechanical Engineering, A.C. College of Engineering and Technology, Karaikudi, Tamilnadu, India M. Chellamalai, Department of Micro and Precision Machining, Central Manufacturing Technology Institute, Bangalore, India Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 9
    Publication Date: 2011-10-13
    Description:    In this paper, the technique of saliency detection is proposed to model people’s biological ability of attending to their interest. There are two phases in the scheme of intelligent saliency searching: saliency filtering and saliency refinement. In saliency filtering, non-salient regions of a scene image are filtered out by measuring information entropy and biological color sensitivity. The information entropy evaluates the level of knowledge and energy contained, and the color sensitivity measures biological stimulation of a presented scene. In saliency refinement, candidate salient regions obtained are cultivated for a good representation of saliency by extracting salient objects, similarly to people’s manner of perception. The performance of the proposed technique is studied on noiseless and noisy natural scenes and evaluated with eye fixation data. The evaluation proved the effectiveness of the approach in discovering salient regions or objects from scene images. The performance of addressing transformation and illumination variance is also investigated. Content Type Journal Article Category Original Paper Pages 1-14 DOI 10.1007/s00138-011-0372-6 Authors Shuzhi Sam Ge, Social Robotics Lab, Interactive Digital Media Institute and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore Hongsheng He, Social Robotics Lab, Interactive Digital Media Institute and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore Zhengchen Zhang, Social Robotics Lab, Interactive Digital Media Institute and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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  • 10
    Publication Date: 2011-10-13
    Description:    Early prediction of natural disasters like floods and landslides is essential for reasons of public safety. This can be attained by processing Synthetic-Aperture Radar (SAR) images and retrieving soil-moisture parameters. In this article, TerraSAR-X product images are investigated in combination with a water-cloud model based on the Shi semi-empirical model to determine the accuracy of soil-moisture parameter retrieval. SAR images were captured between January 2008 and September 2010 in the vicinity of the city Maribor, Slovenia, at different incidence angles. The water-cloud model provides acceptable estimated soil-moisture parameters at bare or scarcely vegetated soil areas. However, this model is too sensitive to speckle noise; therefore, a pre-processing step for speckle-noise reduction is carried out. Afterwards, self-organizing neural networks (SOM) are used to segment the areas at which the performance of this model is poor, and at the same time neural networks are also used for a more accurate approximation of model parameters’ values. Ground-truth is measured using the Pico64 sensor located on the field, simultaneously with capturing SAR images, in order to enable the comparison and validation of the obtained results. Experimental results show that the proposed method outperforms the water-cloud model accuracy over all incidence angles. Content Type Journal Article Category Original Paper Pages 1-16 DOI 10.1007/s00138-011-0375-3 Authors Matej Kseneman, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia Dušan Gleich, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia Božidar Potočnik, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia Journal Machine Vision and Applications Online ISSN 1432-1769 Print ISSN 0932-8092
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