Line Based Camera Calibration In Machine Vision Dynamic Applications

The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.10210010

The spiderweb protocluster is being magnetized by its central radio jet

Large scale structure and cosmolog

X-ray emission from the jets and lobes of the spiderweb

Galaxie

Cosmological parameters from Galaxy Clusters: an Introduction

This lecture is an introduction to cosmological tests with clusters of galaxies. Here I do not intend to provide a complete review of the subject, but rather to describe the basic procedures to set up the fitting machinery to constrain cosmological parameters from clusters, and to show how to handle data with a critical insight. I will focus mainly on the properties of X-ray clusters of galaxies, showing their success as cosmological tools, to end up discussing the complex thermodynamics of the diffuse intracluster medium and its impact on the cosmological tests.Comment: 32 pages, 16 figures, conference proceedings for the 3rd Aegean Summer School, Chios, 26 September - 1 October, 200

Prediction Of Protein-protein Binding Hot Spots: A Combination Of Classifiers Approach

In this work we approach the problem of predicting protein binding hot spot residues through a combination of classifiers. We consider a comprehensive set of structural and chemical properties reported in the literature for characterizing hot spot residues. Each component classifier considers a specific set of properties as feature set and their output are combined by the mean rule. The proposed combination of classifiers achieved a performance of 56.6%, measured by the F-Measure with corresponding Recall of 72.2% and Precision of 46.6%. This performance is higher than those reported by Darnel et al. [4] for the same data set, when compared through a t-test with a significance level of 5%. © 2008 Springer-Verlag Berlin Heidelberg.5167 LNBI165168Apweiler, R., Bairoch, A., Wu, C.H., UniProt: The Universal Protein Knowl-edgebase (2004) Nucl. Acid. Res, 32, pp. D115-D119Arkin, M.R., Wells, J.A., Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing Towards the Dream (2004) Nature Reviews, Drug Discovery, 3, pp. 301-317Bussab, W.O., Morettin, P.A., (2002) Basic Statistics, , 5th edn. Editora Saraiva, São Paulo, Brazil , in PortugueseDarnell, S.J., Page, D., Mitchell, J.C., An Automated Decision-Tree Approach to Predicting Protein Interaction Hot Spots (2007) Proteins, 68, pp. 813-823Duin, R.P.W., Juszczak, P., Paclik, P., (2004) PRTools4, a Matlab Toolbox for Pattern Recognition, , Delft University of TechnologyHagerty, C.G., Munchnik, I., Kulikowski, C., Two Indices Can Approximate Four Hundred and Two Amino Acid Properties (1999) Proc. of 1999 IEEE Int. Simp. Intell. Control./Intell. Syst. and Semiotics, pp. 365-369. , Cambridge, MA, ppKortemme, T., Baker, D., A Simple Physical Model for Binding Energy Hot Spots in Protein-Protein Complexes (2002) Proc. Natl. Acad. Sci. USA, 99, pp. 14116-14121Liang, J., Edelsbrunner, H., Fu, P., Sudhakar, P.V., Analytical Shape Computation of Macromolecules: I. Molecular Area and Volume through Alpha Shape (1998) Proteins, 33, pp. 1-17Mancini, A.L., Higa, R.H., Oliveira, A., STING Contacts: A Web-Based application for Identification and Analysis of Amino Acid Contacts within Protein Structure and Across Protein Interfaces (2004) Bioinformatics, 20 (13), pp. 2145-2147Moreira, I.S., Fernandes, P.A., Ramos, M.J., Hot Spots - A Review of the Protein-Protein Interface Determinant Amino-Acid Residues (2007) Proteins, 68, pp. 803-812Pupko, R., Bell, R.E., Mayrose, I., Rate4Site: An Algorithmic Tool for the Identification of Functional Regions in Proteins by Surface Mapping of Evolutionary Determinants Within Their Homologues (2002) Bioinformatics, 18 (SUPL.1), pp. S71-S77Webb, A., (2002) Statistical Pattern Recognition, , Wiley, Chicheste

Automatic Visual Alignment Using Planar Regional Features And Stereo Vision

This paper addresses the determination of the rigid transformation between camera and object reference frames from a pair of intensity images and a known scene model. Two difficult parts of this problem that deserve particular attention are the matching between image and model features and the matching of image-features between stereo views. We propose the use of planar regions as features, what make both problems simpler. The former is handled by an invariant-based approach, for which a less complex base can be adopted, and the latter, by applying the epipolar constraint for inferior and superior bounds of region coordinates. The presented approach may be useful in many applications where camera-based tracking requires automatic initialization. Copyright UNION Agency - Science Press.8588Azuma, R.T., The challenge of making augmented reality work outdoors (1999) Mixed Reality: Merging Real and Virtual Worlds, pp. 379-390. , Y. Ohta andH. Tamura, eds. Springer-VerlagBajura, M., Neumann, U., Dynamic registration correction in augmented reality systems (1995) IEEE VRAISproc, pp. 189-196Cornelis, M.V.K., Pollefeys, M., Gool, L.V., Augmented reality from uncalibrated video sequences (2000) 3D Structure from Images SMILEForsyth, D.A., Mundy, J.L., Zisserman, A., Coelho, C., Heller, A.J., Rothwell, C.A., Invariant descriptors for 3D object recognition and pose (1991) PAMI(13), (10), pp. 971-991Fox, D., Burgard, W., Dellaert, F., Thrun, S., Monte Carlo localization: Efficient position estimation for mobile robots (1999) Proc. of the 16 th National Conference on Artificial IntelligenceGonzalez, R.C., Woods, R.E., (1992) Digital Image Processing, , Addison-Wesley, ReadingHaustler, G., Ritter, D., Feature-based object recognition and localization in 3D-space using a single video image (1999) CVIU, 73 (1), pp. 64-81Koller, D., Moving object recognition and classification based on recursive shape parameter estimation (1993) Proc. of the 12th Israeli Conf on Artificial Intelligence, Computer Vision, and Neural Networks, pp. 359-368. , Tel-Aviv, IsraelOlson, C.F., Time and space efficient pose clustering (1994) IEEE Conference on Computer Vision and Pattern Recognition, pp. 251-258Se, Lowe, Little, Global localization using distinctive visual features (2002) Proc. of the Intl. Conference on Intelligent Robots and Systems, pp. 226-231. , Lausanne, SwitzerlandSimon, G., Berger, M.O., A two-stage robust statistical method for temporal registration from features of various type (1997) INRIA TR 3235Wolfson, H.J., Rigoutsos, L., Geometric hashing: An overview (1997) CalSE(4), (4), pp. 10-2

A Simple And Efficient Method For Predicting Protein-protein Interaction Sites.

Computational methods for predicting protein-protein interaction sites based on structural data are characterized by an accuracy between 70 and 80%. Some experimental studies indicate that only a fraction of the residues, forming clusters in the center of the interaction site, are energetically important for binding. In addition, the analysis of amino acid composition has shown that residues located in the center of the interaction site can be better discriminated from the residues in other parts of the protein surface. In the present study, we implement a simple method to predict interaction site residues exploiting this fact and show that it achieves a very competitive performance compared to other methods using the same dataset and criteria for performance evaluation (success rate of 82.1%).7389890

Surveillance And Tracking In Feature Point Region With Predictive Filter Of Variable State

Surveillance in today's world is a very common issue in computational vision. This activity is present in literature in two different ways: first, as having both camera and objects in motion (Behrad et al. 2000); second, having detection of moving objects by means of one static camera (Lipton et al. 1998). This paper is centered in the last approach, where the interest is to find the movement of objects in images by detecting temporal differences and to define the movement region, which is analyzed by growing region, selecting one region and tracking the object. Once the region is selected, the interest points are determined through a modified corner detector of Harris et al. (1988). A reference data bank is created, to be used in the matching process and determining the characteristic of corresponding points. With these corresponding points, the movement parameters of the region can be estimated and the prediction filter (VSDF) in the tracking cycle initialized. The method that is developed here consists in considering the tracking cycle a matching process by normalized correlation with the help of the prediction filter to adjust the estimated measurements. Thus a method that allows tracking of points of interest in a surveillance region, in a stream of images with significative results to implement appropriate real time algorithms. In this stage of our research Matlab and regular digital cameras were used for prototype design of tools and experimenting. © 2007 Taylor & Francis Group.5762Azarbayejani, A., Pentland, A., Recursive estimation of motion, structure, and focal length (1995) IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (6), pp. 562-575Bar-Shalom, Y., Li, X., (1993) Estimation and Tracking: Principles, techniques and software, , Artech House. BostonBehrad, A. Shahrokni, A. Motamedi, S.A. and Madani, K. 2000. A Robust Vision-Based Moving Target Detection and Tracking System. In [email protected], M-O, Wrobel-Dautcourt, B. Petitjean, S. and Simon, G. 1999. Mixing synthetic and video images of an outdoor urban environment. 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Harwood, D. and Davis, L.S. 1998. W4 Who? When? Where? What? A Real Time System for Detecting and Tracking People. In 3rd International Conference on Face and Gesture Recognition. Nara. Japan, pp: 222-227Harris, C. and Stephens, M. 1988. A Combined Corner and Edge Detector. In Proceeding 4th Alvey Vision Conference (AVC88) pp. 147-151Hartley, R.I., In Defence of the 8-point Algorithm (1995) Proceeding of the IEEE International Conference on Computer VisionHartley, R.I., Kruppa's Equations Derived from Fundamental Matrix (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (2), pp. 133-135. , FebJaynes, C., Fast Feature Extraction of Mechanical Parts in Motion, (1999), Technical Report, Department of Computer Science. University of KentuckyKanade, T., Tomassi, C., Detection and Tracking of point features (1991), Technical Report CMU-CS-91-132. Carnegie Mellon University, AprilKoller, D. Klinker, G. Rose, E. Breen, D Whitaker, R. Tuceryan M. 1998. Real-time Vision-Based Camera Tracking for Augmented Reality Applications. Tec. Rep. California Inst. Of Technology. e-mail dieter. [email protected], A. J. Fujiyoshi, H. and Patil, R. S. 1998. Moving Target Classification and Tracking from Real-Time Video. In proc. IEEE Workshop on Applications of Computer Vision (WACV). Princeton NJ, October, pp. 8-14McLauchlan, P., Murray, D., A Unifying Framework for Structure and Motion recovery From Image Sequence (1995) Proc. 5th Int'l Conf. On Computer Vision, pp. 314-320. , Boston, pp, JuneMcLauchlan, P., The Variable State Dimension Filter (1999) Technical Report VSSP, , 4/99. University of Surrey, Dept. of Electrical Engineering, NovPilu, M., A Direct Method for Stereo Correspondence based on Singular Value Decomsition (1997) Proc. CVPR, pp. 261-266. , 97, ppRavela, S., Draper, B., Lim, J., Weiss, R., Tracking Object Motion Across Aspect Changes for Augmented Reality (1996) Proc. ARPA Image Understanding Workshop, , Palm Spring, CASchmid, C., Mohr, R., Bauckhage, C., Evaluation of Interest Point Detectors (2000) International Journal of Computer Vision, 37 (2), pp. 151-172Shapiro, L., Stockman, G., (2000) Computer VisionSoatto, S., Frezza, R., Perona, P., Motion Estimation via Dynamic Vision, (1994) Technical Report CIT-CDS, , 94-004, California Institute of TechnologyTrucco, E., Verri, A., (1998) Introductory techniques for 3D Computer Vision, , Prentice Hall. NJTuceryan, M., Greer, D., Whitaker, R., Breen, D., Crapton, C., Ahlers, K., Calibration Requirements and Procedures for a Monitor-Based Augmented Reality System (1995) IEEE Trans. On Visualization and Computer Graphics, 1 (3), pp. 255-273. , Sep. ppWelch, G., Bishop, G., Kalman Filter (2001) SIGGRAPH 2001, , Los Angeles, CA, AugustWeng, J., Cohen, P., Herniou, Camera Calibration with Distortion Models Accuracy Evaluation (1992) IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (10), pp. 965-980Wren, C., Azarbayejani, A., Darrell, T., Pentland, A., Pfinder: Real-time tracking of the human body (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7), pp. 780-785Zhang, Z., Deriche, R., Faugeras, O., Luong, Q., A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry, (1996) INRIA Research Report, , 2273, Ma

Generation Of Dense Disparity Maps In Stereo Vision

This paper presents an approach for stereo image matching that results in a dense and non-smooth disparity map. In the first step, a feature-based matching technique is applied and the obtained sparse disparity map is interpolated. In the second step the disparities in the smooth interpolated map are iteratively adjusted using an area-based matching technique and the disparity gradient. The obtained results show that disparities for non-smooth surfaces can be more accurately determined improving the description of three-dimensional scenes.6867870Accame, M., De Natale, F.G.B., Giusto, D.D., Hierarchical block matching for disparity estimation in stereo sequences (1995) Proceedings of ICIP95, pp. 374-377Barnard, S.T., Thompson, W.B., Disparity analysis of images (1980) IEEE Transactions on Pattern Analysis and Machine Intelligence, 2 (4), pp. 333-340. , JulBurt, P., Julesz, B., A Disparity Gradient Limit for Binocular Fusion (1980) Science, 208, pp. 615-617Hendriks, E.A., Marosi, G., Recursive disparity estimation algorithm for real-time stereoscopic video applications (1996) Proceedings of ICIP96, pp. 891-894Hu, M.K., Visual Pattern Recognition by Moment Invariants (1962) Transactions on Information Theory, 8, pp. 179-187Kanade, T., Dubois, E., A stereo matching algorithm with an adaptive window: Theory and experiment (1994) IEEE Transactions on Pattern Analisys and Machine Intelligence, 16 (9), pp. 1207-1212. , SeptLiu, J., Skerjanc, R., Stereo and motion correspondence in a sequence of stereo images (1993) Signal Processing: Image Communication, 5, pp. 305-318Marr, D., Poggio, T., Cooperative computation of stereo disparity (1976) Science, 194, pp. 301-328Sandwell, D.T., Biharmonic spline interpolation of GEOS-3 and seasat altimeter data (1987) Geophysical Research Letters, 14 (2), pp. 139-142Terzopoulos, D., Multilevel computation processes for visual surface reconstruction (1983) Computer Vision and Graphics Image Processing, 24, pp. 52-96Zitnick, C.L., Kanade, T., A Cooperative Algorithm for Stereo Matching and Occlusion Detection (2000) IEEE Transactions on Pattern Analisys Arid Machine Intelligence, 22 (7), pp. 675-684. , Jul