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FPGA Implementation of Multi-scale Face Detection Using HOG Features and SVM Classifier

.T., Ogunbona, P., Li, W. (2011). Human detection with contour-based local motion binary patterns. In Image Processing (ICIP), 2011 18th IEEE International Conference on , pp. 3609–3612 [16] Ojala, T., Pietikainen, M., Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on , 24(7), 971–987 [17] Ojala, T., Pietikainen, M., Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions

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Statistical Testing of Segment Homogeneity in Classification of Piecewise–Regular Objects

: Multilayer perceptrons vs. dynamic time warping, Neural Networks 3 (4): 453–465. Ciresan, D., Meier, U., Masci, J. and Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification, Neural Networks 32 : 333–338. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, CA, USA , pp. 886–893. Gray, R., Buzo, A., Gray, A., Jr. and Matsuyama, Y. (1980). Distortion measures for speech processing

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Directional measures of postural sway as predictors of balance instability and accidental falls

results of DI and the SV azimuth in young healthy subjects document that, to maintain the most stable posture, the neuromuscular system must allocate more effort to control AP stability. This can be seen in the mean value of both directional indices (DIAP = 0.77 vs DIML = 0.49). The invariant value of the SV azimuth (0.93 radian) in these subjects also documents this feature of postural control. It is significant, however, that measures in young subjects are rigorously controlled regardless of the visual input. This fixed angle and the SV set the optimal level of

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Integrated region-based segmentation using color components and texture features with prior shape knowledge

: An efficient algorithm based on immersion simulations, Pattern Analysis and Machine Intelligence   13 (6): 583-598. Wang, J., Kong, J., Lu, Y., Qi, M. and Zhang, B. (2008). A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints, Computerized Medical Imaging and Graphics   32 (8): 685-698. Wang, Z. and Vemuri, B. C. (2004). An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation, Proceedings of the 2004 IEEE

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Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

crowded scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (1): 18–32. Mandl, F. (2008). Statistical Physics, 2nd Edition , Manchester Physics, Hoboken, NJ. Mathiassen, J.R. and Skavhaug, A. (2002). Texture similarity measure using Kullback–Leibler divergence between gamma distributions, ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark , Part III, pp. 133–147. McNamara, G.R. and Zanetti, G. (1988). Use of the Boltzmann equation to simulate

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Joint modeling of flood peak discharges, volume and duration: a case study of the Danube River in Bratislava

, L., Merz, B., Merz, R., Molnar, P., Montanari, A., Neuhold, C., Parajka, J., Perdigão, R.A.P., Plavcova, L., Rogger, M., Salinas, J.L., Sauquet, E., Schär, C., Szolgay, J., Viglione, A., Blöschl, G., 2013. Understanding flood regime changes in Europe: a state of the art assessment. Hydrol. Earth Syst. Sci. Discuss., 10, 15525-15624. Hoefinding, W., 1940. Scale of invariant correlation theory. Schr. Math. Angew. Math., Univ. Berlin, Berlin, 5, 3, 181-233. (In German.) Joe, H., 1997. Multivariate Models and Dependence Concepts. Chapman and Hall, New

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Nonlinear analysis of vehicle control actuations based on controlled invariant sets

with electronic throttle and automatic transmission, IEEE Transactions on Control Systems Technology 15 (3): 474–482. Korda, M., Henrion, D. and Jones, C.N. (2013). Convex computation of the maximum controlled invariant set for discrete-time polynomial control systems, Conference on Decision and Control, Firenze, Italy , pp. 7107–7112. Kritayakirana, K. and Gerdes, J. (2012a). Using the centre of percussion to design a steering controller for an autonomous race car, Vehicle System Dynamics 50 (Supp1): 33–51. Kritayakirana, K. and Gerdes, J

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Perception of specific military skills – the impact of perfectionism and self-efficacy

, CDS, and MA. Time-invariant predictors are self-oriented and socially prescribed perfectionism. Time-varying predictors are self-efficacy measured at each time point. 3 Results 3.1 Preliminary analyses Confirmatory factor analyses (CFAs) were conducted on all measures and time points to check for construct validity. Specific military skills were treated as correlated three-factor models, perfectionism was treated as a two-factor model, and self-efficacy was treated as a one-dimensional factor model. A summary of the results from these factor

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An Automated Approach for Complementing Ad Blockers’ Blacklists

workload characterization: the search for invariants. In Proc. SIGMETRICS ’96 , pages 126–137, 1996. [5] P. Barford, A. Bestavros, A. Bradley, and M. Crovella. Changes in web client access patterns: Characteristics and caching implications. World Wide Web , 2(1-2):15–28, 1999. [6] L. Bernaille, R. Teixeira, I. Akodkenou, A. Soule, and K. Salamatian. Traffic classification on the fly. SIGCOMM Comput. Commun. Rev. , 36(2):23–26, Apr. 2006. [7] C. M. Bishop. Pattern recognition and machine learning . Springer, 2006. [8] M. Butkiewicz, H. V

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Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

) with correlation measures for gene expression analysis. Neurocomputing, 69(6-7):651-659, March 2006. [51] S. Saralajew and T. Villmann. Adaptive tangent metrics in generalized learning vector quantization for transformation and distortion invariant classification learning. In Proceedings of the International Joint Conference on Neural networks (IJCNN) , Vancover, pages 2672-2679. IEEE Computer Society Press, 2016. [52] S. Saralajew, D. Nebel, and T. Villmann. Adaptive Hausdorff distances and tangent distance adaptation for

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