E. H. Adelson and J. R. Bergen, Spatiotemporal energy models for the perception of motion, Journal of the Optical Society of America A, vol.2, issue.2, p.284299, 1985.
DOI : 10.1364/JOSAA.2.000284

J. K. Aggarwal and M. S. Ryoo, Human activity analysis, ACM Computing Surveys, vol.43, issue.3, p.143, 2011.
DOI : 10.1145/1922649.1922653

J. E. Albus, L. J. Lewins, and J. R. Schacht, Centroid tracking using a probability map for target segmentation, SPIE Conf. on Acquisition , Tracking, and Pointing XVI, p.175185, 2002.

. Wu, An optimal algorithm for approximate nearest neighbor searching in xed dimensions, J. of the ACM, vol.45, issue.6, p.891923, 1998.

B. B. Drost, M. Ulrich, N. Navab, and S. Ilic, Model globally, match locally: Efficient and robust 3D object recognition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.9981005, 2010.
DOI : 10.1109/CVPR.2010.5540108
URL : http://campar.cs.tum.edu/pub/drost2010CVPR/drost2010CVPR.pdf

D. H. Ballard, Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, vol.13, issue.2, p.111122, 1981.
DOI : 10.1016/0031-3203(81)90009-1

R. Barate, Apprentissage de fonctions visuelles pour un robot mobile par programmation génétique

R. Barate and A. Manzanera, Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming, 8th International Conference on Articial Evolution (EA'07), p.2536, 2007.
DOI : 10.1007/978-3-540-79305-2_3
URL : https://hal.archives-ouvertes.fr/hal-01222652

R. Barate and A. Manzanera, Evolution of visual controllers for obstacle avoidance in mobile robotics, Evolutionary Intelligence, vol.11, issue.3, p.85102, 2009.
DOI : 10.1007/s12065-009-0021-4
URL : https://hal.archives-ouvertes.fr/hal-01130998

O. Barnich and M. Van-droogenbroeck, ViBE: A powerful random technique to estimate the background in video sequences, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, p.945948, 2009.
DOI : 10.1109/ICASSP.2009.4959741

T. Bernard, From Sigma - Delta modulation to digital halftoning of images, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, p.28052808, 1991.
DOI : 10.1109/ICASSP.1991.150985

M. Thierry, B. Y. Bernard, F. Zavidovique, and . Devos, A programmable Articial Retina, IEEE Journal of Solidstate Circuits, pp.28-7789798, 1993.

G. Bertrand, On P-simple points Compte Rendus à l'Académie des Sciences, pp.321-110771084, 1995.

F. Bimbard, Improvement in motion-detection algorithms for real-time processing by using the OpenMP library and/or SIMD instructions

S. Bouchafa and B. Zavidovique, c-velocity : A owcumulating uncalibrated approach for 3d plane detection, Int. J. Comput. Vision, vol.97, issue.2, p.148166, 2012.

P. Bouthémy and P. Lalande, Recovery of moving object masks in an image sequence using local spatiotemporal contextual information, Optical Engineering, vol.32, issue.6, pp.32-612051212, 1993.
DOI : 10.1117/12.134183

E. Jack and . Bresenham, Algorithm for computer control of a digital plotter, IBM Systems Journal, vol.4, issue.1, p.2530, 1965.

A. Buades, B. Coll, and J. Morel, A Non-Local Algorithm for Image Denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), p.6065, 2005.
DOI : 10.1109/CVPR.2005.38

P. Burman and W. Polonik, Multivariate mode hunting: Data analytic tools with measures of significance, Journal of Multivariate Analysis, vol.100, issue.6, p.11981218, 2009.
DOI : 10.1016/j.jmva.2008.10.015

A. Caplier, C. Dumontier, F. Luthon, and P. Coulon, Algorithme de détection de mouvement par modélisation markovienne. Mise en ÷uvre sur DSP, pp.13-2175190, 1996.

F. Martínez-carrillo, A. Manzanera, and E. R. Castro, A motion descriptor based on statistics of optical ow orientations for action classication in video-surveillance, Accepted to : Int. Conf. on Multimedia and Signal Processing (CMSP'12), 2012.

T. H. Chalidabhongse, K. Kim, D. Harwood, and L. S. Davis, A perturbation method for evaluating background subtraction algorithms, Proc. Joint IEEE Int. Work. on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2003.

R. Chaudhry, A. Ravichandran, G. D. Hager, and R. Vidal, Histograms of oriented optical ow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions

. Collective, Intel R C++ Compiler for Linux Systems -User's

. Guide, Intel Corporation, Document, vol.number, pp.253254-253268, 1996.

J. Cousty, G. Bertrand, L. Najman, and M. Couprie, Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.8, p.3113621374, 2009.
DOI : 10.1109/TPAMI.2008.173
URL : https://hal.archives-ouvertes.fr/hal-00622410

M. Crosier and L. D. Grin, Using basic image features for texture classication, Int. J. of Computer Vision, vol.88, issue.3, p.447460, 2010.

C. Franklin and . Crow, Summed-area tables for texture mapping, SIG- GRAPH Comput. Graph, vol.18, issue.3, p.207212, 1984.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, Visual categorization with bags of keypoints, Workshop on Statistical Learning in Computer Vision, p.122, 2004.

L. Costa, Robust skeletonization through exact Euclidean distance transform and its application to neuromorphometry, Real-Time Imaging, vol.6, issue.6, p.415431, 2000.

D. Filliat, A visual bag of words method for interactive qualitative localization and mapping, Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007.
DOI : 10.1109/ROBOT.2007.364080
URL : https://hal.archives-ouvertes.fr/hal-00640996

R. J. Fisher, A. J. Fisher, and H. G. Dietz, Compiling for SIMD Within a Register, 11th Annual Workshop on Languages and Compilers for Parallel Computing (LCPC'98), volume 1656 of Lecture Notes in Computer Science, p.290304
DOI : 10.1007/3-540-48319-5_19

D. J. Fleet and A. D. Jepson, Computation of component image velocity from local phase information, International Journal of Computer Vision, vol.4, issue.1, p.77104, 1990.
DOI : 10.1007/BF00056772

D. Florins and A. Manzanera, Detection of oating mines in infrared sequences by multiscale geometric ltering, SPIE Conference in Defence Security and Sensing : Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 2012.

R. Forchheimer and A. Astrøm, Near-sensor image processing: a new paradigm, IEEE Transactions on Image Processing, vol.3, issue.6, p.736746, 1994.
DOI : 10.1109/83.336244

W. T. Freeman and E. H. Adelson, The design and use of steerable lters, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.13, issue.9, p.891906, 1991.

M. Garrigues and A. Manzanera, Exact and approximate median splitting on distributed memory machines, Int. Conf. on Parallel and Distributed Processing Techniques and Applications, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01118331

M. Garrigues and A. Manzanera, Real Time Semi-dense Point Tracking, Int. Conf. on Image Analysis and Recognition, p.245252, 2012.
DOI : 10.1007/978-3-642-31295-3_29
URL : https://hal.archives-ouvertes.fr/hal-01118324

V. Gies, T. M. Bernard, and A. Mérigot, Asynchronous Regional Computation Capabilities for Digital Retinas, 2006 International Workshop on Computer Architecture for Machine Perception and Sensing, 2006.
DOI : 10.1109/CAMP.2007.4350341

L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, Actions as space-time shapes [54] L. Grady and C. Alvino. The piecewise smooth Mumford-Shah functional on an arbitrary graph, IEEE Trans. on Pattern Analysis and Machine Intelligence IEEE Trans. on Image Processing, vol.29, issue.1211, pp.22472253-22472271, 2007.

P. Guermeur and A. Manzanera, Image Characterization from Statistical Reduction of Local Patterns, Progress in Pattern Recognition, p.571578, 2009.
DOI : 10.1007/978-3-642-10268-4_67
URL : https://hal.archives-ouvertes.fr/hal-01130930

G. Heitz and D. Koller, Learning spatial context : Using stu to nd things, European Conference on Computer Vision (ECCV'08), 2008.

C. J. Hilditch, Linear skeletons from square cupboards, Machine Intelligence, vol.4, p.403420, 1969.

J. Hiraiwa, E. Vargas, and S. Toral, An FPGA based embedded vision system for real-time motion segmentation, 17th Int. Conf. on Systems, Signals and Image Processing (IWSSIP'10), p.360363, 2010.

B. K. Horn and B. G. Schunck, Determining optical ow, Articial Intelligence, vol.17, p.185203, 1981.

P. V. Hough, Machine analysis of bubble chamber pictures, Int. Conf. High Energy Accelerators and Instrumentation, 1959.

J. and A. Rosenfeld, A Pyramid Framework for Early Vision : Multiresolutional Computer Vision, 1994.

K. Karmann and A. Brandt, Time-Varying Image Processing and Moving Object Recognition, chapter Moving Object Recognition Using an Adaptive Background Memory, 1990.

Y. Ke, R. Sukthankar, and M. Hebert, Ecient visual event detection using volumetric features, International Conference on Computer Vision, pp.166-173, 2005.

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, Background modeling and subtraction by codebook construction, Proc. ICIP, p.30613064, 2004.

J. J. Koenderink and A. J. Van-doorn, Representation of local geometry in the visual system, Biological Cybernetics, vol.53, issue.6, pp.367-375, 1987.
DOI : 10.1007/BF00318371

L. Lacassagne, A. Manzanera, J. Denoulet, and A. Mérigot, High performance motion detection : some trends BIBLIOGRAPHIE 63
DOI : 10.1007/s11554-008-0096-7
URL : http://dx.doi.org/10.1007/s11554-008-0096-7

L. Lacassagne and B. Zavidovique, Light speed labeling: efficient connected component labeling on RISC architectures, Journal of Real-Time Image Processing, vol.89, issue.1, p.117135, 2011.
DOI : 10.1007/s11554-009-0134-0

B. C. Lee and M. Hedley, Background estimation for video surveillance, Proc. IVCNZ'02, p.315320, 2002.

B. Leibe, A. Leonardis, and B. Schiele, An Implicit Shape Model for Combined Object Categorization and Segmentation, ECCV Workshop on Statistical Learning in Computer Vision, 2004.
DOI : 10.1007/11957959_26

F. Leymarie and M. D. Levine, Fast raster scan distance propagation on the discrete rectangular lattice, CVGIP: Image Understanding, vol.55, issue.1, 1992.
DOI : 10.1016/1049-9660(92)90008-Q

T. Lindeberg, Feature detection with automatic scale selection

T. Lindeberg and B. Ter-haar-romeny, Geometry-Driven Diffusion in Computer Vision, chapter Linear scale-space : I. Basic theory, II. Early visual operations, Series in Mathematical Imaging and Vision, p.177, 1994.

C. Lohou, Etude d'algorithmes de squelettisation pour images 2D et 3D selon une approche topologie digitale ou topologie discrète, 2001.

T. Low and A. Manzanera, Ground-plane classication for robot navigation, International Conference on Control, Automation, Robotics and Vision (ICARCV'10), 2010.

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, p.91110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

B. D. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proc. DARPA Image Understanding Workshop, p.121130, 1981.

M. Cherng-min, On topology preservation in 3d thinning. CV- GIP : Image Understanding, pp.59-3328339, 1994.

A. Manzanera, Algorithmique de traitement d'images pour la détection des cibles -système antichar ERYX, 2002.

A. Manzanera, Morphological segmentation on the programmable retina : towards mixed synchronous
URL : https://hal.archives-ouvertes.fr/hal-01222701

A. Rosenfeld and J. L. Pfaltz, Sequential operations in digital picture processing, J. ACM, vol.13, issue.4, p.471494, 1966.

E. Rosten and T. Drummond, Machine Learning for High-Speed Corner Detection, European Conference on Computer Vision (ECCV'06), p.430443, 2006.
DOI : 10.1007/11744023_34

Y. Rubner and C. Tomasi, Texture-based image retrieval without segmentation, Proceedings of the Seventh IEEE International Conference on Computer Vision, p.10181024, 1999.
DOI : 10.1109/ICCV.1999.790380
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.8164

D. Rutovitz, Pattern Recognition, Journal of the Royal Statistical Society. Series A (General), vol.129, issue.4, pp.504-530, 1966.
DOI : 10.2307/2982255

P. Sand and S. Teller, Particle video : Long-range motion estimation using point trajectories, Computer Vision and Pattern Recognition (CVPR'06), p.21952202, 2006.

J. Santos-victor, G. Sandini, F. Curotto, and S. Garibaldi, Divergent stereo for robot navigation: learning from bees, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.434-439, 1993.
DOI : 10.1109/CVPR.1993.341094

C. Schmid and R. Mohr, Local grayvalue invariants for image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.5, p.530534, 1997.
DOI : 10.1109/34.589215
URL : https://hal.archives-ouvertes.fr/inria-00548358

S. D. Shapiro, Feature space transforms for curve detection, Pattern Recognition, vol.10, issue.3, p.129143, 1978.
DOI : 10.1016/0031-3203(78)90022-5

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.22, issue.8, p.888905, 2000.

F. Y. Shih and Y. Wu, Fast Euclidean distance transformation in two scans using a 3??3 neighborhood, Computer Vision and Image Understanding, vol.93, issue.2, p.195205, 2004.
DOI : 10.1016/j.cviu.2003.09.004

C. Stauer and E. Grimson, Learning patterns of activity using real-time tracking, IEEE Trans. on PAMI, 2000.

R. Stefanelli and A. Rosenfeld, Some Parallel Thinning Algorithms for Digital Pictures, Journal of the ACM, vol.18, issue.2, p.255264, 1971.
DOI : 10.1145/321637.321646

H. Sutter, The free lunch is over : A fundamental turn toward concurrency in software, Dr. Dobb's Journal, vol.30, issue.3, p.202210, 2005.

C. Tomasi and T. Kanade, Detection and tracking of point features, 1991.

C. R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, Pnder : Real-time tracking of the human body, IEEE Trans. on PAMI, 1997.

H. Ye, L. Lacassagne, D. Etiemble, L. Cabaret, J. Falcou et al., Impact of high-level transformers for highlevel synthesis for motion detection algorithm, Conference on Design and Architectures for Signal and Image Processing (DA- SIP'2012), 2012.

B. Zavidovique and G. Stamon, Bilevel processing of multilevel images, Pattern Recognition and Image Processing, 1981.

G. Zhu, C. Xu, W. Gao, and Q. Huang, Action recognition in broadcast tennis video using optical ow and support vector machine, ECCV Workshop on HCI, p.8998, 2006.

]. G. Zipf, Human behavior and the principle of least-eort

F. Martínez-carrillo, A. Manzanera, and E. R. Castro, Analysing the hovering ight of the hummingbird using statistics of the optical ow eld. Accepted to : ICPR Workshop on Visual observation and analysis of animal and insect behavior, 2012.

M. Garrigues and A. Manzanera, Real Time Semi-dense Point Tracking, Int. Conf. on Image Analysis and Recognition, pp.245-252, 2012.
DOI : 10.1007/978-3-642-31295-3_29
URL : https://hal.archives-ouvertes.fr/hal-01118324

A. Manzanera, Local Jet Feature Space Framework for Image Processing and Representation, 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, p.261268, 2011.
DOI : 10.1109/SITIS.2011.49
URL : https://hal.archives-ouvertes.fr/hal-01119682

F. Martínez-carrillo, A. Manzanera, C. S. Marta, and E. R. Castro, Characterization of motion cardiac patterns in magnetic resonance cine, 2011 International Conference on Image Information Processing, p.15, 2011.
DOI : 10.1109/ICIIP.2011.6108837

T. Low and A. Manzanera, Ground-plane classication for robot navigation, International Conference on Control, Automation, Robotics and Vision (ICARCV'10), 2010.

L. Henao, A. Manzanera, and E. Romero, Extracción y seguimiento de los miembros inferiores sin marcadores, VI Seminario Internacional de Procesamiento y Análisis de Imágenes Médicas (SIPAIM'10), 2010.

A. Manzanera, Local jet based similarity for nl-means ltering, International Conference on Pattern Recognition (ICPR'10), p.26682671, 2010.
DOI : 10.1109/icpr.2010.654
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.1792

L. Lacassagne, A. Manzanera, and A. Dupret, Motion detection: Fast and robust algorithms for embedded systems, 2009 16th IEEE International Conference on Image Processing (ICIP), 2009.
DOI : 10.1109/ICIP.2009.5413946
URL : https://hal.archives-ouvertes.fr/hal-01130889

R. Barate and A. Manzanera, Learning Vision Algorithms for Real Mobile Robots with Genetic Programming, 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS), 2008.
DOI : 10.1109/LAB-RS.2008.20
URL : https://hal.archives-ouvertes.fr/hal-01222632

R. Barate and A. Manzanera, Generalization performance of vision based controllers for mobile robots evolved with genetic programming, Proceedings of the 10th annual conference on Genetic and evolutionary computation, GECCO '08, 2008.
DOI : 10.1145/1389095.1389349
URL : https://hal.archives-ouvertes.fr/hal-01222613

R. Barate and A. Manzanera, Evolving vision controllers with a twophase genetic programming system using imitation, 10th International Conference on the Simulation of Adaptive Behavior (SAB'08), p.7382, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01222604

C. Dubreu, A. Manzanera, and E. Bohain, Simulation of video sequences for an accurate evaluation of tracking algorithms on complex scenes, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXII
DOI : 10.1117/12.784262
URL : https://hal.archives-ouvertes.fr/hal-01222640

O. Vermeulen, A. Manzanera, and L. Lacassagne, Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007.
DOI : 10.1109/SITIS.2007.142
URL : https://hal.archives-ouvertes.fr/hal-01222664

A. Manzanera, Sigma-delta background subtraction and the zipf law, Progress in Pattern Recognition, p.4251, 2007.

P. Guermeur, P. Dokladal, E. Dokladalova, and A. Manzanera, Fpga lab sessions in a general-purpose image processing course, 2nd International Workshop on Recongurable Computing Education (RCE'07), 2007.
URL : https://hal.archives-ouvertes.fr/hal-00622533

C. Dubreu, A. Manzanera, and E. Bohain, Comprehensive evaluation of tracking systems by non-photorealistic simulation, Acquisition, Tracking, Pointing, and Laser Systems Technologies XXI, 2007.
DOI : 10.1117/12.720821
URL : https://hal.archives-ouvertes.fr/hal-01222654

T. Ridene and A. Manzanera, Mécanismes d'attention visuelle sur rétine programmable, Traitement et Analyse de l'Information : Méthodes et Applications (TAIMA'07), p.301306, 2007.

P. Nadrag, A. Manzanera, and N. Burrus, Smart retina as a contourbased visual interface, Distributed Smart Cameras Workshop (DSC'06), 2006.
URL : https://hal.archives-ouvertes.fr/hal-01222683

C. Julien, A. Richefeu, and . Manzanera, Détection de mouvement par capteur intelligent, ORASIS'05, 2005.

C. Julien, A. Richefeu, and . Manzanera, A new hybrid dierential lter for motion detection, International Conference on Computer Vision and Graphics, 2004.

A. Manzanera and J. C. Richefeu, A robust and computationally ecient motion detection algorithm based on sigma-delta background estimation, Indian Conference on Computer Vision, Graphics and Image Processing, p.4651, 2004.

A. Manzanera and T. M. Bernard, Metrical properties of a collection of 2D parallel thinning algorithms, International Workshop on Combinatorial Image Analysis (IWCIA'03, 2003.
DOI : 10.1016/S1571-0653(04)00491-3
URL : https://hal.archives-ouvertes.fr/hal-01222698

C. Julien, A. Richefeu, and . Manzanera, A morphological dominant points detection and its cellular implementation, International Symposium on Signal Processing and its Applications (ISSPA'03), p.181184, 2003.

A. Manzanera, Morphological segmentation on the programmable retina : towards mixed synchronous/asynchronous algorithms, 6th International Symposium on Mathematical Morphology (ISMM'02), 2002.
URL : https://hal.archives-ouvertes.fr/hal-01222701

A. Manzanera, F. Prêteux, and T. M. Bernard, Markovian modeling on programmable retina, Conference on Controle Quality by Articial Vision (QCAV'01), p.232237, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00271830

A. Manzanera, M. Thierry, F. Bernard, B. Prêteux, and . Longuet, Ultra-Fast Skeleton Based on an Isotropic Fully Parallel Algorithm, 8th Discrete Geometry for Computer Imagery (DGCI'99), p.313, 1999.
DOI : 10.1007/3-540-49126-0_24
URL : https://hal.archives-ouvertes.fr/hal-01245393

A. Manzanera, M. Thierry, F. Bernard, B. Prêteux, and . Longuet, A unied mathematical framework for a compact and fully parallel n-d skeletonization procedure, Vision Geometry VIII (VG'99), 1999.

A. Manzanera, M. Thierry, F. Bernard, B. Prêteux, and . Longuet, Medial faces from a concise 3D thinning algorithm, Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999.
DOI : 10.1109/ICCV.1999.791239
URL : https://hal.archives-ouvertes.fr/hal-01245449

M. Thierry, A. Bernard, and . Manzanera, Improved low complexity fully parallel thinning algorithm, International Conference on Image Analysis and Processing (ICIAP'99), 1999.

A. Manzanera and J. Jolion, Pyramide irrégulière, 9ème congrès AFCET/RFIA (RFIA'94), p.221229, 1994.

A. Manzanera and J. Jolion, Tesselation hiérarchique irrégulière, Colloque Géométrie Discrète en Imagerie (DGCI'93) Thèses encadrées, p.98107, 1993.

C. Dubreu, Algorithmique de traitement d'image des systèmes de surveillance infrarouges air-sol, 2009.

R. Barate, Apprentissage de fonctions visuelles pour un robot mobile par programmation génétique, 2008.

J. Richefeu, Détection et analyse du mouvement sur système de vision à base de rétine numérique

A. Manzanera, Vision Articielle Rétinienne, Ecole Nationale Supérieure des Télécommunications, 2000.

G. Bertrand, On P-simple points Comptes RendusàRendus`Rendusà l'Académie des Sciences, pp.321-322, 1995.

G. Borgefors, Distance transformations in digital images, Computer Vision, Graphics, and Image Processing, vol.34, issue.3, pp.344-371, 1986.
DOI : 10.1016/S0734-189X(86)80047-0

U. Eckhardt and G. Maderlechner, INVARIANT THINNING, International Journal of Pattern Recognition and Artificial Intelligence, vol.07, issue.05, pp.1115-1144, 1993.
DOI : 10.1142/S021800149300056X

A. X. Falcão, L. Da-fontoura-costa, and B. S. Da-cunha, Multiscale skeletons by image foresting transform and its application to neuromorphometry, Pattern Recognition, vol.35, issue.7, pp.1571-1582, 2002.
DOI : 10.1016/S0031-3203(01)00148-0

L. J. Latecki, U. Eckhardt, and A. Rosenfeld, Well-composed sets, Computer Vision and Image Understanding, pp.61-170, 1995.
DOI : 10.1016/s1076-5670(00)80028-2

A. Manzanera, T. M. Bernard, F. Prêteux, and B. Longuet, <bold>n</bold>-dimensional skeletonization: a unified mathematical framework, Journal of Electronic Imaging, vol.11, issue.1, pp.25-37, 2002.
DOI : 10.1117/1.1426080

A. Manzanera and T. M. Bernard, MB: A coherent collection of 2D parallel thinning algorithms, 2002.
URL : https://hal.archives-ouvertes.fr/hal-01222700

K. Siddiqi, S. Bouix, A. Tannenbaum, and S. W. Zucker, Hamilton-Jacobi skeletons References 1. Hough, P.: Machine analysis of bubble chamber pictures, Int. Conf. High Energy Accelerators and Instrumentation, pp.215-231, 1959.
DOI : 10.1023/A:1016376116653

R. O. Duda and P. E. Hart, Use of the Hough transformation to detect lines and curves in pictures, Communications of the ACM, vol.15, issue.1, pp.11-15, 1972.
DOI : 10.1145/361237.361242

D. H. Ballard, Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, vol.13, issue.2, pp.111-122, 1981.
DOI : 10.1016/0031-3203(81)90009-1

B. Leibe, A. Leonardis, and B. Schiele, An Implicit Shape Model for Combined Object Categorization and Segmentation, In: ECCV Workshop on Statistical Learning in Computer Vision, 2004.
DOI : 10.1007/11957959_26

G. Peyré, Manifold models for signals and images, Computer Vision and Image Understanding, vol.113, issue.2, pp.249-260, 2009.
DOI : 10.1016/j.cviu.2008.09.003

W. Freeman and E. Adelson, The design and use of steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.9, pp.891-906, 1991.
DOI : 10.1109/34.93808

Y. Rubner and C. Tomasi, Texture-based image retrieval without segmentation, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1018-1024, 1999.
DOI : 10.1109/ICCV.1999.790380
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.8164

G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, Visual categorization with bags of keypoints, Workshop on Statistical Learning in Computer Vision (ECCV'04), pp.1-22, 2004.

K. Mikolajczyk and C. Schmid, Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision, vol.60, issue.1, pp.63-86, 2004.
DOI : 10.1023/B:VISI.0000027790.02288.f2
URL : https://hal.archives-ouvertes.fr/inria-00548554

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

J. Koenderink and A. Van-doorn, Representation of local geometry in the visual system, Biological Cybernetics, vol.53, issue.6, pp.367-375, 1987.
DOI : 10.1007/BF00318371

C. Schmid and R. Mohr, Local grayvalue invariants for image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.5, pp.530-534, 1997.
DOI : 10.1109/34.589215
URL : https://hal.archives-ouvertes.fr/inria-00548358

M. Crosier and L. Griffin, Using Basic Image Features for Texture Classification, International Journal of Computer Vision, vol.62, issue.1, pp.447-460, 2010.
DOI : 10.1007/s11263-009-0315-0

L. Florack, B. Ter-haar-romeny, M. Viergever, and J. Koenderink, The Gaussian scale-space paradigm and the multiscale local jet, International Journal of Computer Vision, vol.24, issue.1, pp.61-75, 1996.
DOI : 10.1007/BF00126140

J. Orchard, M. Ebrahimi, and A. Wong, Efficient non-local means denoising using the SVD, Proc. ICIP, pp.1732-1735, 2008.

T. Lindeberg, Feature detection with automatic scale selection, Int. J. of Computer Vision, vol.30, issue.2, pp.77-116, 1998.

J. L. Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM, vol.18, issue.9, pp.509-517, 1975.
DOI : 10.1145/361002.361007

D. Mount and S. Arya, ANN: A library for approximate nearest neighbor searching, CGC Workshop on Computational Geometry, 1997.

A. Buades, B. Coll, and J. Morel, A Non-Local Algorithm for Image Denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.60-65, 2005.
DOI : 10.1109/CVPR.2005.38

A. Manzanera, Local Jet Based Similarity for NL-Means Filtering, 2010 20th International Conference on Pattern Recognition, pp.2668-2671, 2010.
DOI : 10.1109/ICPR.2010.654
URL : https://hal.archives-ouvertes.fr/hal-01130884

H. Wang and D. Suter, A consensus-based method for tracking: Modelling background scenario and foreground appearance, Pattern Recognition, vol.40, issue.3, pp.1091-1105, 2007.
DOI : 10.1016/j.patcog.2006.05.024

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, Background modeling and subtraction by codebook construction, Proc. ICIP, pp.3061-3064, 2004.

C. Kervrann and J. Boulanger, Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation, International Journal of Computer Vision, vol.27, issue.2, pp.45-69, 2008.
DOI : 10.1007/s11263-007-0096-2

P. Burman and W. Polonik, Multivariate mode hunting: Data analytic tools with measures of significance, Journal of Multivariate Analysis, vol.100, issue.6, pp.1198-1218, 2009.
DOI : 10.1016/j.jmva.2008.10.015

R. Caplier, A. Dumontier, C. Luthon, F. Coulon, and P. , Mrf based motion detection algorithm image processing board implementation, Traitement du signal, vol.13, issue.2, pp.177-190, 1996.

T. H. Chalidabhongse, K. Kim, D. Harwood, and L. Davis, A perturbation method for evaluating background subtraction algorithms, Proc. Joint IEEE Int. Work. on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2003.

S. Cheung and C. Kamath, Robust techniques for background subtraction in urban traffic video, Visual Communications and Image Processing 2004, 2004.
DOI : 10.1117/12.526886

J. Denoulet, G. Mostafaoui, L. Lacassagne, and A. Mérigot, Implementing Motion Markov Detection on General Purpose Processor and Associative Mesh, Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05), 2005.
DOI : 10.1109/CAMP.2005.31
URL : https://hal.archives-ouvertes.fr/hal-00622245

A. Elgammal, D. Harwood, and L. Davis, Non-parametric Model for Background Subtraction, Proc. IEEE ECCV, 2000.
DOI : 10.1007/3-540-45053-X_48
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.3233

K. Karmann and A. Von-brandt, Moving object recognition using an adaptive background memory, Time-Varying Image Processing and Moving Object Recognition, 1990.

T. Komuro, I. Ishii, M. Ishikawa, and A. Yoshida, A digital vision chip specialized for high-speed target tracking, IEEE Transactions on Electron Devices, vol.50, issue.1, pp.191-199, 2003.
DOI : 10.1109/TED.2002.807255

L. Lacassagne, M. Milgram, and P. Garda, Motion detection, labeling, data association and tracking, in real-time on RISC computer, Proceedings 10th International Conference on Image Analysis and Processing, pp.520-525, 1999.
DOI : 10.1109/ICIAP.1999.797648

B. Lee and M. Hedley, Background estimation for video surveillance, Proc. IVCNZ'02, pp.315-320, 2002.

A. Manzanera and J. Richefeu, A robust and computationally efficient motion detection algorithm based on R?D background estimation, Proc. ICVGIP'04, pp.46-51, 2004.

N. Mcfarlane and C. Schofield, Segmentation and tracking of piglets in images, Machine Vision and Applications, vol.5, issue.105, pp.187-193, 1995.
DOI : 10.1007/BF01215814

A. Mérigot, Associative nets: a graph-based parallel computing model, IEEE Transactions on Computers, vol.46, issue.5, pp.558-571, 1997.
DOI : 10.1109/12.589222

A. Mittal and N. Paragios, Motion-based background subtraction using adaptive kernel density estimation, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004.
DOI : 10.1109/CVPR.2004.1315179

N. Oliver, B. Rosario, and A. Pentland, A Bayesian computer vision system for modeling human interactions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.831-843, 2000.
DOI : 10.1109/34.868684

M. Piccardi, Background subtraction techniques: a review, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2004.
DOI : 10.1109/ICSMC.2004.1400815

P. Power and J. Schoonees, Understanding background mixture models for foreground segmentation, Proc. IVCNZ'02, pp.267-271, 2002.

J. Richefeu and A. Manzanera, A NEW HYBRID DIFFERENTIAL FILTER FOR MOTION DETECTION, Proc. ICCVG'04, 2004.
DOI : 10.1007/1-4020-4179-9_105
URL : https://hal.archives-ouvertes.fr/hal-01222690

C. Stauffer and E. Grimson, Learning patterns of activity using real-time tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.747-757, 2000.
DOI : 10.1109/34.868677

K. Toyoma, J. Krumm, B. Brumitt, and B. Meyers, Wallflower: principles and practice of background maintenance, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.255-261, 1999.
DOI : 10.1109/ICCV.1999.791228

C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, Pfinder: real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.780-785, 1997.
DOI : 10.1109/34.598236

A. Manzanera and J. C. Richefeu, A new motion detection algorithm based on ??????? background estimation, Pattern Recognition Letters, vol.28, issue.3, pp.320-328, 2007.
DOI : 10.1016/j.patrec.2006.04.007
URL : https://hal.archives-ouvertes.fr/hal-01222650

K. P. Karmann and A. Von-brandt, Moving Object Recognition Using an Adaptive Background Memory, Time-Varying Image Processing and Moving Object Recognition, 1990.

M. Piccardi, Background subtraction techniques: a review, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2004.
DOI : 10.1109/ICSMC.2004.1400815

S. C. Cheung and C. Kamath, Robust techniques for background subtraction in urban traffic video, Visual Communications and Image Processing 2004, 2004.
DOI : 10.1117/12.526886

N. Mcfarlane and C. Schofield, Segmentation and tracking of piglets in images, Machine Vision and Applications, pp.187-193, 1995.
DOI : 10.1007/BF01215814

A. Manzanera and J. Richefeu, A robust and computationally efficient motion detection algorithm based on ?-? background estimation, Proc. ICVGIP, pp.46-51, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01222695

A. Manzanera and J. Richefeu, A new motion detection algorithm based on ??????? background estimation, Pattern Recognition Letters, vol.28, issue.3, pp.320-328, 2007.
DOI : 10.1016/j.patrec.2006.04.007
URL : https://hal.archives-ouvertes.fr/hal-01222650

P. Power and J. Schoonees, Understanding background mixture models for foreground segmentation, Imaging and Vision Computing, 2002.

G. Zipf, Human behavior and the principle of least-effort, 1949.

Y. Caron, P. Makris, and N. Vincent, A method for detecting artificial objects in natural environments, Object recognition supported by user interaction for service robots, pp.600-603, 2002.
DOI : 10.1109/ICPR.2002.1044812

C. Intel, Intel R C++ Compiler for Linux Systems -User's Guide (1996-2003) Document number, pp.253254-253268

R. 1. Bleuler, S. Brack, M. Thiele, L. Zitzler, and E. , Multiobjective genetic programming: reducing bloat using SPEA2, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), pp.536-543, 2001.
DOI : 10.1109/CEC.2001.934438
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.7279

S. Cagnoni, Evolutionary Computer Vision: A Taxonomic Tutorial, 2008 Eighth International Conference on Hybrid Intelligent Systems, pp.1-6, 2008.
DOI : 10.1109/HIS.2008.168

D. Coombs, M. Herman, T. Hong, and M. Nashman, Real-time obstacle avoidance using central flow divergence, and peripheral flow, IEEE Transactions on Robotics and Automation, vol.14, issue.1, pp.49-59, 1998.
DOI : 10.1109/70.660840
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.486

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002.
DOI : 10.1109/4235.996017

E. Dunn, G. Olague, and E. Lutton, Parisian camera placement for vision metrology, Pattern Recognition Letters, vol.27, issue.11, pp.1209-1219, 2006.
DOI : 10.1016/j.patrec.2005.07.019

M. Ebner, R. Poli, W. Langdon, M. Schoenauer, T. Fogarty et al., On the evolution of interest operators using genetic programming Late breaking papers at EuroGP'98: the first European workshop on genetic programming, pp.6-10, 1998.

M. Ebner, A Real-Time Evolutionary Object Recognition System, Genetic programming: proceedings of the 12th European conference EuroGP, pp.268-279, 2009.
DOI : 10.1109/ISWC.2002.1167222

M. Ebner and A. Zell, Evolving a task specific image operator. Evolutionary image analysis, signal processing and telecommunications: first European Workshop, EVOIASP, pp.74-89, 1999.

M. Ebner and A. Zell, Centering behavior with a mobile robot using monocular foveated vision, Robotics and Autonomous Systems, vol.32, issue.4, pp.207-218, 2000.
DOI : 10.1016/S0921-8890(99)00127-X

D. Floreano, T. Kato, D. Marocco, and E. Sauser, Coevolution of active vision and feature selection, Biological Cybernetics, vol.90, issue.3, pp.218-228, 2004.
DOI : 10.1007/s00422-004-0467-5

C. Gagné, M. Schoenauer, M. Parizeau, and M. Tomassini, Genetic programming, validation sets, and parsimony pressure of lecture notes in computer science, Proceedings of EuroGP 2006, pp.109-120, 2006.

F. Gomez and R. Miikkulainen, Incremental Evolution of Complex General Behavior, Adaptive Behavior, vol.5, issue.3-4, pp.3-4317, 1997.
DOI : 10.1177/105971239700500305

B. Horn and B. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.1651

I. Horswill, Polly: a vision-based artificial agent, Proceedings of the eleventh national conference on artificial intelligence (AAAI-93), pp.824-829, 1993.

L. Lacassagne, A. Manzanera, J. Denoulet, and A. Mérigot, High performance motion detection: some trends toward new embedded architectures for vision systems, Journal of Real-Time Image Processing, vol.51, issue.5, pp.127-146, 2009.
DOI : 10.1007/s11554-008-0096-7
URL : https://hal.archives-ouvertes.fr/hal-01131002

L. Cun, Y. Muller, U. Ben, J. Cosatto, E. Flepp et al., Off-road obstacle avoidance through end-to-end learning, Proceedings of the conference on neural information processing systems, pp.739-746, 2006.

L. Lorigo, R. Brooks, and W. Grimson, Visually-guided obstacle avoidance in unstructured environments, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, pp.373-379, 1997.
DOI : 10.1109/IROS.1997.649086

T. Low and G. Wyeth, Learning to avoid indoor obstacles from optical flow, Proceedings of the 2007 Australasian conference on robotics and automation, pp.1-10, 2007.

B. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proceedings of DARPA image understanding Workshop, pp.121-130, 1981.

D. Marocco and D. Floreano, Active vision and feature selection in evolutionary behavioral systems, From Animals Animat, vol.7, pp.247-255, 2002.

M. Martin, Evolving visual sonar: Depth from monocular images, Pattern Recognition Letters, vol.27, issue.11, pp.1174-1180, 2006.
DOI : 10.1016/j.patrec.2005.07.015
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.1774

J. Michels, A. Saxena, and A. Ng, High speed obstacle avoidance using monocular vision and reinforcement learning, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.593-600, 2005.
DOI : 10.1145/1102351.1102426
URL : http://ai.stanford.edu/~asaxena/rccar/ICML_ObstacleAvoidance.pdf

L. Muratet, S. Doncieux, Y. Brière, and M. J. , A contribution to vision-based autonomous helicopter flight in urban environments, Robotics and Autonomous Systems, vol.50, issue.4, pp.195-209, 2005.
DOI : 10.1016/j.robot.2004.09.017
URL : https://hal.archives-ouvertes.fr/hal-01185695

R. Nelson and J. Aloimonos, Obstacle avoidance using flow field divergence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.10, pp.1102-1106, 1989.
DOI : 10.1109/34.42840

G. Olague and C. Puente, Parisian evolution with honeybees for three-dimensional reconstruction, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.191-198, 2006.
DOI : 10.1145/1143997.1144030

O. Pauplin, J. Louchet, E. Lutton, D. La-fortelle, and A. , Evolutionary optimisation for obstacle detection and avoidance in mobile robotics, J Adv Comput Intell Intell Inform, vol.9, issue.6, pp.622-629, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00000495

C. Perez and G. Olague, Evolutionary learning of local descriptor operators for object recognition, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO '09, pp.1051-1058, 2009.
DOI : 10.1145/1569901.1570043

C. Reynolds, An evolved, vision-based model of obstacle avoidance behavior, Artificial Life III, pp.327-346, 1994.

A. Saxena, S. Chung, and A. Ng, 3-D Depth Reconstruction from a Single Still Image, International Journal of Computer Vision, vol.35, issue.8, pp.53-69, 2008.
DOI : 10.1007/s11263-007-0071-y

M. Suzuki, Enactive Robot Vision, Adaptive Behavior, vol.425, issue.4, 2007.
DOI : 10.1177/1059712308089183

L. Trujillo and G. Olague, Synthesis of interest point detectors through genetic programming, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.887-894, 2006.
DOI : 10.1145/1143997.1144151

L. Trujillo and G. Olague, Automated Design of Image Operators that Detect Interest Points, Evolutionary Computation, vol.2003, issue.8, pp.483-507, 2008.
DOI : 10.1109/TPAMI.2006.3

I. Ulrich and I. Nourbakhsh, Appearance-based obstacle detection with monocular color vision, Proceedings of AAAI conference, pp.866-871, 2000.

J. Walker, S. Garrett, and M. Wilson, Evolving Controllers for Real Robots: A Survey of the Literature, Adaptive Behavior, vol.11, issue.3, pp.179-203, 2003.
DOI : 10.1177/1059712303113003

P. Whigham, Grammatically-based genetic programming In: Proceedings of the workshop on genetic programming: from theory to real-world applications, pp.33-41, 1995.

R. Azencott, Simulated Annealing: Parallelization Techniques, 1992.

A. Biancardi and M. Segrovia-martinez, Adaptative segmentation of MR axial brain images using connected components, International Workshop on Visual Form, Capri, pp.295-302, 2001.

G. Blelloch, Vector Models for Data-Parallel Computing, 1990.

A. Caplier, L. Bonnaud, and J. Chassery, Robust fast extraction of video objects combining frame differences and adaptive reference image, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), pp.785-788, 2001.
DOI : 10.1109/ICIP.2001.958611
URL : https://hal.archives-ouvertes.fr/hal-00379553

M. Ceccarelli and A. Petrosino, A parallel fuzzy scale-space approach to the unsupervised texture separation, Pattern Recognition Letters, vol.23, issue.5, pp.557-567, 2002.
DOI : 10.1016/S0167-8655(01)00151-9

F. S. Cohen and D. B. Cooper, Simple parallel hierarchical and relaxation algorithms for segmenting non-causal Markovian random fields, IEEE PAMI, vol.9, issue.2, pp.195-219, 1987.

X. Cray, J. Denoulet, and A. Mérigot, CUDA: http://www.nvidia.com/cuda 12 System on chip evolution of a SIMD architecture for image processing, In: Computer Architecture and Machine Perception, vol.11, pp.12-16, 2003.

J. Denoulet and A. Mérigot, Evaluation of a SIMD architecture dedicated to image processing, Global Signal Process, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00622249

J. Denoulet, G. Mostafaoui, L. Lacassagne, and A. Mérigot, Implementing Motion Markov Detection on General Purpose Processor and Associative Mesh, Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05), pp.288-293, 2005.
DOI : 10.1109/CAMP.2005.31
URL : https://hal.archives-ouvertes.fr/hal-00622245

K. Diefendorff, P. K. Dubeyn, R. Hochsprung, and H. Scales, AltiVec extension to PowerPC accelerates media processing, IEEE Micro, vol.20, issue.2, 2000.
DOI : 10.1109/40.848475

B. Ducourthial and A. Merigot, Parallel asynchronous computations for image analysis, Proceedings of the IEEE, vol.90, issue.7, pp.1218-1229, 2002.
DOI : 10.1109/JPROC.2002.801454

B. Ducourthial, G. Constantinescu, and A. Merigot, Implementing image analysis with a graph-based parallel computing model. Computing. In: Supplementum, GbR'97, Workshop on Graph based Representation, pp.111-121, 1997.

P. Dudek, An asynchronous cellular logic network for trigger-wave image processing on fine-grain massively parallel arrays, IEEE Transactions on Circuits and Systems II: Express Briefs, vol.53, issue.5, pp.354-358, 2006.
DOI : 10.1109/TCSII.2006.869916

D. Dulac, A. Merigot, and S. Mohammadi, Associative meshes: a new parallel architecture for image analysis applications. In: Computer Architecture and Machine Perception, pp.393-399, 1993.

R. Forchheimer and A. Astrøm, Near-sensor image processing: a new paradigm, IEEE Transactions on Image Processing, vol.3, issue.6, pp.736-746, 1994.
DOI : 10.1109/83.336244

D. Hillis, W. Tucker, and L. W. , The CM-5 Connection Machine: a scalable supercomputer, Communications of the ACM, vol.36, issue.11, pp.31-40, 1993.
DOI : 10.1145/163359.163361

B. Galilee, F. Mamalet, M. Renaudin, and P. Y. Coulon, Parallel Asynchronous Watershed Algorithm-Architecture, IEEE Transactions on Parallel and Distributed Systems, vol.18, issue.1, pp.44-56, 2007.
DOI : 10.1109/TPDS.2007.253280
URL : https://hal.archives-ouvertes.fr/hal-00144521

V. Gies and T. Bernard, Increasing Interconnection Network Connectivity for Reducing Operator Complexity in Asynchronous Vision Systems, International Conference on Discrete Geometry for Computer Imagery, pp.1-10, 2005.
DOI : 10.1007/978-3-540-31965-8_1

V. Gies and T. M. Bernard, Tree extension of micro-pipelines for mixed synchronous-asynchronous implementation of regional image computations, Sensors and Camera Systems for Scientific and Industrial Applications VI, 2005.
DOI : 10.1117/12.601852

J. Klein, F. Lemonnier, M. Gauthier, and R. Peyrard, Hardware implementation of the watershed zone algorithm based on a hierarchical queue structure, IEEE Workshop on Nonlinear Signal and Image Processing, 1995.

P. Lalande and P. Bouthemy, A statistical approach to the detection and tracking of moving objects in an image sequence, Eur Signal Process Conf, pp.947-950, 1990.

C. Limousin, J. Sebot, A. Vartanian, and N. Drach, Architecture optimization for multimedia application exploiting data and thread-level parallelism, Journal of Systems Architecture, vol.51, issue.1, pp.15-27, 2005.
DOI : 10.1016/j.sysarc.2004.06.002
URL : https://hal.archives-ouvertes.fr/hal-01195861

F. Lohier, L. Lacassagne, and P. Garda, A New Methodology to Optimize DMA Data Caching: Application toward the Real-time Execution of an MRF-based Motion Detection Algorithm on a multi-processor DSP: A robust and computationally efficient motion detection algorithm based on Sigma?Delta background estimation, International Conference on Signal Processing Applications and Technology Proceedings Indian Conference on Computer Vision, Graphics and Image Processing, 1999.

A. Mérigot, Associative nets: a graph-based parallel computing model, IEEE Transactions on Computers, vol.46, issue.5, pp.558-571, 1997.
DOI : 10.1109/12.589222

A. Mérigot and B. Zavidovique, IMAGE ANALYSIS ON MASSIVELY PARALLEL COMPUTERS: AN ARCHITECTURAL POINT OF VIEW, International Journal of Pattern Recognition and Artificial Intelligence, vol.06, issue.02n03, pp.387-399, 2002.
DOI : 10.1142/S0218001492000230

P. Nadrag, A. Manzanera, and N. Burrus, Smart retina as a contourbased visual interface, In: ACM Distributed Smart Cameras Workshop, issue.06, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01222683

J. Sébot and N. Drach-temam, Memory Bandwidth: The True Bottleneck of SIMD Multimedia Performance on a Superscalar Processor, Proceedings of the 5th International Euro-Par Conference on Parallel Processing, pp.439-447, 2001.
DOI : 10.1007/3-540-44681-8_63

S. Piskorski, L. Lacassagne, S. Bouaziz, and D. Etiemble, Customizing CPU Instructions for Embedded Vision Systems, 2006 International Workshop on Computer Architecture for Machine Perception and Sensing, 2006.
DOI : 10.1109/CAMP.2007.4350352

S. Piskorski, L. Lacassagne, M. Kieffer, and D. Etiemble, Efficient floating point interval processing for embedded systems and applications, Int Symp Sci Comput Comput Arithmetic Valid Numer, 2006.

A. Rodriguez-vazquez, G. Linan-cembrano, L. Carranza, E. Roca-moreno, R. Carmona-galan et al., ACE16k: The Third Generation of Mixed-Signal SIMD-CNN ACE Chips Toward VSoCs, IEEE Transactions on Circuits and Systems I: Regular Papers, vol.51, issue.5, pp.851-863, 2004.
DOI : 10.1109/TCSI.2004.827621

I. Tera-scale-project, B. Stamon, and G. , Bilevel processing of multilevel images, Proceedings PRIP'81, 1449.