Journal papers by N. Petkov since 1992

The articles marked by TOP-1% belong to the group of 1% most cited papers in computer science or engineering according to the criteria of the Institute for Scientific Information.

2019

• Manuel Lopez Antequera, Maria Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov: Place and Object Recognition by CNN-Based COSFIRE Filters. IEEE Access, Vol. 7 (1), pages 66157-66166, DOI: 10.1109/ACCESS.2019.2918267, 2019. [open access]
• Ahmad Alsahaf, George Azzopardi, Bart Ducro, Egiel Hanenberg, Roel F Veerkamp, Nicolai Petkov: Estimation of Muscle Scores of Live Pigs Using a Kinect Camera. IEEE Access, Vol. 7, pages 52238-52245, DOI 10.1109/ACCESS.2019.2910986, 2019. [open access]
• Maria Leyva-Vallina, Nicola Strisciuglio, Manuel Lopez Antequera, Radim Tylecek, Michael Blaich, Nicolai Petkov: TB-Places: A Data Set for Visual Place Recognition in Garden Environments. IEEE Access, Vol. 7 (1), pages 52277-52287, DOI 10.1109/ACCESS.2019.2910150, 2019. [open access]
• Nicola Strisciuglio, Mario Vento, Nicolai Petkov: Learning sound representations using trainable COPE feature extractors. Pattern Recognition, 2019.
• M Mohammadi, N Petkov, K Bunte, RF Peletier, F-M Schleif: Globular cluster detection in the gaia survey. Neurocomputing, 2019.
• Chenyu Shi, Joost M Meijer, Jiapan Guo, George Azzopardi, Gilles FH Diercksr, Enno Schmidt, Detlef Zillikens, Marcel F Jonkman, Nicolai Petkov: Detection of u-serrated patterns in direct immunofluorescence images of autoimmune bullous diseases by inhibition-augmented COSFIRE filters. International journal of medical informatics, Vol. 122, pages 27-36, 2019.
• Alessia Saggese, Nicola Strisciuglio, Mario Vento, Nicolai Petkov: Learning skeleton representations for human action recognition. Pattern Recognition Letters, Vol. 118, pages 23-31, 2019.
• Jiapan Guo, George Azzopardi, Chenyu Shi, Nomdo M Jansonius, Nicolai Petkov: Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening. IEEE Access, Vol. 9, pages 8527-8541, DOI 10.1109/ACCESS.2018.2890544, 2019. [pdf] [open access]

2018

• Ahmad Alsahaf, George Azzopardi, Bart Ducro, Egiel Hanenberg, Roel F Veerkamp, Nicolai Petkov: Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest. Journal of animal science, Vol. 96, Issue 12, pages 4935-4943, 2018.
• Marcos del Pozo Banos, Carlos M Travieso, Kate Loxton, Nicolai Petkov, Damon Berridge, Keith Lloyd, Caroline Jones, Sarah Spencer, Ann John: Combining Artificial Neural Networks, Routine Health Records and Suicide Risk Estimation. International Journal of Population Data Science, Vol. 3, Issue 4, 2018.
• Eirini C Schiza, Theodoros Kyprianou, Nicolai Petkov, Christos N Schizas: Proposal for an eHealth Based Ecosystem Serving National Healthcare. IEEE journal of biomedical and health informatics, 2018.
• Marcos DelPozo-Banos, Ann John, Nicolai Petkov, Damon Mark Berridge, Kate Southern, Keith LLoyd, Caroline Jones, Sarah Spencer, Carlos Manuel Travieso: Using neural networks with routine health records to identify suicide risk: feasibility study. JMIR mental health, Vol. 5, Issue 2, 2018, pages: e10144.

2017

• L. Fernandez-Robles, G. Azzopardi, E. Alegre, N. Petkov: Machine-vision-based identification of broken inserts in edge profile milling heads. J. of Robotics and Computer-Integrated Manufacturing 44 (2017) 276-283.
• B. Gecer, G. Azzopardi, N. Petkov: Color-blob-based COSFIRE filters for object recognition. J. Image and Vision Computing 57 (2017): 165-174. Available online 15 Nov 2016, on-line

2016

• G. Azzopardi, N. Petkov: Special issue on selected papers from CAIP 2015 (editorial). Machine Vision and Applications (2016) 27: 1115. doi:10.1007/s00138-016-0813-3. on-line
• Guo, J., Shi, C., Azzopardi, G. et al. Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition. Machine Vision and Applications (2016) 27: 1197-1211. doi:10.1007/s00138-016-0777-3. View on-line
• Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2016). Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Machine Vision and Applications (2016) 27: 1137-1149. DOI: 10.1007/s00138-016-0781-7. View on-line
• Emerencia, A. C., Krieke, L. V. D., Bos, E. H., de Jonge, P., Petkov, N., & Aiello, M. (2016). Automating Vector Autoregression on Electronic Patient Diary Data. IEEE Journal of Biomedical and Health Informatics, 20(2), 631-643. DOI: 10.1109/JBHI.2015.2402280
• F-M. Schleif, B. Hammer, J. G. Monroy, J. G. Jimenez, J-L. Blanco-Claraco, M. Biehl, N. Petkov: Odor recognition in robotics applications by discriminative time-series modeling. Pattern Analysis and Applications 19 (1) 207-220, Feb. 2016. [on-line].

2015

• E.C. Schiza, K.C. Neokleous, N. Petkov and C.N. Schizas: A patient centered electronic health: eHealth system development. Technology and Health Care Journal, 23 (4): 509-522, 2015.
  http://www.ncbi.nlm.nih.gov/pubmed/26409913, doi: 10.3233/THC-150919.
• P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio, M. Vento: Audio surveillance of roads: A system for detecting anomalous sounds. IEEE Trans. on Intelligent Transportation Systems, PP (99): 1-10, 2015.
  doi:10.1109/TITS.2015.2470216.
• I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, N. Petkov: MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Systems with Applications, 42 (19): 6578-6585, 2015.
• P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio, M. Vento: Reliable detection of audio events in highly noisy environments. Pattern Recognition Letters, Available online 9 July 2015, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2015.06.026.
• G. Azzopardi, N. Strisciuglio, M. Vento, N. Petkov: Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical image analysis, 19 (1): 46-57, 2015. TOP-0.1%

2014

• E. Gheorghiu, F. A. A. Kingdom, N. Petkov: Contextual modulation as de-texturizer. Vision Research, 104, p. 12-23, 2014.
• G. Azzopardi, N. Petkov: Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models. Frontiers in computational neuroscience, 8, article nr. 80, 9 pages, 2014.
• G. Azzopardi, A. Rodriguez-Sanchez, J. Piater, N. Petkov: A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection. Plos one, 9 (7), article nr. 98424, 13 pages, 2014.

2013

• E. Alegre, M. Biehl, N. Petkov, L. Sachez: Assessment of accrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ. Computer Methods and Programs in Biomedicine, 111 (3): 525-536, 2013.
  
• A. C Emerencia, L. van der Krieke, S. Sytema, N. Petkov, M. Aiello: Generating personalized advice for schizophrenia patients. Artificial Intelligence in Medicine, 58 (1): 23-36, 2013. [doi:10.1016/j.artmed.2013.01.002], [on-line].
• G. Azzopardi, N. Petkov: Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 35 (2): 490-503, 2013. TOP-1%
  [doi: 10.1109/TPAMI.2012.106], [pdf © IEEE or pdf © IEEE]
• G. Azzopardi, N. Petkov: Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recognition Letters, 34 (8): 922-933, 2013.
  http://dx.doi.org/10.1016/j.patrec.2012.11.002
• I.E. Giotis, K. Bunte, N. Petkov, and M. Biehl: Adaptive Matrices and Filters for Color Texture Classification. J. Mathematical Imaging and Vision, 47 (1-2): 79-92, 2013.
• I.E. Giotis, M. Visser, M.F. Jonkman, N. Petkov: Discriminative power of visual attributes in dermatology. Skin Research and Technology, 19 (1): e123-e13, 2013.

2012

• G. Papari, P. Campisi, N. Petkov: New families of Fourier eigenfunctions for steerable filtering. IEEE Trans. on Image Processing, 21 (6), 2012, 2931-2943.
  [doi: 10.1109/TIP.2011.2179060]
• G. Azzopardi, N. Petkov: A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biological Cybernetics, 106 (3), 2012, 177-189. TOP-1%
  [pdf], [doi: 10.1007/s00422-012-0486-6]
• I. E. Giotis, N. Petkov: Cluster-based adaptive metric classification, Neurocomputing, 81, 2012, 33-40.

2011

• K. Bunte, M. Biehl, M.F. Jonkman, and N. Petkov: Learning effective color features for content based image retrieval in dermatology. Pattern Recognition, 44 (9), 2011, 1892-1902.
  [doi: 10.1016 j.patcog.2010.10.024].
• G. Papari, N. Petkov: An improved model for surround suppression by steerable filters and multilevel inhibition with application to contour detection. Pattern Recognition, 44 (9), 2011, 1999-2007.
  [doi: 10.1016/j.patcog.2010.08.013].
• N. Petkov, X. Jiang: CAIP-Computer Analysis of Images and Patterns (Editorial). Pattern Recognition, 44 (9), 2011, 1841-1841.
  [doi: 10.1016/j.patcog.2011.03.017].
• J.A.J. van der Krieke, A.C. Emerencia, S. Sytema, M. Aiello, N. Petkov, and D. Wiersma: An online self-management tool for people suffering from psychosis. Schizophrenia Bulletin, 37 (Supplement: 1), 2011, 283-283.
• G. Papari and N. Petkov: Edge and line oriented contour detection: State of the art. Image and Vision Computing, 29 (2-3), 2011, 79-103.
  [bib], [pdf © Elsevier], [doi: 10.1016/j.imavis.2010.08.009]. TOP-0.1%.

2010

• H.H.W.J. Bosman, N. Petkov, and M.F. Jonkman: Comparison of Color Representations for Content Based Image Retrieval in Dermatology. Skin Research and Technology, 16 (1), 2010, 109-113.
  [for a copy of the pdf file, send me an email request] [doi: 10.1111/j.1600-0846.2009.00405.x]

2009

• G. Papari and N. Petkov: Continuous Glass Patterns for Painterly Rendering. IEEE Trans. on Image Processing, 18 (3), 2009, 652-664.

2008

• G. Papari and N. Petkov: Adaptive pseudo-dilation for Gestalt edge grouping for contour detection. IEEE Trans. on Image Processing, 17 (10), 2008, 1950-1962.
  [bibTex], [pdf © IEEE], [DOI 10.1109/TIP.2008.2002306]
• E. Alegre, M. Biehl, N. Petkov and L. Sanchez: Automatic classification of the accrosome status of boar spermatozoa using digital image processing and LVQ, Computers in Biology and Medicine, 38 (4), 2008, 461-468.
  [bibTex], [pdf], [DOI 10.1016/j.compbiomed.2008.01.005]
  if you want a copy of the pdf file as it appeared in CBM, please send me an email request

2007

• N. Petkov and E. Subramanian: Motion detection, noise reduction, texture suppression and contour enhancement by spatiotemporal Gabor filters with surround inhibition, Biological Cybernetics, 97 (5-6), 2007, 423-439.
  [bibTex], [pdf © Springer; the original publication is available at www.springerlink.com], [DOI 10.1007/s00422-007-0182-0], [demo].
• G. Papari, N. Petkov and P. Campisi: Artistic edge and corner enhancing smoothing, IEEE Trans. on Image Processing, 16 (10), 2007, 2449-2662.
  [bibTex], [pdf © IEEE], [DOI 10.1109/TIP.2007.903912], [supplementary illustrations]
• G. Papari, P. Campisi, N. Petkov and A. Neri: A biologically motivated multiresolution approach to contour detection, EURASIP Journal on Advances in Signal Processing, Vol. 2007, Article ID 71828, 28 pages, 2007.
  [bibTex], [pdf © 2007 G. Papari, P. Campisi, N. Petkov and A. Neri], [DOI 10.1155/2007/71828], [supplementary illustrations]

2006

• A. Ghosh and N. Petkov: Effect of high curvature point deletion on the performance of two contour based shape recognition algorithms, International Journal of Pattern Recognition and Artificial Intelligence, 20 (6), 2006, 913-924.
  [bibTex], [pdf © World Sci. Publ. Co.] TOP-20%
• L. Sanchez, N. Petkov and E. Alegre: Statistical approach to boar semen evaluation using intracellular intensity distribution of head images, Cellular and Molecular Biology, 52 (6), 2006, 38-43.
  [bibTex], [pdf © C.M.B. Edition], [DOI 10.1170/T736] TOP-10%

2005

• A. Ghosh and N. Petkov: A cognitive evaluation procedure for contour based shape descriptors, Int. Journal of Hybrid Intelligent Systems, 2 (4), 2005, 237-252.
  [bibTex], [pdf © IOS Press, AKI and the authors], [ICR test database].
• A. Ghosh and N. Petkov: Robustness of shape descriptors to incomplete contour representations, IEEE Trans. on Pattern Analysis and Machine Intelligence, 27 (11), 2005, 1793-1804.
  [bibTex], [pdf © IEEE], [ICR test database]. TOP-10%

2004

• C. Grigorescu, N. Petkov and M. A. Westenberg: Contour and boundary detection improved by surround suppression of texture edges, Image and Vision Computing, 22 (8), 2004, 609-622.
  [bibTex], [pdf , html, © Elsevier], [DOI 10.1016/j.imavis.2003.12.004]. TOP-2%

2003

• C. Grigorescu and N. Petkov: Distance sets for shape filters and shape recognition, IEEE Trans. on Image Processing, 12 (10), 2003, 1274-1286.
  [bibTex], [pdf © IEEE] TOP-2%
• C. Grigorescu, N. Petkov and M. A. Westenberg: Contour detection based on nonclassical receptive field inhibition, IEEE Trans. on Image Processing, 12 (7), 2003, 729-739.
  [bibTex], [pdf © IEEE], [demo and database]. TOP-1%
• N. Petkov and M. A. Westenberg: Suppression of contour perception by band-limited noise and its relation to non-classical receptive field inhibition, Biological Cybernetics, 88, 2003, 236-246.
  [bibTex], [pdf © Springer; the original publication is available at www.springerlink.com], [DOI 10.1007/s00422-002-0378-2], [demo], TOP-2%
• C. Grigorescu, N. Petkov and M.A. Westenberg: The role of non-CRF inhibition in contour detection, Journal of Computer Graphics, Visualization, and Computer Vision (WSCG), 11 (2), 2003, 197-204.
  [bibTex], [pdf], [demo],

2002

• S.E. Grigorescu, N. Petkov and P. Kruizinga: Comparison of texture features based on Gabor filters, IEEE Trans. on Image Processing, 11 (10), 2002, 1160-1167.
  [bibTex], [pdf © IEEE] TOP-1%

2001

• Th. Lippert, N. Petkov, P. Palazzari and K. Schilling: Hyper-systolic matrix multiplication, Parallel Computing, 27 (6), 2001, 737-759.
  [bibTex], [pdf © Elsevier]

2000

• P. Kruizinga and N. Petkov: Computational model of dot-pattern selective cells, Biological Cybernetics, 83 (4), 2000, 313-325.
  [bibTex], [pdf © Springer; the original publication is available at www.springerlink.com], [demo].

1999

• P. Kruizinga and N. Petkov: Non-linear operator for oriented texture, IEEE Trans. on Image Processing, 8 (10), 1999, 1395-1407.
  [bibTex], [pdf © IEEE] TOP-2%

1997

• N. Petkov and P. Kruizinga: Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells, Biological Cybernetics, 76 (2), 1997, 83-96.
  [bibTex], [pdf © Springer; the original publication is available at www.springerlink.com] TOP-2%

1995

• N. Petkov: Biologically motivated computationally intensive approaches to image pattern recognition, Future Generation Computer Systems, 11 (4-5), 1995, 451-465.
  [bibTex], [pdf, html © Elsevier], [DOI 10.1016/0167-739X(95)00015-K].TOP-10%.

1994

• T. Lourens, N. Petkov and P. Kruizinga: Large scale natural vision simulations, Future Generation Computer Systems, 10, 1994, 351-358.

1993

• N. Petkov: Fuzzy number subtraction convolution on the CM-2, International Journal of Modern Physics, 4 (1), 1993, 181-196. (Available in book form: Th. Lippert, K. Schilling and P. Ueberholz (eds.): Science on the Connection Machine (Singapore: World Scientific Publ., 1993), pp.181-196.)

1992

• T. D. Dontje, T. Lippert, N. Petkov and K. Schilling: Statistical analysis of simulation-generated time series: Systolic vs. semi-systolic correlation on the Connection Machine, Parallel Computing, 18, 1992, 575-588.
• T. Lippert, N. Petkov and K. Schilling: Quark propagator on the Connection Machine, Parallel Computing, 18, 1992, 1291-1299.

Last changed: 2019-06-27.