Publications

J. Humberto Sossa A.

List of recent publications:

A PDF version of most of these publications can be e-mailed to you upon request.

2017:

Journal papers:

Y. Campos, H. Sossa, G. Pajares. Comparative analysis of texture descriptors in maize fields with plants, soil and object discrimination. Precision Agriculture 1-19.

E. Zamora and H. Sossa. Dendrite Morphological Neurons Trained by Stochastic Gradient Descent. Neurocomputing. doi.org/10.1016/j.neucom.2017.04.044.

E. Ortiz, M. Mejía, H. Sossa. Using Pulse Coupled Neural Networks to improve image filtering contamined with Gaussian Noise. Computación y Sistemas 21(2):381-395. doi: 10.13053/CyS-21-2-2742.

E. Cabrera and H. Sossa. Generating exponentially stable states for a Hopfield Neural Network. Neurocomputing. https://doi.org.10.1016/j.neucom.2017.08.032

F. Arce, E. Zamora, H. Sossa and R. Barrón. Differential Evolution Training Algorithm for Dendrite Morphological Neural Networks. To appear in Soft Computing.

Book Chapters:

H. Sossa and H. Sánchez. Computing the Number of Bubbles and Tunnels of a 3-D Binary Object. LNCS 10163. A. Fred et al. (Eds.). Pattern Recognition Applications and Methods. 5th International Conference, ICPRAM 2016, Rome, Italy, February 24-26, 2016, Revised Selected Papers. Springer International Publishing. Pp. 194-211.

A. Peña, L. A. Cárdenas and H. Sossa. Chapter 3: A Landscape of Learning Analytics: An Exercise to Highlight the Nature of an Emergent Field. Learning Analytics: Fundaments, Applications and Trends. Studies in Systems, Decision and Control 94, A. Peña (Editor). Springer-Verlag.


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Conference Papers:

L. Delgado, G. Hernandez, E. Zamora, H. Sossa, A. Barreto, F. Ramos and R. Reyes. Classification of the Estrous Cycle through Texture and Shape Features. 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017). November 27-December 1, 2017. Hawaii, USA.

F. Arce, E. Zamora, G. Hernández, H. Sossa (2017). Efficient lane detection based on artificial neural networks. UDMS-SDSC_2017. 2nd International Conference on Smart Data and Smart Cities. October 4–6, 2017, Puebla, Mexico.

F. Furlán, E. Rubio, H. Sossa and V. Ponce (2017). Humanoid Robot Hierarchical Navigation Using Petri Nets and Fuzzy Logic. SICE 2017 Annual Conference. Kanazawa, Japan, September 19-22, 2017.

I. Chavez, H. Sanchez and H. Sossa (2017). Hyperspectral image compression using chain codes. Fifth International Conference on Advances in Computing, Communication and Information Technology (CCIT 2017). Zurich, Switzerland during 02-03 September, 2017. Pp. 93-98.

S. Valadez, H. Sossa and R. Santiago (2017). The Step Size Impact on the Computational Cost of Spiking Neuron Simulation. Computing Conference 2017, London, UK, July 18-20, 2017. Pp. 722-728.

F. Arce, E. Zamora and H. Sossa. Dendrite Ellipsoidal Neuron. IJCNN 2017. Anchorage, Alaska, USA, May 14-19, 2017.

E. de la Rosa, W. Yu and H. Sossa. Fuzzy Modeling from Black-Box Data with Deep Learning Techniques. Accepted 14th International Symposium on Neural Networks (ISNN 2017). Sapporo, Hokkaido, Japan. June 21-26, 2017.

G. Hernández. E. Zamora, H. Sossa. Comparing Deep and Dendrite Neural Networks: A case Study. Mexican Conference in Pattern Recognition (MCPR 2017). Huatulco, México, Junio 21-24, 2017.

S. Valadez, J. González and H. Sossa. Efficient Pattern Recognition Using the Frequency Response of a Spiking Neuron. Mexican Conference in Pattern Recognition (MCPR 2017). Huatulco, México, Junio 21-24, 2017.

J. U. González, E. Rubio and H. Sossa. Visión por Computadora en un Robot Móvil Tipo Oruga. COMIA 2017.

F. Furlán, E. Rubio, H. Sossa and V. Ponce. Navegación jerárquica de un robot humanoide usando redes de Petri y lógica difusa. COMIA 2017.

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Under review:

R. Rodríguez, E. Torres, H. Sossa and Y. Garcés. A new stopping criterion for the mean shift iterative algorithm. Its use in image segmentation. Journal of Intelligent & Robotic Systems.

S. Valadez, H. Sossa and R. Santiago. On the Accuracy and Computational Cost of Spiking Neuron Implementation. IEEE Transactions on Neural Networks and Learning Systems.

A. Luviano, E. Amaya, O. Gutiérrez, H. Sossa. Design and construction of a robotic platform for 3D reconstruction through an embedded processing system. IEEE Latin America Transactions.

J. M. Anteli, B. Gudiño, L. E. Falcón, G. Sanchez and H. Sossa. Evaluating dendrite morphological neural networks in the recognition of voluntary movements from electroencephalographic signals.

A. Sancen, R. Santiago, H. Sossa, F. J. Pérez, J. J. Martínez, J. A. Padilla (2017). Quantitative evaluation of binary digital region asymmetry with application to skin lesion detection. BMC Medical Informatics and Decision Making.

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2016:

Journal papers:

Y. Campos, H. Sossa, Gonzalo Pajares. Spatio-temporal analysis for obstacle detection in agricultural videos. Applied Soft Computing 45:86-97.

R. Ocampo, G. Sanchez, M. A. de Luna, R. Vega, L. E. Falcón, and H. Sossa. Using PCA and Logistic Regression to improve feature selection of DNA Microarray Data. Journal of Intelligent Data Analysis 20:S53-S67.

V. Osuna, E. Cuevas, D. Oliva, H. Sossa and M. Pérez. A bio-inspired evolutionary algorithm: Allostatic Optimization. International Journal of Bio-Inspired Computation 8(3):154-169.

Book Chapters:

V. Osuna, V. Zuñiga, D. Oliva, E. Cuevas, H. Sossa. Image Segmentation Using an Evolutionary Method Based on Allostatic Mechanisms. Studies in Computational Intelligence Vol. 630: Image Feature Detectors and Descriptors: Foundations and Applications. Eds. Ali Ismail and M. Hassaballah. Pp. 255-279.

Conference Papers:

H. Sossa (2016). On the Computation of the Number of Bubbles and Tunnels of a 3-D Binary Object, 5th International Conference on Pattern Recognition Applications and Methods, Roma, Italia, February 24-26, 2016. Pp. 17-23.

H. Sossa, A. Carreon and R. Santiago (2016). Training a Multilayered Perceptron to Compute the Euler Number of a 2-D Binary Image. MCPR 2016. LNCS 9703. Pp. 44-53.

R. Ocampo, G. Sanchez, L. E. Falcon and H. Sossa (2016).
Automatic Construction of Radial-Basis Function Networks through an Adaptive Partition Algorithm. MCPR 2016. LNCS 9703. Pp. 198-20'7.

B. Gudiño, H. Sossa, G. Sanchez and J. M. Antelis (2016). Classication of Motor States from Brain Rhythms Using Lattice Neural Networks. MCPR 2016. LNCS 9703. Pp. 303-312.

H. Sossa, A. Carreón, E. Guevara, R. Santiago (2016). Computing the 2-D Image Euler Number by an Artificial Neural Network, International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada. Pp. 1609-1616.

Y. Campos, E. Rodner, J. Denzler, H. Sossa, and G. Pajares (2016). Vegetation segmentation in cornfield images using Bag of Words. Advanced Concepts for Intelligent Vision Systems (ACIVS 2016). Museo Storico Della Citta' Di Lecce, Italy. Oct. 24-27, 2016. LNCS 10016. Pp. 193-204.

H. Sossa, A. Carreón, R. Santiago, E. Bribiesca, A. Petrilli (2016). Efficient Computation of the Euler Number of a 2-D Binary of Image. Mexican International Conference on Artificial Intelligence (MICAI 2016), Cancún, Quintana Roo, México, Oct. 23-29, 2016. Pp.

H. Sossa, A. Carreón, R. Santiago, A. Petrilli (2016). Support Vector Machines Applied to 2-D Binary Image Euler Number Computation, International Conference on Mechatronics, Electronics and Automotive Engineering 2016 (ICMEAE 2016), Cuernavaca, Morelos, Mexico. Pp 3-8.

F. Arce, E. Zamora, H. Sossa and R. Barrón (2016). Dendrite Morphological Neural Networks Trained by Differential Evolution. 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, December 6-9, 2016. Pp.

E. Zamora and H. Sossa (2016). Dendrite Morphological Neurons Trained by Stochastic Gradient Descent. 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece, December 6-9, 2016. Pp.

2015:

Journal papers:

R. Vega, G. Sánchez, L. E. Falcón, H. Sossa and E. Guevara (2015). Retinal vessel extraction using Lattice Neural Networks with dendritic processing. Computers in Biology and Medecine 58:20-30.

V. Osuna, E. Cuevas, D. Oliva, H. Sossa and M. Pérez (2015). A bio-inspired evolutionary algorithm: Allostatic Optimization. International Journal of Bio-Inspired Computation.

R. Santiago, S. Valadez, H. Sossa, D. Asael, M. Ornelas (2015). A study of the Associative Pattern Classifier Method for Multi-Class Processes. Journal of Optoelectronics and Advanced Material 17(5-6):713-719. May 2015.

C. Aguilar, H. Sossa and S. Suárez. A Backstepping based procedure with saturation functions to control the PVTOL system. Nonlinear Dynamics. DOI 10.1007/s11071-015-2400-y

R. Ocampo, G. Sanchez, M. A. de Luna, R. Vega, L. E. Falcón, and H. Sossa. Using PCA and Logistic Regression to Improve Feature Selection of DNA Microarray Data. Journal of Intelligent Data Analysis 20:S53-S67.

Conference Papers:

L. Ojeda, R. Vega, L. E. Falcon, G. Sanchez-Ante, H. Sossa, and J. M. Antelis (2015). Classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing. 7th Mexican Conference on Pattern Recognition (MCPR 2015). LNCS 9116: 23-32. Springer Verlag.

C. Garibay, G. Sanchez-Ante, L. E. Falcon-Morales, and H. Sossa (2015). Modified Binary Inertial Particle Swarm Optimization for Gene Selection in DNA Microarray Data. 7th Mexican Conference on Pattern Recognition (MCPR 2015). LNCS 9116: 271-281. Springer Verlag

S. Valadez, H. Sossa, R. Santiago-Montero, and E. Guevara (2015). Encoding polysomnographic signals into spike firing rate for sleep staging. 7th Mexican Conference on Pattern Recognition (MCPR 2015). LNCS 9116: 282-291. Springer Verlag.

H. Sossa (2015). On the number of holes of a 2-D binary object. International Conference on Machine Vision (MVA 2015). Tokyo, Japan, May 18-22, 2015.

2014:

Journal papers:

A. Peña, H. Sossa, I. Méndez (2014). Activity theory as a framework for building adaptive E-Learning Systems: A case to provide empirical evidence. Computers in Human Behavior. 30:131-145. 

H. Sossa and E. Guevara (2014). Efficient Efficient training for dendrite morphological neural networks. Neurcomputing 131:132-142. 

H. Sossa, E. Rubio, A. Peña, E. Cuevas and R. Santiago (2014). Alternative Formulations to Compute the Binary Shape Euler Number. IET-Computer Vision. 8(3):171-181.

R. Santiago, M. A. López, H. Sossa (2014). Digital Shape Compactness measure by means of Perimeter Ratios. Electronics Letters 50(3):171-173.

Confrence Papers:

H. Sossa, G. Cortés and E. Guevara (2014). New Radial Basis Function Neural Network Architecture for Pattern Classification: First Results. 19th Iberoamerican Congress on Pattern Recognition (CIARP 2014). Puerto Vallarta, México, 2-5 de noviembre de 2014. LNCS 8827, pp. 706-713.

R. Ocampo, M. A. de Luna, R. Vega, G. Sanchez-Ante, L. E. Falcon-Morales and H. Sossa (2014). Pattern Analysis in DNA Microarray Data through PCA-Based Gene Selection. 19th Iberoamerican Congress on Pattern Recognition (CIARP 2014). Puerto Vallarta, México, 2-5 de noviembre de 2014. LNCS 8827, pp. 532-539.

G. Hernández, H. Sossa, E. Rubio, J. A. Escareño (2014). Identificación de objetos mediante un quadrotor equipado con una cámara y redes neuronales. XVI Congreso Latinoamericano de Control Automático (CLCA 2014). Octubre 14-17, Cancún, Quintana Roo, México. Pp. 1475-1480.

2013:

Books:

H. Sossa. Visión Artificial: Rasgos descriptores para el reconocimiento de objetos. Editorial RAMA. ISBN 978-84-9964-142-3.

Journal papers:

V. Osuna, E. Cuevas, H. Sossa (2013). A Comparison of Nature Inspired Algorithms for Multi-Threshold Image Segmentation. Expert Systems with Applications 40:1213-1219.

J. F. Serrano, C. Avilés, J. Villegas, H. Sossa (2013). Self Organizing Natural Scene Image Retrieval. Expert Systems with Applications 40:2398-2409.

H. Sánchez, H. Sossa, Ulf-Dietrich, E. Bribiesca (2013). The Euler-Poincaré Formula Through Contact Surfaces of Voxelized Objects. JART 11:58-78.

L. E. Gómez, H. Sossa, R. Barrón, F. Cuevas, J. F. Jiménez (2013). Redes neuronales dinámicas aplicadas a la recomendación musical optimizada. Polibits 45:83-92.

B. A. Garro, H. Sossa, R. A. Vázquez (2013). Diseño automatizado de redes neuronales mediante el uso del algoritmo de evolución diferencial (ED). Polibits 46:13-27.

E. Cuevas, V. Osuna, H. Sossa (2013). Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Applied Soft Computing 13:3047-3059.

H. Sossa, R. Santiago, E. Rubio, M. Pérez (2013). Computing the Euler Number of a Binary Image based on a Vertex Codification. JART 11:360-370.

D. Zaldivar, E. Cuevas, M. A. Pérez, H. Sossa, J. G. Rodríguez, E. O. Palafox (2013). An educational fuzzy-based control platform using LEGO robots. International Journal of Electrical Engineering Education. 50(2):157-171. ISSN 0200-7209 (Print). ISSN 2050-4578 (Online)

L. E. Gómez, H. Sossa, R. Barrón, F. Cuevas and J. F. Jiménez (2013). Redes neuronales dinámicas aplicadas a la recomendación musical optimizada. Polibits 47:89-95.

F. Pérez, H. Sossa, R. Martínez, D. Lorias and A. Minor (2013). Video-Based Tracking of Laparoscopic Instruments using an Orthogonal Webcams System. International Journal of Medical, Pharmaceutical Science and Engineering 7(8):1-4.

Book chapters:

A. Peña and H. Sossa (2013). Chapter 6: Fuzzy Cognitive maps for Applied Sciences and Engineering. Book: Fuzzy Cognitive Maps for Applied Sciences and Engineering. Springer.

Conference papers:

H. Sossa and E. Guevara. Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition. Mexican Conference on Pattern Recognition (MCPR 2013). LNCS 7914, pp. 314-324.

H. Sossa and E. Guevara. Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition on RGB-D Data. 8th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013). LNAI 8073, pp. 304.313
.

R. Vega, E. Guevara, L. E. Falcon, G. Sanchez-Ante and H. Sossa. Blood Vessel Segmentation in Retinal Images using Lattice Neural Networks. 12th Mexican International Conference on Artificial Intelligence (MICAI 2013).

R. Ocampo, G. Sánchez, L. Falcón and H. Sossa. Automatic Reading of Electro-echanical Utility Meters. 12th Mexican International Conference on Artificial Intelligence (MICAI 2013).

2012:

Journal papers:

E. Cuevas, D. Oliva, D. Zaldivar, M. Pérez and H. Sossa (2012). Circle detection using Electromagnetism-like optimization. Information Sciences 182(1):40-55. 
 
A. Peña and H. Sossa, F. Cervantes (2012). Predictive Student Model Supported by Fuzzy-Causal Knowledge and Inference. Expert Systems with Applications 39:4690-4709.

R. Rodríguez, E. Torres and H. Sossa (2012). Image Segmentation via an iterative algorithm of the mean shift filtering for different values of the stoping thershold. International Journal of Imaging and Robotics 7(1):27-43.

E. Cuevas, F. Sención, D. Zaldivar. M. Perez and H. Sossa (2012). A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization. Applied Intelligence. DOI 10.1007/s10489-011-0330-z

L. E. Gómez, H. Sossa, R. Barrón and J. F. Sánchez (2012). A New Approach for Music Retrieval based on Dynamic Neural Network. International Journal of Hybrid Intelligent Systems 9:1-11. DOI 10.3233/HIS-2011-0143.

E. Cuevas, V. Osuna, D. Zaldivar, M. Perez and H. Sossa (2012). Multi-threshold Segmentation Based on Artificial Immne Systems. Mathematical Problems in Engineering. DOI: 10.1155/2012/874761.

Book chapters:

L. E. Toledo, F. J. Cuevas, J. Vielma, H. Sossa (2012). Chapter 5: Fringe Pattern Demodulation using Evolutionary Algorithms. Book Title: Advanced Topics in Measurements. Pp. 79-102. InTech. ISBN 978-953-51-0128-4.

A. Peña, I. Méndez, R. Mizoguchi, H. Sossa (2012), Proactive Sequencing based on a Causal and Fuzzy Student Model. Book Title: Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends. A. Peña-Ayala, (Ed.). Springer-Verlag, New York, Series: Smart Innovation, Systems and Technologies Series, No. 17. Pp. 49-76. ISBN: 978-3-642-642-30170-4.


 J. Villegas, G. Olague, H. Sossa and C. Avilés (2012). Evolutionary Associative Memories through Genetic Programming. Book Title: Parallel Architectures and Bioinspired Algorithms. Springer-Verlag. Series: Studies in Computational Intelligence. Vol. 415. Pp. 171-188. ISBN 978-3-642-28788-6.

R. Rodríguez, D. Domínguez, E. Torres and H. Sossa (2012). Image Segmentation through an Iterative Algorithm of the Mean Shift. Book Title: Advances in Image Segmentation, Pei-Gee Peter Ho(Ed.) INTECH. ISBN 980-953-307-581-0.

H. Sossa, B. A. Garro, J. Villegas, G. Olague and C. Avilés (2012). Evolutionary Computation Applied to the Automatic Design of Artificial Neural Networks and Associative Memories. AISC 175, pp. 285–297.

V. Osuna, E. Cuevas and H. Sossa (2012). Segmentation of Blood Cell Images Using Evolutionary Methods. AISC 175, pp. 299–311.

E. Cuevas, H. Sossa, V. Osuna, D. Zaldivar and M. Pérez (2012). Fast Circle Detection Using Harmony Search Optimization. AISC 175, pp. 313–325.

J. Jiménez, H. Sossa and F. Cuevas. Particle Swarm Optimization Applied to Interferogram Demodulation. AISC 175, pp. 327–337.

2011:

Books:

R. Rodríguez y H. Sossa (2011). Procesamiento y análisis digital de imágenes. Editorial RAMA. ISBN 978-84-9964-077-8.


Journal papers:

J. Villegas, H. Sossa, C. Avilés and G. Olague (2011). Automatic Synthesis of Associative Memories through Genetic Programming: a co-evolutionary approach. Revista Mexicana de Física 57(2):110-116.

R. Rodríguez, A. G. Suárez, H. Sossa (2011). A Segmentation Algorithm based on an Iterative Computation of the Mean Shift Filtering. Journal of Intelligent Robotic Systems 63(3):447-463.


R. A. Vázquez and H. Sossa (2011). Behavioral Study of Median Associative Memory under True Color Image Patterns. Neurocomputing 74:2985-2997.


H. Sossa, E. Cuevas and D. Zaldivar (2011). Alternative Way to Compute the Euler Number of a Binary Image. Journal of Applied Research and Technology 9(3):335-341.


R. Rodríguez, E. Torres and H. Sossa (2011). Image Segmentation based on an Iterative Computation of the Mean Shift Filtering for different values of window sizes. International Journal of Imaging and Robotics 6(11):1-19.

B. Garro, H. Sossa and R. A. Vazquez (2011). Back-Propagation vs Particle Swarm Optimization Algorithm: which Algorithm is better to adjust the Synaptic Weights of a Feed-Forward ANN? International Journal of Artificial Intelligence 7(11):208-218.

Image Databases: