October 30, 2017

IJCNN Sessions

IJCNN-1 Non-iterative Approaches in Learning
IJCNN-2 Special Session on Machine Learning and Deep Learning Methods applied to Vision and Robotics (MLDLMVR)
IJCNN-3 Complex-Valued and Quaternionic Neural Networks
IJCNN-4 Special Session on Deep Neural Audio Processing
IJCNN-5 Data Driven Approach for Bio-medical and Healthcare
IJCNN-6 Feature Extraction and Learning on Image and Text Data
IJCNN-7 Advances in Reservoir Computing
IJCNN-8 Empowering Deep Learning Models
IJCNN-09 Spiking Neural Networks (SNN)
IJCNN-10 Deep Learning for Structured and Multimedia Information
IJCNN-11 Cognition and Development
IJCNN-12 Biologically Inspired Computational Vision

IJCNN-1 Non-iterative Approaches in Learning

Organized by P. N. Suganthan (epnsugan@ntu.edu.sg ) and Filippo Maria Bianchi

Optimization, which plays a central role in learning, has received considerable attention from academics, researchers, and domain workers. Many optimization problems in machine learning can be tackled with non-iterative approaches, which can be presented in closed-form manner. Those methods are in general computationally faster than iterative solutions and less sensitive to parameter settings. Even though non-iterative methods have attracted much attention in recent years, there exists a performance gap when compared with older methods and other competing paradigms. This special session aims to bridge this gap.

Scope and Topics

The first target of this special session is to present the recent advances of non-iterative solutions in learning. Secondly, the focus is on promoting the concepts of non-iterative optimization with respect to counterparts, such as gradient-based methods and derivative-free iterative optimization techniques. Besides the dissemination of the latest research results on non-iterative algorithms, it is also expected that this special session will cover some practical applications, present some new ideas and identify directions for future studies.

Original contributions, comparative studies with both iterative and non-iterative methods are welcome. Typical paradigms include (but not limited to) random vector functional link (RVFL), Echo State Networks (ESN), kernel ridge regression (KRR), random forests (RF), etc…

The topics of the special session include, but are not limited to:

  • Methods with and without randomization
  • Regression, classification and time series analysis
  • Kernel methods such as kernel ridge regression, kernel adaptive filters, etc.
  • Feedforward, recurrent, multilayer, deep and other structures.
  • Ensemble learning
  • Moore-Penrose pseudo inverse, SVD and other solution procedures.
  • Gaussian Process regression
  • Non-iterative methods for large-scale problems with and without kernels
  • Theoretical analysis of non-iterative methods
  • Comparative studies with competing for iterative methods


IJCNN-2 Special Session on Machine Learning and Deep Learning Methods applied to Vision and Robotics (MLDLMVR)

Organized by José García-Rodríguez (jgarcia@dtic.ua.es), Alexandra Psarrou, Isabelle Guyon, Andrew Lewis

Over the last decades there has been an increasing interest in using machine learning and in the last few years, deep learning methods, combined with other vision techniques to create autonomous systems that solve vision problems in different fields. This special session is designed to serve researchers and developers to publish original, innovative and state-of-the-art algorithms and architectures for real-time applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, neural networks, intelligent interfaces and biomimetic object-vision recognition.

Scope and Topics

This special session provides a platform for academics, developers, and industry-related researchers belonging to the vast communities of *Neural Networks*, *Computational Intelligence*, *Machine Learning*, *Deep Learning*, *Biometrics*, *Vision systems*, and *Robotics *, to discuss, share experience and explore traditional and new areas of the computer vision, machine and deep learning combined to solve a range of problems. The objective of the workshop is to integrate the growing international community of researchers working on the application of Machine Learning and Deep Learning Methods in Vision and Robotics to a fruitful discussion on the evolution and the benefits of this technology to the society.

The methods and tools applied to vision and robotics include, but are not limited to, the following:

  • Computational Intelligence methods
  • Machine Learning methods
  • Self-adaptation and self-organization
  • Robust computer vision algorithms (operation under variable conditions, object tracking, behavior analysis and learning, scene segmentation)
  • Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
  • Convolutional Neural Networks CNN
  • Recurrent Neural Networks RNN
  • Deep Reinforcement Learning DRL
  • Hardware implementation and algorithms acceleration (GPUs, FPGAs)

The fields of application can be identified, but are not limited to, the following:

  • Video and Image Processing
  • Video tracking
  • 3D Scene reconstruction
  • 3D Tracking in Virtual Reality Environments
  • 3D Volume visualization
  • Intelligent Interfaces (User-friendly Human-Machine Interface)
  • Multi-camera and RGB-D camera systems
  • Multi-modal Human Pose Recovery and Behavior Analysis
  • Gesture and posture analysis and recognition
  • Biometric Identification and Recognition
  • Extraction of Biometric Features (fingerprint, iris, face, voice, palm, gait)
  • Surveillance systems
  • Autonomous and Social Robots
  • Robotic vision
  • Industry 4.0
  • IoT and Cyber-physical Systems


IJCNN-3 Complex-Valued and Quaternionic Neural Networks

Organized by Marcos Eduardo Valle (valle@ime.unicamp.br), Igor Aizenberg, Akira Hirose, and Danilo Mandic

Complex-valued neural networks (CVNNs) and quaternionic neural networks (QNNs) constitute a growing research area that has attracted continued interest for the last decade. One of the most important characteristics of CVNNs is the proper treatment of phase and the information contained in phase, e.g., the treatment of wave- and rotation-related phenomena such as electromagnetism, light waves, quantum waves, and oscillatory phenomena. QNNs, which have potential applications in three- and four-dimensional data modeling, have been effectively used for processing and analysis of multivariate images such as color and polarimetric SAR images.

More generally, hypercomplex-valued neural networks – which include CVNNs and QNNs, treat multidimensional data as a single entity. There are several new directions in hypercomplex-valued neural networks: from the formal generalization of the commonly used algorithms to the hypercomplex case that is mathematically richer than regular neurons, to the use of original activation functions that can increase significantly the neuron and network functionality. There are also many interesting applications of HVNNs in pattern recognition and classification, nonlinear filtering, intelligent image processing, brain-computer interfaces, time-series prediction, bioinformatics, robotics, etc.

The CVNNs special session, which indeed covers hypercomplex-valued models, has become a traditional event of the IJCNN conference. Eight special sessions organized since 2006 (WCCI-IJCNN 2006, Vancouver; WCCI-IJCNN 2008, Hong Kong; IJCNN 2009, Atlanta; WCCI-IJCNN 2010, Barcelona; IJCNN-2011, San Jose; WCCI-IJCNN 2012, Brisbane; IJCNN-2013, Dallas; IJCNN-2014, Beijing, WCCI-IJCNN 2016, Vancouver) attracted numerous submissions and had large audiences. They featured many interesting presentations and very productive discussions.

Scope and Topics

IJCNN 2018, which is an integrated part of IEEE WCCI 2018, will be a very attractive forum for complex-valued and quaternionic neural networks. It will be possible to organize a systematic and comprehensive exchange of ideas in the area, to present the recent research results, and to discuss future trends. We hope the proposed session will attract not only potential speakers but also many kinds of research interested in joining the hypercomplex-valued neural network community. We expect that this session would be very beneficial for computational intelligence researchers and other specialties that are in need of the sophisticated neural networks tools.

Papers that are, or might be, related to all aspects of the hypercomplex-valued neural networks, including CVNNs and QNNs, are invited. We welcome contributions on theoretical advances as well as contributions of applied nature. We also welcome interdisciplinary contributions from other areas that are on the borders of the proposed scope. Topics include, but are not limited to:

  • Theoretical Aspects of CVNNs and QNNs
  • Complex-Valued and Quaternion Activation Functions
  • Learning Algorithms for CVNNs
  • Complex-Valued and Quaternionic Associative Memories
  • Pattern Recognition, Classification and Time Series Prediction using CVCNNs and QNNs
  • CVNNs and QNNs in Nonlinear Filtering
  • Dynamics of Complex-Valued and Quaternionic Neurons
  • Learning Algorithms for CVCNNs and QNNs
  • Chaos in Complex Domain
  • Feedforward CVCNNs
  • Spatiotemporal CVNNs Processing
  • Frequency Domain CVNNs Processing
  • Phase-Sensitive Signal Processing
  • Applications of CVNNs and QNNs in Image Processing, Speech Processing, and Bioinformatics
  • Quantum Computation and Quantum Neural Networks
  • CVNN in Brain-Computer Interfaces
  • CVNNs and QNNs in Robotics
  • Clifford and Hypercomplex-valued Neural Networks

IJCNN-4 Special Session on Deep Neural Audio Processing

Organized by Emanuele Principi, Aurelio Uncini, Björn Schuller, Stefano Squartini (s.squartini@univpm.it)

Computational Audio Processing techniques have been largely addressed by scientists and technicians in many application areas, like entertainment, human-machine interfaces, security, forensics, and health. Depending on the problem under study, these techniques have been successfully applied to speech signal processing (speech/speaker recognition, speech enhancement, emotion and speaker state recognition, privacy-preserving speech processing), music information retrieval and automated music generation, and in generic sound processing for acoustic monitoring, acoustic scene understanding and sound separation, detection, and identification. In the case of animal vocalization analysis, some efforts have been recently spent in the automatic classification and recognition of animal species by means of their emitted sounds.

In the different fields, state of the art performance has recently been obtained by using data-driven learning systems, often represented by variants of deep neural network architectures. Several challenges remain open, due to the increasing complexity of the tasks, the presence of non-stationary operating conditions, the difference between laboratory and real acoustic scenarios, and the necessity to respect hard real-time constraints, also when the amount of data to process is big and battery-powered devices are involved. In some other application contexts, the challenge is facing a scarce amount of data to be used for training, and suitable architectures and algorithms need to be designed on purpose. Moreover, also the employment of cross-domain approaches to exploit the information contained in diverse kinds of environmental audio signals are often needed, as recently investigated by some pioneering works.

Scope and Topics

The aim of this special session is to provide a forum for the presentation of the most recent advancements on Deep Neural Networks algorithms applied to Digital Audio problems, with particular attention to speech analysis and enhancement, music information retrieval and generation, as well as acoustic scenes and events detection and classification, exploring also new and emerging methods, such as end-to-end and one-shot/zero-shot learning. Topics include, but are not limited to:

  • Computational Audio Analysis
  • Deep Learning Algorithms in Digital Audio
  • Neural Architectures for Audio Processing
  • Transfer, Weakly Supervised, and Reinforcement Learning for Audio
  • Music Information Retrieval
  • Music Performance Analysis
  • Neural Methods for Music/Speech generation and synthesis
  • Computational Methods for Physical Instrument Modeling
  • Music Content Analysis
  • Voice conversion
  • Speech and Speaker Analysis and Classification
  • Sound Detection and Identification
  • Acoustic Novelty Detection
  • Computational methods for Wireless Acoustic Sensor Networks
  • Acoustic Scene Analysis
  • Cross-domain Audio Analysis
  • Signal enhancement with neural networks
  • End-to-End learning for Digital Audio Applications
  • Privacy preserving computational speech processing
  • One-shot/Zero-shot learning for digital audio applications


IJCNN-5 Data Driven Approach for Bio-medical and Healthcare

Organized by Paul J Kennedy (Paul.Kennedy@uts.edu.au), Mukesh Prasad and Alexei Manso Correa Machado

Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. For example, data analysis methods are applied on biomedical datasets, namely DNA microarray data or Next Gen sequencing data to predict treatment outcomes of paediatric Acute Lymphoblastic Leukaemia patients. Moreover, clustering methods are routinely used to investigate the interpretation of the correlated genes associated with cellular and biological function.

Scope and Topics

Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore feature selection methods to select stable sets of genes to improve predictive performance along with interpretation. There is also a need to explore big data in biomedical and healthcare research. An increasing flood of data characterises human health care and biomedical research. Healthcare data are available in different formats, including numeric, textual reports, signals and images, and the data are available from different sources. An interesting aspect is to integrate different data sources in the data analysis process which requires exploiting the existing domain knowledge from available sources. The data sources can be ontologies, annotation repositories, and domain experts’ reports.

This special session aims to bring together the current research progress (from both academia and industry) on data analysis for biomedical and healthcare applications. It will attract healthcare practitioners who have access to interesting sources of data but lack the expertise in using the data mining effectively. Special attention will be devoted to handle feature selection, class imbalance, and data fusion in biomedical and healthcare applications.

The main topics of this special session include, but are not limited to, the following:

  • Information fusion and knowledge transfer in biomedical and healthcare applications
  • Data Analysis of the biomedical data including genomics.
  • Text mining for medical reports.
  • Statistical analysis and characterization of biomedical data.
  • Machine Learning Methods Applied to Medicine
  • Large Datasets and Big Data Analytics on biomedical and healthcare applications.
  • Information Retrieval of Medical Images
  • Machine learning technique for single cell sequencing analysis
  • Medical imaging and genomics


IJCNN-6 Feature Extraction and Learning on Image and Text Data

Organized by Domingo Mery (domingo.mery@uc.cl), Jefersson Alex dos Santos, Nabin Sharma and Mukesh Prasad

The current resurgence of neural networks in the form of deep learning have shown remarkable results in fundamental tasks such as segmentation, tracking, detection, recognition, classification, clustering and feature learning. The deep features extracted from deep neural network architectures are robust and have good representation for most of the fundamental computer vision tasks. Although, deep learning has shown tremendous amount of success in the fundamental tasks in the areas of image and text analysis, the intuitive understanding of the architectures are yet to be explored in details. There is a need of further exploration of architectures, whichis suitable for a specific tasks. Training of neural network architecture and then transfer the learned features to another unknown task, which requires transfer learning and fine tuning. Therefore, transfer learning also has a significant research scope, both from theoretical and application perspective. Traditional machine learning approaches rely on hand crafted features such edges, texture, SIFT etc. The fusion of such features are used to tackle many complex computer vision problem and they poorly generalized to the unknown scene.

Scope and Topics

Recent advances in both feature extraction and learning on image and text data have allowed us to develop promising solutions that are being increasingly used in our society. For this reason, this special session aims to bring together researchers, scientists, engineers and students to discuss the state of the art and the new trends in feature extraction and learning on image and text data. The idea of the session is to present recent theories and applications in deep learning, transfer learning, reinforcement learning and some other feature extraction/learning techniques for various image and text oriented tasks, such as object recognition, image retrieval/classification, annotation, multimedia processing, image super-resolution, text mining and text analysis. Special attention will be devoted to handle advanced issues of network architecture design, real-time performance criteria for various applications and diverse application areas. The main topics of this special session include, but are not limited to, the following:

  • Cross domain transfer learning
  • Real-time Object segmentation, detection and recognition in complex environment
  • Human gesture/activity recognition
  • Visual analysis of crowds, surveillance systems and applications
  • Document image analysis and systems
  • Handwriting recognition
  • Writer identification
  • Text classification/analysis
  • Sparse representation and low-rank representation for feature extraction
  • Contextual scene understanding and summarization
  • Ensemble of traditional and deep learning techniques
  • Methods aplicable to Forensic Science
  • Drone based applications.
  • Novel Reinforcement Learning algorithms with deep representation layer

IJCNN-7 Advances in Reservoir Computing

Organized by Claudio Gallicchio (gallicch@di.unipi.it), Alessio Micheli, Simone Scardapane and Peter Tiňo

During the last decade, Reservoir Computing (RC) has attested itself as a state of the art paradigm for efficient learning in the temporal domain. The extreme efficiency of the approach follows from limiting the training algorithm to only a readout component, while the temporal encoding process is carried out by a dynamical pool of recurrent neurons that, under certain conditions, is able to develop a rich representation of the temporal information even if untrained.
The common paradigm has been instantiated into several models, among which the Echo State Network and the Liquid State Machine represent the most widely known ones.
Since its origins, we have witnessed a progressively increasing popularization of the RC approach, especially by virtue of its ease of implementation and its extreme training efficiency. At the same time, several theoretical research lines strongly contributed in understanding the bias of untrained recurrent models, grounding the RC methodology on a solid mathematical basis and better delineating the potentialities and downsides of the approach.

Scope and Topics

After 10 years since the introduction of the term “Reservoir Computing” into the neural networks literature, it is now time to sum up all the recent research initiatives in the field and start drawing its future. This session calls for both theoretical and applicative contributions emerging in the field of RC, aiming at stimulating an open discussion in the neural network community.

The list of relevant topics for this session includes, but is not limited to, the following:

  • Novel Reservoir Computing models
  • Theoretical analysis of Reservoir Computing
  • Physical implementations of Reservoir Computing
  • Evolutions of the RC paradigm (e.g., conceptors)
  • Biological motivations and applications in Neuroscience of Reservoir Computing
  • Adaptation of reservoir dynamics and of system dynamics
  • Deep Reservoir Computing models
  • Reservoir Computing for learning in structured domains (trees, graphs, networks, …)
  • Reservoir Computing for Big Data
  • Novel application fields for the RC paradigm

IJCNN-8 Empowering Deep Learning Models

Organized by Nicolò Navarin (nnavarin@math.unipd.it), Luca Oneto, Luca Pasa and Alessandro Sperduti

In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, computer vision, drug discovery, genomics and many others.

Scope and Topics

The goal of this special session is to provide a forum for focused discussions on new extensions of deep learning models and techniques and to gain a deeper understanding of the difficulties and limitations associated with state-of-the-art approaches and algorithms. Practitioners should provide practical insights to the theoreticians, which in turn, should supply theoretical insights and guarantees, further strengthening and sharpening practical intuitions and wisdom.

Examples of these possible extensions are:

  • Multimodal and Multitask Deep Learning
  • Deep Transfer Learning
  • Deep Recurrent and Recursive Neural Networks
  • Deep Learning on Structured Data
  • Interpretability of Deep Learning
  • Private and Federated Deep Learning
  • Generative and Adversarial Deep Learning
  • Randomized Deep Learning (Deep ELM, Deep ESN, Deep Reservoir Computing)

The focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations between the different fields of deep learning and other fields of research in solving real world problems. Both theoretical and practical results (e.g. Social Data Analysis, Speech, Natural Language Processing, Cybersecurity) are welcome to our special session. This special session is supported by the IEEE Task Force on Deep Learning http://deeplearning.math.unipd.it .

IJCNN-09 Spiking Neural Networks (SNN)

Organized by Professor Nikola Kasabov (nkasabov@aut.ac.nz), Maryam G Doborjeh (mgholami@aut.ac.nz), Dr Elisa Capecci, Zohre Gholami

Spiking Neural Networks (SNN) are a rapidly emerging means of neural information processing, drawing inspiration from the brain processes. They have the potential to advance technologies and techniques in fields as diverse as medicine, finance, computing, and indeed any field that involves complex temporal or spatiotemporal data. SNN, as the third generation of neural networks, can operate on noisy data, in changing environments at low power and with high effectiveness. Due to their basis in biological neural networks, SNN research is strongly positioned to benefit from advances made in the fields of molecular, evolutionary and cognitive neuroscience. There is presently considerable interest in this topic. We believe that this area is quickly establishing itself as an effective alternative to traditional machine learning technologies, and the interest in this area of research is growing rapidly.

Scope and Topics

The aim of this special session is to bring together research works of contemporary areas of SNN, including theoretical, computational, application-oriented, experimental studies, and emerging technologies such as neuromorphic hardware. This special session invites researchers to present state-of-the-art approaches, recent advances and the potential of SNN.

The topics relevant to this special session include, but are not limited to, the following:

  • Theory of SNN
  • Learning algorithms for SNN, including Deep Learning
  • Computation with and within SNN
  • Theory or practice in biologically-plausible neural simulation or biomimetic models
  • Big data and stream data processing in SNN
  • Multiple sensor networks data processing in SNN
  • Neuromorphic hardware systems and applications
  • Optimization of SNN
  • SNN models of cognitive development
  • Information encoding for SNN
  • SNN applications in neuroinformatics, bioinformatics, medicine and ecology.
  • SNN in BCI
  • SNN in neuro-robotic
  • Any other topics relating to SNN, their theory, or applications.

IJCNN-10 Deep Learning for Structured and Multimedia Information

Organized by Davide Bacciu (bacciu@di.unipi.it), Silvio Jamil F. Guimarães and Zenilton K. G. Patrocínio Jr


A key factor triggering the deep learning revolution has been its ground-breaking performance on image and video processing applications. These have been built mostly on a (multi-dimensional) raw data representation of the visual information.  Multimedia content, on the other hand, calls for more articulated data representations catering for the multimodal nature of this information. These are often based on a structured representation that can capture the complexity of the contextual, semantic and geometrical relationships among the visual, phonetic and textual entities and concepts.

Scope and Topics

The goal of this special session is to provide a forum for researchers working on the next generation of deep learning models for machine vision and multimedia information, which are capable of extracting and processing information in a structured representation and/or with a multimodal nature. We welcome contributions proposing innovative deep models dealing with:

  • learning hierarchical or networked representations of multimedia information;
  • processing of structured multimedia information under the form of sequences, labelled trees, as well as more general forms of graphs;
  • understanding and synthesizing of multimodal data;
  • fusion of multimodal information.

This special session is meant to attract researchers from deep learning, machine vision and multimedia information communities. We aim to bring together researchers with consolidated tradition on structured data processing (such as in machine learning and NLP) with those with machine vision and multimedia processing insight, but mostly working with flat-data representations.

Topics of interest for this special session include, but are not limited to, the following:

  • deep learning models for structured data;
  • representation learning in machine vision and multimedia processing;
  • hierarchical/structured visual processing;
  • deep models for visual data streams;
  • generative and variational deep learning for multimedia data;
  • multimedia data synthesis;
  • attentional and bio-inspired models for the processing of visual and audio information;
  • applied deep learning to machine vision and multimedia processing, such as: biomedical images and biobanks, pose and gesture estimation from graphs, etc.;
  • innovative software and libraries for deep learning and multimedia content understanding.

IJCNN-11 Cognition and Development

Organized by Nikolas J. Hemion (nhemion@softbankrobotics.com), Anna-Lisa Vollmer, Angelo Cangelosi, Pierre-Yves Oudeyer

The special session aims at the presentation of the latest results in the investigation of machine learning and cognitive robotics models that are taking inspiration from our understanding of sensorimotor, cognitive, and social development in humans and other animals.

Scope and Topics

The aim is twofold: On the one hand, the special session provides a platform for presenting insights about the functions and processes underlying developmental learning. On the other hand, it encourages discussion of the role of developmental aspects in learning, in particular in the light of the ongoing success of deep learning methods.

Relevant research questions include:

  • How can deep learning models be improved by including developmental processes, such as intrinsic motivation, sensorimotor exploration, or social learning and interaction?
  • Is current deep learning methodology suited for autonomous and open-ended learning in cognitive robots? Or will it be necessary to consider developmental processes, and if so, which ones?
  • What are the mechanisms that allow a child or robot to autonomously develop cognitive capabilities?
  • How does the social and physical environment, with which the child interacts, shape and scaffold the child’s developing cognitive skills and knowledge? And how can machine learning systems (including cognitive robots) exploit these beneficial factors?
  • What is the relative contribution of Nature and Nurture in the development of human and machine intelligence?
  • What do qualitative stages during development, and body and brain maturational changes, tell us about the mechanisms and principles supporting development?

The special session also encourages submissions of relevant research on cognition and development from other disciplines, such as child psychology, developmental linguistics, neuroscience, philosophy, and interdisciplinary approaches to cognition and development.

List of main topics:

  • developmental robotics
  • epigenetic robotics
  • developmental deep learning
  • neuro-robotics
  • bio-inspired and cognitive robotics
  • cognitive modelling
  • intrinsic motivation
  • sensorimotor development
  • cognitive development
  • social development
  • language acquisition

IJCNN-12 Biologically Inspired Computational Vision

Organized by: Khan M. Iftekharuddin (kiftekha@odu.edu)


Constructive understanding of computational principles of visual information processing, perception and cognition is one of the most fundamental challenges of contemporary science. Deeper insight into biological vision helps to advance intelligent systems research to achieve robust performance similar to biological systems. Biological inspiration indicates that sensory processing, perception, and action are intimately linked at various levels in animal vision. Implementing such integrated principles in artificial systems may help us achieve better, faster and more efficient intelligent systems.

Scope and Topics

This special session provides an integrated platform to present original ideas, theory, design, and applications of computational vision. Topics of interest include, but are not limited to the following:

  • Theoretical approaches and modeling in computational vision
  • Neuronal mechanisms of visual processing
  • Low level vision and its relationship to biological machinery
  • Artificial learning systems for image and information processing and evidential reasoning for recognition
  • Intelligent search in communications networks
  • Modeling issues in ATM networks, agent-oriented computing architectures
  • Perception of shape, shadows, poses, color and illumination in object recognition
  • Tracking for inferring shapes and 3D motions
  • Active visual perception, attention and robot vision
  • Functional Magnetic Resonance Imaging (fMRI) studies of visual segmentation and perception
  • Application of computational vision in areas of
    • Automated target identification and acquisition systems in defense and industry
    • Biomedical imaging
    • 3D photography
    • Face recognition
    • Learning to segment camouflaged objects
    • Motor actions and robotics
    • Image databases and indexing
  • Hardware implementation of computational vision
  • Contemporary deep learning algorithms, structures and methods for computer vision applications
    • Transfer learning
    • Meta learning
    • Recurrent learning
  • Any other topics related to biological approaches in computer vision