Dr. Amir Hossein Assadi Profile

Dr. Amir Hossein Assadi

Professor at Univ of Wisconsin Madison

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Author

Publications (18)

Proceedings Article | 14 March 2013 Paper

Quantifying patterns of dynamics in eye movement to measure goodness in organization of design elements in interior architecture

Hasti Mirkia, Arash Sangari, Mark Nelson, Amir Assadi

Proceedings Volume 8651, 86511D (2013) https://doi.org/10.1117/12.2008580

KEYWORDS: Eye, Visualization, Eye models, Calibration, Data modeling, Neural networks, Motion analysis, Iris recognition, Mathematical modeling, Brain

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Proceedings Article | 17 January 2005 Paper

Information processing of motion in facial expression and the geometry of dynamical systems

Amir Assadi, Hamid Eghbalnia, Brenton McMenamin

Proceedings Volume 5675, (2005) https://doi.org/10.1117/12.586073

KEYWORDS: Video, Computer programming, Dynamical systems, Principal component analysis, Video processing, Feature extraction, Video surveillance, Data processing, Image segmentation, Video coding

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Proceedings Article | 17 January 2005 Paper

Anatomically constrained neural network models for the categorization of facial expression

Brenton McMenamin, Amir Assadi

Proceedings Volume 5675, (2005) https://doi.org/10.1117/12.593973

KEYWORDS: Amygdala, Facial recognition systems, Neural networks, Network architectures, Parallel processing, Visual cortex, Visual process modeling, Algorithm development, Detection and tracking algorithms, Performance modeling

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Proceedings Article | 28 May 2003 Paper

MFA-SPINE noise reduction for text images

Joseph Uchill, Amir Assadi

Proceedings Volume 5014, (2003) https://doi.org/10.1117/12.477718

KEYWORDS: Digital filtering, Image filtering, Matrices, Denoising, Visualization, Document imaging, Algorithms, Convolution, Computer programming, Image compression

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Proceedings Article | 30 January 2003 Paper

Matrix frequency analysis and its applications to language classification of textual data for English and Hebrew

Joseph Uchill, Amir Assadi

Proceedings Volume 4793, (2003) https://doi.org/10.1117/12.454831

KEYWORDS: Matrices, Image classification, Statistical analysis, Mathematics, Image analysis, Internet, Data mining, Detection and tracking algorithms, Quantization, Computer networks

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Proceedings Article | 24 November 2002 Paper

Improving subjective pattern recognition in chemical senses through reduction of nonlinear effects in evaluation of sparse data

Amir Assadi, Firooz Rasouli, Susan Wrenn, M. Subbiah

Proceedings Volume 4794, (2002) https://doi.org/10.1117/12.454826

KEYWORDS: Neural networks, Data modeling, Principal component analysis, Chemical analysis, Visualization, Information technology, Data analysis, Data acquisition, Pattern recognition, Mathematical modeling

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Artificial neural network models are typically useful in pattern recognition and extraction of important features in large data sets. These models are implemented in a wide variety of contexts and with diverse type of input-output data. The underlying mathematics of supervised training of neural networks is ultimately tied to the ability to approximate the nonlinearities that are inherent in network’s generalization ability. The quality and availability of sufficient data points for training and validation play a key role in the generalization ability of the network. A potential domain of applications of neural networks is in analysis of subjective data, such as in consumer science, affective neuroscience and perception of chemical senses. In applications of ANN to subjective data, it is common to rely on knowledge of the science and context for data acquisition, for instance as a priori probabilities in the Bayesian framework. In this paper, we discuss the circumstances that create challenges for success of neural network models for subjective data analysis, such as sparseness of data and cost of acquisition of additional samples. In particular, in the case of affect and perception of chemical senses, we suggest that inherent ambiguity of subjective responses could be offset by a combination of human-machine expert. We propose a method of pre- and post-processing for blind analysis of data that that relies on heuristics from human performance in interpretation of data. In particular, we offer an information-theoretic smoothing (ITS) algorithm that optimizes that geometric visualization of multi-dimensional data and improves human interpretation of the input-output view of neural network implementations. The pre- and post-processing algorithms and ITS are unsupervised. Finally, we discuss the details of an example of blind data analysis from actual taste-smell subjective data, and demonstrate the usefulness of PCA in reduction of dimensionality, as well as ITS.

Proceedings Article | 24 November 2002 Paper

Automation of pattern recognition in cDNA microarray data: an application to gene-based diagnostic prediction of cancers

Liya Wang, Amir Assadi

Proceedings Volume 4794, (2002) https://doi.org/10.1117/12.454825

KEYWORDS: Cancer, Principal component analysis, Pattern recognition, Signal processing, Diagnostics, Bioinformatics, Superposition, Neural networks, Analytical research, Biological research

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Proceedings Article | 2 November 2001 Paper

Perceptual geometry of space and form: visual perception of natural scenes and their virtual representation

Amir Assadi

Proceedings Volume 4476, (2001) https://doi.org/10.1117/12.447288

KEYWORDS: Visualization, Natural surfaces, Intelligence systems, Mathematics, Dynamical systems, Mathematical modeling, Physics, Visual process modeling, Computer programming, Solids

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Proceedings Article | 2 November 2001 Paper

Geometric methods in nonlinear analysis of data from brain imaging

Hamid Eghbalnia, Amir Assadi

Proceedings Volume 4476, (2001) https://doi.org/10.1117/12.447277

KEYWORDS: Functional magnetic resonance imaging, Electroencephalography, Principal component analysis, Independent component analysis, Error analysis, Brain, Brain imaging, Data analysis, Temporal resolution, Statistical analysis

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Proceedings Article | 2 November 2001 Paper

Dynamics and robustness for singular value decomposition: application to face recognition

Semmi Pasha, Hamid Eghbalnia, Amir Assadi

Proceedings Volume 4476, (2001) https://doi.org/10.1117/12.447280

KEYWORDS: Principal component analysis, Facial recognition systems, Independent component analysis, Statistical analysis, Databases, Error analysis, Data modeling, Data analysis, Algorithms, Visualization

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Proceedings Article | 4 December 2000 Paper

Learning optimal wavelets from overcomplete representations

Hamid Eghbalnia, Amir Assadi

Proceedings Volume 4119, (2000) https://doi.org/10.1117/12.408612

KEYWORDS: Wavelets, Visual process modeling, Visualization, Principal component analysis, Cognitive modeling, Associative arrays, Signal processing, Brain, Computing systems, Fourier transforms

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Proceedings Article | 23 October 2000 Paper

Learning theoretic method for estimation of geometry of surfaces in natural scenes

Amir Assadi, Hamid Eghbalnia

Proceedings Volume 4117, (2000) https://doi.org/10.1117/12.404811

KEYWORDS: Visualization, Visual process modeling, Wavelets, Principal component analysis, Natural surfaces, Statistical analysis, Image segmentation, Eye models, Computer vision technology, Brain

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In biological and computational vision, the perception and description of geometric attributes of surfaces in natural scenes has received a great deal of attention. The physical and geometric properties of surfaces related to their optical characteristics depend on their texture. In previous work, we introduced the concept of the Gestalt of a surface. The Gestalt being a geometric object that retains the mathematical properties of the surface. The Gestalt is determined using the theory of Riemannian foliations from differential topology and the concept of an observer's subjective function in a probabilistic manner. In this paper, we continue our study of geometry of natural surfaces with textures that exhibit statistical regularity at some resolution. It appears that all earlier algorithms in computer vision for extraction of shape attributes from texture have made some (piecewise) smoothness assumption about the surfaces under study. However, natural surfaces, as well as most synthetic ones are not smooth in the mathematical sense. Hence, the domain of applicability of current algorithms is severely limited. We propose algorithms to estimate geometric invariants of textured surfaces that are not necessarily smooth, but possess a statistically regular structure. An important step is based on a learning theoretic method for parameterization of textured surfaces. This method takes into account the statistical texture information in a 2D image of the surface. From a dictionary of geometry for the parameter space, a supervised artificial neural network selects the optimal choice for parameterization. As a result, the algorithms for shape from texture (slant, tilt, curvature...) have a more efficient implementation and a faster runtime. In this paper, we explain the significance of statistically symmetric patterns on surfaces. We show how such texture regularity can be used to solve the linearized problem, leaving the full details of the linearization of the Gestalt of surfaces to a forthcoming paper. The solution of the linearized problem together with algorithms to linearized surface Gestalts provide the desired estimates for the geometric features of natural surfaces with statistically regular textures at some scale.

Proceedings Article | 23 October 2000 Paper

Perception of space and geometry through visual attention and statistical learning

Amir Assadi, Hamid Eghbalnia

Proceedings Volume 4117, (2000) https://doi.org/10.1117/12.404812

KEYWORDS: Visualization, Visual process modeling, Intelligence systems, Principal component analysis, Eye, Data processing, Natural surfaces, Brain, Retina, Visual system

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In Vision Geometry '99 we introduced the Gestalt approach to perceptual approximation of surfaces in natural scenes; that is, as a geometric theory retaining certain mathematical properties of surfaces while adhering to the human perceptual organization of vision. The theory of curves follows the same philosophy, relying on optical properties of physical objects whose features in the scale and resolution -- imposed by the observer -- afford 'a one-dimensional Gestalt.' The Gestalt theory of curves and surfaces is part of the Perceptual Geometry of the natural world that hypothetically evolves within intelligent systems capable of retaining partial information from stimuli in 'memory' and visual 'learning' through 'brain plasticity.' Perceptual geometry aims at explaining geometry from the perspective of visual perception, and in turn, how to apply such geometric findings to the ecological study of vision. Perceptual geometry attempts to answer fundamental questions in perception of form and representation of space through synthesis of cognitive and biological theories of visual perception with geometric theories of the physical world. Algorithms in this theory are typically presented based on a combination of a mathematical formulation of eye-movements and multi-scale multi-resolution filtering. In this paper, methods from statistical pattern recognition are applied to explain the learning-theoretic and perceptual analogs of geometric theory of space and its objects optically defined by curves and surfaces. The human visual system recovers depth from the visual stimuli registered on the two-dimensional surface of the retina by means of a variety of mechanisms, from bottom-up (such as stereopsis and motion parallax) to top-down influences. Perception and representation of space and objects within the visual environment rely on combination of a multitude of such mechanisms. The problem of modeling cortical representation of visual information is, therefore, very complex and challenging.

Proceedings Article | 13 July 2000 Paper

Quantum neurocomputation

Hamid Eghbalnia, Amir Assadi

Proceedings Volume 4047, (2000) https://doi.org/10.1117/12.391963

KEYWORDS: Probability theory, Independent component analysis, Neurons, Quantum information, Quantum computing, Quantum physics, Visual system, Computing systems, Neural networks, Pattern recognition

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Proceedings Article | 6 June 2000 Paper

Bayesian analysis of multimodal data and brain imaging

Amir Assadi, Hamid Eghbalnia, Miroslav Backonja, Ronald Wakai, Paul Rutecki, Victor Haughton

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387621

KEYWORDS: Functional magnetic resonance imaging, Independent component analysis, Magnetoencephalography, Data modeling, Electroencephalography, Temporal resolution, Brain, Maxwell's equations, Brain imaging, Spatial resolution

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It is often the case that information about a process can be obtained using a variety of methods. Each method is employed because of specific advantages over the competing alternatives. An example in medical neuro-imaging is the choice between fMRI and MEG modes where fMRI can provide high spatial resolution in comparison to the superior temporal resolution of MEG. The combination of data from varying modes provides the opportunity to infer results that may not be possible by means of any one mode alone. We discuss a Bayesian and learning theoretic framework for enhanced feature extraction that is particularly suited to multi-modal investigations of massive data sets from multiple experiments. In the following Bayesian approach, acquired knowledge (information) regarding various aspects of the process are all directly incorporated into the formulation. This information can come from a variety of sources. In our case, it represents statistical information obtained from other modes of data collection. The information is used to train a learning machine to estimate a probability distribution, which is used in turn by a second machine as a prior, in order to produce a more refined estimation of the distribution of events. The computational demand of the algorithm is handled by proposing a distributed parallel implementation on a cluster of workstations that can be scaled to address real-time needs if required. We provide a simulation of these methods on a set of synthetically generated MEG and EEG data. We show how spatial and temporal resolutions improve by using prior distributions. The method on fMRI signals permits one to construct the probability distribution of the non-linear hemodynamics of the human brain (real data). These computational results are in agreement with biologically based measurements of other labs, as reported to us by researchers from UK. We also provide preliminary analysis involving multi-electrode cortical recording that accompanies behavioral data in pain experiments on freely moving mice subjected to moderate heat delivered by an electric bulb. Summary of new or breakthrough ideas: (1) A new method to estimate probability distribution for measurement of nonlinear hemodynamics of brain from a multi- modal neuronal data. This is the first time that such an idea is tried, to our knowledge. (2) Breakthrough in improvement of time resolution of fMRI signals using (1) above.

Proceedings Article | 2 June 2000 Paper

Learning theoretic approach to differential and perceptual geometry: I. Curvature and torsion are the independent components of space curves

Amir Assadi, Hamid Eghbalnia

Proceedings Volume 3959, (2000) https://doi.org/10.1117/12.387191

KEYWORDS: Independent component analysis, Intelligence systems, Principal component analysis, 3D modeling, Motion models, Mathematical modeling, Visual process modeling, Visualization, Statistical analysis, Signal to noise ratio

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In standard differential geometry, the Fundamental Theorem of Space Curves states that two differential invariants of a curve, namely curvature and torsion, determine its geometry, or equivalently, the isometry class of the curve up to rigid motions in the Euclidean three-dimensional space. Consider a physical model of a space curve made from a sufficiently thin, yet visible rigid wire, and the problem of perceptual identification (by a human observer or a robot) of two given physical model curves. In a previous paper (perceptual geometry) we have emphasized a learning theoretic approach to construct a perceptual geometry of the surfaces in the environment. In particular, we have described a computational method for mathematical representation of objects in the perceptual geometry inspired by the ecological theory of Gibson, and adhering to the principles of Gestalt in perceptual organization of vision. In this paper, we continue our learning theoretic treatment of perceptual geometry of objects, focusing on the case of physical models of space curves. In particular, we address the question of perceptually distinguishing two possibly novel space curves based on observer's prior visual experience of physical models of curves in the environment. The Fundamental Theorem of Space Curves inspires an analogous result in perceptual geometry as follows. We apply learning theory to the statistics of a sufficiently rich collection of physical models of curves, to derive two statistically independent local functions, that we call by analogy, the curvature and torsion. This pair of invariants distinguish physical models of curves in the sense of perceptual geometry. That is, in an appropriate resolution, an observer can distinguish two perceptually identical physical models in different locations. If these pairs of functions are approximately the same for two given space curves, then after possibly some changes of viewing planes, the observer confirms the two are the same.

Proceedings Article | 30 March 2000 Paper

Visual target selection employing local-to-global strategies for support vector machines

Hamid Eghbalnia, Amir Assadi

Proceedings Volume 4055, (2000) https://doi.org/10.1117/12.380563

KEYWORDS: Visualization, Eye, Principal component analysis, Target detection, Independent component analysis, Error analysis, Data modeling, Computer programming, Associative arrays, Retina

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Proceedings Article | 23 September 1999 Paper

Geometry of the perceptual space

Amir Assadi, Stephen Palmer, Hamid Eghbalnia, John Carew

Proceedings Volume 3811, (1999) https://doi.org/10.1117/12.364087

KEYWORDS: Visualization, Neurons, Mathematical modeling, Visual process modeling, Mathematics, Eye, Psychology, Cones, Brain, Computational modeling

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