Topographic Independent Component Analysis Research Articles

Prediction of material properties of propellant waste modified bricks through microstructures by Topographic independent component analysis coupled 3D Convolution neural networks

PurposeDeciphering materials properties from the textural information of microstructural images is still a challenge. ObjectiveProposes a Topographic Independent Component Analysis coupled with 3-D Convolutional Neural Network (TICA- 3DCNN) architecture that processes the features from scanning electron microscope (SEM) images to predict the material properties of high energy propellant (HEP) modified bricks. MethodFirst, high-energy modified bricks with ten different HEP additives fractions is prepared, and their microstructural information is acquired with SEM. Then, SEM images of each group are pre-processed and subjected to the TICA analysis to decipher the distinct higher-order microstructural dependencies, such as co-activation of components. These microstructural co-activation components are subsequently ordered and passed into 3D CNN architecture to map them with the material properties such as water absorption, compressive strength, density, and porosity. An extreme learning machine is employed as a fully connected classification layer in the 3D-CNN architecture. Finally, the performance of the proposed TICA - 3DCNN approach is compared with the traditional 2D CNN frameworks such as Inception-v4 and Faster RCNN. ResultsProposed TICA-3DCNN architecture has performed with higher sensitivity and specificity than Inception-v4 and Faster RCNN architecture in deciphering microstructural textural properties and mapping them with materials properties. ConclusionThe Proposed model can be used in the construction and building materials to predict material properties through microstructural features.

Facial Image Analysis for Identification of Gender: A Machine Learning based Approach

Facial biometrics for gender classification is known to significantly improve the efficiency of person identification in biometric access control models. It not only increases the speed but also improves the accuracy. It reduces the effort of finding a match for a face in the databases to almost a half. The process like any pattern recognition framework needs to extract the useful features. A novel gender identification algorithm is proposed which is based on the facial features of a person. Viola Jones object detection technique is used for face detection from an image, and the relevant facial features are extracted using Topographic Independent Component Analysis. An SVM based classifier is trained using the calculated feature vector to determine whether the person in the image is a male or a female.

Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Topographic Independent Component Analysis reveals random scrambling of orientation in visual space.

Neurons at primary visual cortex (V1) in humans and other species are edge filters organized in orientation maps. In these maps, neurons with similar orientation preference are clustered together in iso-orientation domains. These maps have two fundamental properties: (1) retinotopy, i.e. correspondence between displacements at the image space and displacements at the cortical surface, and (2) a trade-off between good coverage of the visual field with all orientations and continuity of iso-orientation domains in the cortical space. There is an active debate on the origin of these locally continuous maps. While most of the existing descriptions take purely geometric/mechanistic approaches which disregard the network function, a clear exception to this trend in the literature is the original approach of Hyvärinen and Hoyer based on infomax and Topographic Independent Component Analysis (TICA). Although TICA successfully addresses a number of other properties of V1 simple and complex cells, in this work we question the validity of the orientation maps obtained from TICA. We argue that the maps predicted by TICA can be analyzed in the retinal space, and when doing so, it is apparent that they lack the required continuity and retinotopy. Here we show that in the orientation maps reported in the TICA literature it is easy to find examples of violation of the continuity between similarly tuned mechanisms in the retinal space, which suggest a random scrambling incompatible with the maps in primates. The new experiments in the retinal space presented here confirm this guess: TICA basis vectors actually follow a random salt-and-pepper organization back in the image space. Therefore, the interesting clusters found in the TICA topology cannot be interpreted as the actual cortical orientation maps found in cats, primates or humans. In conclusion, Topographic ICA does not reproduce cortical orientation maps.

A Novel Method for Feature Extraction and Automatic Recognition of Tire Defects Using Independent Component Analysis

In order to extract the features of tire defects in X-ray images, Linear transformation is often performed on the images. However, Gabor and wavelet transformation are predefined and unchangeable, and their basic functions can’t be adapted to the characteristics of defect images. So we propose a new approach using Independent Component Analysis (ICA) and Topographic Independent Component Analysis (TICA) reconstruction algorithm to extract the features of tire defects and apply automatic recognition of tire defects. First, the basis functions and filters which adapt to the characteristics of defect images are estimated adaptively using ICA and TICA from the tire defect library. Then, the tire defect images are filtered to extract features. Finally, the samples are classified by a nonlinear support vector machine (SVM). Experimental results show that the proposed algorithm has very high recognition rates for shape defects, texture defects and other defects. Its total recognition rate can be as high as 95.76%.

An unsupervised feature learning framework for basal cell carcinoma image analysis

The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model. This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis. The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation. The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem.

An integrated approach for head gesture based interface

The head pose and movement of a user is closely related with his/her intention and thought, recognition of such information could be useful to develop a natural and sensitive user–wheelchair interface. This paper presents an original integrated approach to a head gesture based interface (HGI) which can perform both identity verification and facial pose estimation. Identity verification is performed by two-factor face authentication which is implemented by the combination of topographic independent component analysis (TICA) and multispace random projection (MRP). Modified synergetic computer with melting (Modified SC-MELT) is introduced to classify facial poses. Motion profile generator (MPG) is thoroughly developed during the integration to convert each estimated facial pose sequence into motion control signal to actuate motor movements. The HGI is intended to be deployed as a user–wheelchair interface for disabled and elderly users in which only users with genuine face and valid token may be granted authorized access and hence pilot an electric powered wheelchair (EPW) using their faces. The integration has been verified under a number of experiments to justify the feasibility and performance of the proposed face-based control strategy.

On the application of (topographic) independent and tree-dependent component analysis for the examination of DCE-MRI data

In this contribution we investigate the applicability of different methods from the field of independent component analysis (ICA) for the examination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data from breast cancer research. DCE-MRI has evolved in recent years as a powerful complement to X-ray based mammography for breast cancer diagnosis and monitoring. In DCE-MRI the time related development of the signal intensity after the administration of a contrast agent can provide valuable information about tissue states and characteristics. To this end, techniques related to ICA, offer promising options for data integration and feature extraction at voxel level. In order to evaluate the applicability of ICA, topographic ICA and tree-dependent component analysis (TCA), these methods are applied to twelve clinical cases from breast cancer research with a histopathologically confirmed diagnosis. For ICA these experiments are complemented by a reliability analysis of the estimated components. The outcome of all algorithms is quantitatively evaluated by means of receiver operating characteristics (ROC) statistics whereas the results for specific data sets are discussed exemplarily in terms of reification, score-plots and score images.

Overcomplete topographic independent component analysis

Topographic and overcomplete representations of natural images/videos are important problems in computational neuroscience. We propose a new method using both topographic and overcomplete representations of natural images, showing emergence of properties similar to those of complex cells in primary visual cortex (V1). This method can be considered as an extension of model in Hyvärinen et al. [Topographic independent component analysis, Neural Comput. 13 (7) (2001) 1527–1558], which uses complete topographic representation. We utilize a sparse and approximately uncorrelated decompositions and define a topographic structure on coefficients (the dot products between basis vectors and whitened observed data vectors). The overcomplete topographic basis vectors can be learned via estimation of independent component analysis (ICA) model based on the prior assumption upon basis vectors. Computer simulations are provided to show the relationship between our model and the basic properties of complex cells in V1 cortex. The learned bases are shown to have better coding efficiency than ordinary topographic ICA (TICA) bases.

Defect detection in textile fabric images using subband domain subspace analysis

In this work, a new model that combines the concepts of wavelet transformation and subspace analysis tools, like independent component analysis (ICA), topographic independent component analysis (TICA), and Independent Subspace Analysis (ISA), is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the textural components of the textile image by preprocessing has increased the performance of the system. Based on this observation, in the present work, the aforementioned subspace analysis tools are applied to subband images. The feature vector of a subwindow of a test image is compared with that of a defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The increase performance that results from using wavelet transformation prior to subspace analysis has been discussed in detail. While it has been found that all subspace analysis methods lead to the same detection performances, as a further step, independent subspace analysis is used to classify the detected defects according to their directionalities.

Topographic independent component analysis based on fractal theory and morphology applied to texture segmentation

Texture analysis and segmentation is an important area in image processing. One can employ texture segmentation for quality control in processes related to skin-leather, textile or marble/granite industries, for example. In such a context, the topographic independent component analysis (TICA) is presented as a technique for texture segmentation in which the image base is obtained from the mixture matrix of the model by implementing a bank of statistical filters, which are capable to capture the inherent properties of each texture. Indeed, using the energy as topographic criterion, the TICA filter bank exhibits results that are similar to the independent component analysis (ICA) model, as it has been already shown in the literature. In this paper, we show that using energy and morphologic fractal texture descriptors as topographic criterion those results are improved, in the sense that the segmentation error and the amount of filters are reduced, for the same textures.

A Novel Constrained Topographic Independent Component Analysis for Separation of Epileptic Seizure Signals

Blind separation of the electroencephalogram signals (EEGs) using topographic independent component analysis (TICA) is an effective tool to group the geometrically nearby source signals. The TICA algorithm further improves the results if the desired signal sources have particular properties which can be exploited in the separation process as constraints. Here, the spatial-frequency information of the seizure signals is used to design a constrained TICA for the separation of epileptic seizure signal sources from the multichannel EEGs. The performance is compared with those from the TICA and other conventional ICA algorithms. The superiority of the new constrained TICA has been validated in terms of signal-to-interference ratio and correlation measurement.

EEG/MEG Signal Processing

EEG/MEG Signal Processing

Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Recently, a new paradigm in ICA emerged, that of finding "clusters" of dependent components. This intriguing idea found its implementation into two new ICA algorithms: tree-dependent and topographic ICA. For fMRI, this represents the unifying paradigm of combining two powerful exploratory data analysis methods, ICA and unsupervised clustering techniques. For the fMRI data, a comparative quantitative evaluation between the two methods, tree-dependent and topographic ICA, was performed. The comparative results were evaluated by (1) task-related activation maps, (2) associated time courses, and (3) ROC study. The most important findings in this paper are that (1) both tree-dependent and topographic ICA are able to identify signal components with high correlation to the fMRI stimulus, and that (2) topographic ICA outperforms all other ICA methods including tree-dependent ICA for 8 and 9 ICs. However for 16 ICs, topographic ICA is outperformed by tree-dependent ICA (KGV) using as an approximation of the mutual information the kernel generalized variance. The applicability of the new algorithm is demonstrated on experimental data.

Pixel-based and region-based image fusion schemes using ICA bases

The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this paper, the authors test the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases in image fusion. The bases are obtained by offline training with images of similar context to the observed scene. The images are fused in the transform domain using novel pixel-based or region-based rules. The proposed schemes feature improved performance compared to traditional wavelet approaches with slightly increased computational complexity.

Learning multiview face subspaces and facial pose estimation using independent component analysis

An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.

Representation for dialect recognition using topographic independent component analysis

In dialect speech recognition, the feature of tone in one dialect is subject to changes in pitch frequency as well as the length of tone. It is beneficial for the recognition if a representation can be derived to account for the frequency and length changes of tone in an effective and meaningful way. In this paper, we propose a method for learning such a representation from a set of unlabeled speech sentences containing the features of the dialect changed from various pitch frequencies and time length. Topographic independent component analysis (TICA) is applied for the unsupervised learning to produce an emergent result that is a topographic matrix made up of basis components. The dialect speech is topographic in the following sense: the basis components as the units of the speech are ordered in the feature matrix such that components of one dialect are grouped in one axis and changes in time windows are accounted for in the other axis. This provides a meaningful set of basis vectors that may be used to construct dialect subspaces for dialect speech recognition.

MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.

Topographic independent component analysis as a model of V1 organization and receptive fields

Independent component analysis (ICA) has been recently used as a model of natural image statistics and V1 simple cell receptive fields. Here we show how to extend the ICA model to explain V1 topography as well. This is done by relaxing the independence assumption and ordering the basis vectors so that vectors with strong higher-order correlations are near each other. This is a new principle of topographic organization, and may be more relevant to natural image statistics than the more conventional topographic ordering based on Euclidean distances. For example, our ordering leads to simultaneous emergence of complex cell properties: topographic neighborhoods act like complex cells.