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Lifting Wavelet Based Cognitive Vision System
Get PDFThis paper presents a cognitive vision system based on the learning of lifting wavelets. The learning process consists of four steps: 1. Extract training and query object images automatically from adjacent video frames using our proposed cosine-maximization method; 2. Compute autocorrelation vectors from the extracted training images, and their discriminant vectors by linear discriminant analysis; 3. Map the autocorrelation vectors onto the discriminant vector space to obtain feature vectors; 4. Learn lifting parameters in the feature vectors using the idea of discriminant analysis. The recognition of a query object is performed by measuring cosine distance between its feature vector and the feature vectors for training object images. Our experimental results on vehicle types recognition show that the proposed system performs better than the discriminant analysis of original images
Application of SOM in a health evaluation system
Get PDFA health evaluation system was constructed which visualizes the living habits and health state from a person's checkup list by using the feature of SOM that multi-dimensional data can be mapped onto a two-dimensional surface. Here, three examples cases are reported. A change to the health region of the map by taking medication was visualized by the SOM from the conventional numerical expression. Also, the specific sick record converges towards the sick region of the map when the disease progresses. However, it was shown and visualized for the sick record not to converge in the case of the metastasis of a cancer even if for the same examinee, the cancer has progressed. Finally, for the display of the health point mark, and the display of the sick record, the spherical surface SOM, is demonstrated to be suited in the visualization
Classification of similar productivity zones in the sugar cane culture using clustering of SOM component planes based on the SOM distance matrix
Get PDFA technique called component planes is commonly used to visualize variables behavior with Self Organizing Map (SOM). A methodology to clustering the component planes based on the SOM distance matrix is presented. This methodology is used in order to classify zones with similar agro-ecological conditions in the sugar cane culture. Analyzing the obtained groups it was possible to extract new knowledge about the relationship between the agro-ecological variables and productivity
Dynamical Equilibrium, trajectories study in an economical system : the case of the labor market
Get PDFThe paper deals with the study of labor market dynamics, and aims to characterize its equilibriums and possible trajectories. The theoretical background is the theory of the segmented labor market. The main idea is that this theory is well adapted to interpret the observed trajectories, due to the heterogeneity of the work situations. The Kohonen algorithm is used to define the segments of the labor market. The trajectories are reconstructed by means of a non homogeneous Markov model and classified by using a Kohonen algorithm again
Functional SOM for variable-length signal windows
Get PDFFunctional data, often sampled at high frequency, lead to high-dimensional vectors. The curse of dimensionality makes the latter difficult to handle with standard data analysis tools. Functional data analysis tools take profit of the functional nature of data by projecting them on a smooth basis. This paper shows how to extend functional Self-Organizing Maps (SOM) to signal windows having different lengths. This technique may be applied for example on signal sampled regularly, but for which the duration of each signal is varying; an example concerns electrocardiography (ECG), where the signal is usually cut according to the variable period between two heart beats
Path finding on a spherical SOM using the distance transform and floodplain analysis
Get PDFData visualization has become an important tool for analyzing very complex data. In particular, spatial visualization enables users to view data in a intuitive manner. It has typically been used to externalize clusters and their relationships which exist in highly complex multidimensional data. We envisage that not only cluster formation and relationships but also other types of information, such as temporal changes of datum, can be extracted through the spatialization. In this paper, we investigate an application of trajectory/path analysis carried out using a Self-Organizing Map as a spatialization method. We propose an application of distance transformations to the Geodesic Self-Organizing Map. This new approach allows a user to visually inspect the trajectory of multidimensional knowledge pieces on a two-dimensional space. The trajectories discovered through this approach are essentially the shortest paths between two points on the Self-Organizing Map. However, those paths might go outside of the input dataspace due to the connectivity of neurons imposed by the grid structure. We also present a method to find the shortest path, which falls within the input dataspace using simple floodplain analysis
Self-Organized Ordering of Terms and Documents in NSF Awards Data
Get PDFWe present the results of an analysis of a text corpus of 129,000 abstracts of NSF-sponsored basic research projects between years 1990 and 2003. The methods used in the analysis include term extraction based on a reference corpus and an entropy measure, and the Self-Organizing Map algorithm for the formation of a term map and a document map. Methodologically, the basic approach is based on earlier developments, such as word category maps and the WEBSOM method, but in the level of details, we report several new aspects and quantitative comparison results between methodological variants in this article. The data covers a quite large proportion of US-based scientific research during recent years. The analysis results indicate the basic patterns discernable in the data, both at the level of the awards and at the terminology used in them
Self-Organizing Map with False Neighbor Degree between Neurons for Effective Self-Organization
Get PDFIn the real world, it is not always true that the nextdoor house is close to my house, in other words, "neighbors" are not always "true neighbors". In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain the more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid
3D Modeling of Objects by Using Resilient Neural Network
Get PDFCamera Calibration (CC) is a fundamental issue for Shape-Capture, Robotic-Vision and 3D Reconstruction in Photogrammetry and Computer Vision. The purpose of CC is the determination of the intrinsic parameters of cameras for metric evaluation of the images. Classical CC methods comprise of taking images of objects with known geometry, extracting the features of the objects from the images, and minimizing their 3D backprojection errors. In this paper, a novel implicit-CC model (CC-RN) based on Resilient Neural Networks has been introduced. The CC-RN is particularly useful for 3D reconstruction of the applications that do not require explicitly computation of physical camera parameters in addition to the expert knowledge. The CC-RN supports intelligent-photogrammetry, photogrammetron. In order to evaluate the success of the proposed implicit-CC model, the 3D reconstruction performance of the CC-RN has been compared with two different well-known implementations of the Direct Linear Transformation (DLT). Extensive simulation results show that the CC-RN achieves a better performance than the well-known DLTs in the 3D backprojection of scene
A System for Continuous Learning of Visual Concepts
Get PDFWe present an artificial cognitive system for learning visual concepts. It comprises of vision, communication and manipulation subsystems, which provide visual input, enable verbal and non-verbal communication with a tutor and allow interaction with a given scene. The main goal is to learn associations between automatically extracted visual features and words that describe the scene in an open-ended, continuous manner. In particular, we address the problem of cross-modal learning of visual properties and spatial relations. We introduce and analyse several learning modes requiring different levels of tutor supervision