Data Mining - Concepts and Techniques.

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Ergonomic man-machine interface incorporating adaptive pattern recognition based control system

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

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Advances in Spectral-Spatial Classification of Hyperspectral Images

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (VCR), medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Adaptive pattern recognition based control system and method

Tracking assembly lines in manufacturing to provide assistance is one of the essential requirement in Smart Industry. Nevertheless, these given assistance and guidelines should be offered to operators when needed. Otherwise, it can be deemed patronising in some cases, e.g., experienced operators may require less assistance than junior operators. Therefore, to provide tailored guidance and assistance in assembly lines, the operators' experience-level should be classified at different levels. In this paper, we introduce three scenarios to achieve the classification of operators expert levels in a real case study (micro-step time-series data from a factory assembly line). We implement a Convolutional Neural Network model for time-series classification, using 5 convolutional layers, max-pooling layers and 5 dense layers with dropout to avoid overfitting. We compare the results of our approach with the ground truth and also with other classifiers as K-nearest neighbours, Random Forest and Naive Bayes classifier. Results show an accuracy of 77 to 98% and 71 to 88% for two of considered scenarios.

/pdf/operator-s-experience-level-classification-based-on-micro-3bqw9zh5y1.pdf

Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories

Markov random fields as a SAR texture descriptor for the delineation of urban zones

In this paper we propose a new data model for imperfect geographic information: a twofold fuzzy region model; which is an extension of both the fuzzy region model and the egg/yolk model We show how it is meaningful and practically useful in representing the knowledge that results from classified pixel data By defining different operators on the model we are able to develop an imprecise quality report for geographic databases that actually uses the imperfect classification data as a reference This allows us, despite the large amount of imperfection in geographic classifications, to use them for rather accurate error detection on geographic databases

A Twofold Fuzzy Region Model for Imprecise Quality Control of Geographic Information

This paper proposes an optimization framework for urban transportation networks’ (re-)design which explicitly takes into account the specific decision-making processes of ordinary users and logistic operators. Ordinary users are typically commuters whose travels consist of well-defined pairs of origin and destination points, while logistic operators make deliveries at multiple locations. Obviously, these two user classes have different objectives and scopes of action. These differences are seldom considered in traffic research since most models aggregate the flow demand in OD matrices and use assignment models to predict the response of all users as if the dynamics of their optimization processes were of the same nature. This work demonstrates that better results can be achieved if the particular features of each user class are included in the models. It potentially improves the estimation of the responses and allows managers to shape their control measures to address specific user needs.

Exploring the issue of integrating logistics and traffic control in urban areas

The topic of registering multi- and hyperspectral imagery is well studied in literature. However when the registration must be done between multispectral images and vector data, the literature is more limited. In this paper we focus on registering aerial images in the thermal (IR) band, and vector data delineating houses and other man-made structures in the same region. This differs from classical registration because first of all, the vector data is not in the same format as the thermal image (i.e. vectors versus pixels), although it can be rasterized initially. Secondly, feature points fail to match between the two representations while intensity based methods also have problems due to the large changes between the rasterized vector and thermal images. We present a method based on a modified version of mutual information that outperforms existing methods for these specific inputs.

Sidharta Gautama

Papers

Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories

Markov random fields as a SAR texture descriptor for the delineation of urban zones

A Twofold Fuzzy Region Model for Imprecise Quality Control of Geographic Information

Exploring the issue of integrating logistics and traffic control in urban areas

Registration of vector data and aerial thermal images using modified mutual information