The framework of Support Vector Machines is becoming extremely popular in the field of statistical pattern classification. Kalman filters have been used for long for doing tracking. In this paper we have investigated a technique which couples Kalman filter closely with the SVM. The problem of object tracking can be seen as a pattern recognition problem in which we are looking for a pattern (obj...

The possibility of automatic understanding of customers’ shopping behavior and acting according to their needs is relevant in the marketing domain, attracting a lot of attention lately. In this work, we focus on the task of automatic assessment of customers’ shopping behavior, by proposing a multilevel framework. The framework is supported at low-level by different types of cameras, which are s...

The study of multiple classifier systems has become an area of intensive research in pattern recognition recently. Also in handwriting recognition, systems combining several classifiers have been investigated. In this paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. Those new methods are evaluated and compared to existing approache...

In this paper, we present a new hybrid approach for isolated spoken word recognition using Hidden Markov Model models (HMM) combined with Dynamic time warping (DTW). HMM have been shown to be robust in spoken recognition systems. We propose to extend the HMM method by combining it with the DTW algorithm in order to combine the advantages of these two powerful pattern recognition technique. In t...

We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact...

The study of multiple classifier systems has become an area of intensive research in pattern recognition recently. Also in handwriting recognition, systems combining several classifiers have been investigated. In this paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. Those new methods are evaluated and compared to existing approache...

Actual data collected from real deployments is ultimately the best data that can be had and used in evaluating systems and new concepts. However, available data may not be specific enough to drive certain evaluation goals. Therefore, it is necessary to propose, as an alternative, a method to generate synthetic data for pervasive spaces. A first step in this direction is trying to simulate a cha...

In this paper we describe a Hidden Markov Model (HMM) based writer independent handwriting recognition system. A combination of signal normalization preprocessing and the use of invariant features makes the system robust with respect to variability among di!erent writers as well as di!erent writing environments and ink collection mechanisms. A combination of point oriented and stroke oriented f...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

Oslo in 1988. She is currently working as a researcher at the Norwegian Computing Center and has been involved in several projects concerning document image analysis and machine vision. Her research interests lie mainly within the various applications of statistical pattern recognition. Summary In this paper, we demonstrate that hidden Markov chains have a potential for use in diierent image an...