Nasser Sherkat

Occupancy monitoring in intelligent environment through integrated wireless localizing aggents

The application of wireless localizing agents in the occupancy detection of a single-occupant ambient intelligent environment in the presence of visitors is addressed in this paper. A wireless sensor network constructed from sensory agents of PIR motion detection sensors and door contact sensors is employed to collect the occupancy data from different areas in the ambient intelligent environment. Additionally, an RSSI detection capability is integrated to create wireless localizing agents along with tagging the occupant as a mobile node to distinguish him/her from other occupants or visitors. It is shown that by using a wireless sensor network of localizing agents for the occupancy detection of the tagged occupant, the redundancy and noise in the occupancy signal is reduced; hence, the efficiency of occupancy detection in different areas of the environment is increased.

Word shape analysis for a hybrid recognition system

This paper describes two wholistic recognizers developed for use in a hybrid recognition system. The recognizers use information about the word shape. This information is strongly related to word zoning. One of the recognizers is explicitly limited by the accuracy of the zoning information extraction. The other recognizer is designed so as to avoid this limitation. The recognizers use very simple sets of features and fuzzy set based pattern matching techniques. This not only aims to increase their robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. Letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. Despite some remaining disambiguation problems, wholistic recognizers are found capable of outperforming the segmentation based recognizer. When working together in a hybrid system, the results are significantly higher than that of the individual recognizers. Recognition results are reported and compared.

Towards a Best Linear Combination for Multimodal Biometric Fusion

Owing to effectiveness and ease of implementation Sum rule has been widely applied in the biometric research field. Different matcher information has been used as weighting parameters in the weighted Sum rule. In this work, a new parameter has been devised in reducing the genuine/imposter distribution overlap. It is shown that the overlap region width has the best generalization performance as the weighting parameter amongst other commonly used matcher information. Furthermore, it is illustrated that the equal weighted Sum rule can generally perform better than the Equal Error Rate and d-prime weighted Sum rule. The publicly available databases: the NIST-BSSR1 multimodal biometric and Xm2vts score sets have been used.

Multiple word segmentation with interactive look-up for cursive script recognition

Cursive script recognition is commonly based on finding letters within a word and recognizing them separately. The segmentation process is ambiguous and difficult. A method which combines word segmentation and letter recognition with lexical look-up in order to cope with segmentation ambiguity is presented. Words are first segmented into small elements which are then put together using a database of their possible combinations to produce alternative segmentations. Letter recognition is performed on each letter candidate and lexical look-up is applied, interactively, to prune illegal word recognition results. Lexical look-up is used to postulate word endings for partially recognized words. This provides the means of catering for words with sloppy endings, some misspellings and recovering from some recognition errors. An online cursive script recognition system, based on the above method, is described and evaluated.<<ETX>>

Teleoperation through eye gaze (TeleGaze): A multimodal approach

Most mobile robot teleoperation require monitoring as well as controlling from a remote location. This engages both the hands and the eyes of the human operator for the whole duration of the operation. Aiming at minimizing the human body engagement by freeing the hands of the operator from the controlling task, previous works by the authors proved that both monitoring and controlling can be achieved using solely inputs from the operators eyes. The TeleGaze interface, which has been developed as a novel interface for teleoperation through eye gaze, enabled the operator to control a robotic platform, an onboard active vision and some aspects of the interface using eye-gaze tracking. However, the advantage of free hands using TeleGaze was accompanied by an increase in the tasks general workload causing some extra stress on the operator. In the aim of optimizing the human-robot interaction experience while using TeleGaze, a multimodal approach is believed to be necessary. Therefore, in addition to some further refinements in the design of the interface a multimodal TeleGaze system has also been developed. In this paper the multimodal version of TeleGaze using an accelerator pedal and the native version of TeleGaze using dwell time are both presented. Details of the refinements in the design of the interface and the results of a task-oriented evaluation for three different modes of interaction, including both modes of TeleGaze, are also included.

Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilized. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantized to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system.

Recognising letters in on-line handwriting using hierarchical fuzzy inference

The recognition of unconstrained handwriting has to cope with the ambiguity and variability of cursive script. Preprocessing techniques are often applied to on-line data before representing the script as basic primitives, resulting in the propagation of errors introduced during pre-processing. This paper therefore combines pre-processing of the data (i.e. tangential smoothing) and encoding into primitives (Partial Strokes) in a single step. Finding the correct character at the correct place (i.e. letter spotting) is the main problem in non-holistic recognition approaches. Many cursive letters are composed of common shapes of varying complexity that can in turn consist of other subshapes. In this paper, we present a production rule system using Hierarchical Fuzzy Inference in order to exploit this hierarchical property of cursive script. Shapes of increasing complexity are found on a page of handwriting until letters are finally spotted. Zoning is then applied to verify their vertical position. The performance of letter spotting is compared with an alternative method.

Error recovery in a blended style eye gaze and speech interface

In the work carried out earlier [1][2], it was found that an eye gaze and speech enabled interface was the most preferred form of data entry method when compared to other methods such as mouse and keyboard, handwriting and speech only. It was also found that several non-native United Kingdom (UK) English speaking speakers did not prefer the eye gaze and speech system due to the low success rate caused by the inaccuracy of the speech recognition component. Hence in order to increase the usability of the eye gaze and speech data entry system for these users, error recovery methods are required. In this paper we present three different multimodal interfaces that employ the use of speech recognition and eye gaze tracking within a virtual keypad style interface to allow for the use of error recovery (re-speak with keypad, spelling with keypad and re-speak and spelling with keypad). Experiments show that through the use of this virtual keypad interface, an accuracy gain of 10.92% during first attempt and 6.20% during re-speak by non-native speakers in ambiguous fields (initials, surnames, city and alphabets) can be achieved [3]. The aim of this work is to investigate whether the usability of the eye gaze and speech system can be improved through one of these three multimodal blended multimodal error recovery methods.

Handwriting style classification

Abstract.This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier.

Exploiting zoning based on approximating splines in cursive script recognition

Because of its complexity, handwriting recognition has to exploit many sources of information to be successful, e.g. the handwriting zones. Variability of zone-lines, however, requires a more flexible representation than traditional horizontal or linear methods. The proposed method therefore employs approximating cubic splines. Using entire lines of text rather than individual words is shown to improve the zoning accuracy, especially for short words. The new method represents an improvement over existing methods in terms of range of applicability, zone-line precision and zoning-classification accuracy. Application to several problems of handwriting recognition is demonstrated and evaluated.