Yasser Ebrahim

Shape representation and description using the Hilbert curve

In this paper, a novel linear-time approach to shape representation and description is presented. The object shape is captured by scanning the object image using a space-filling curve (SFC). The resulting vector is smoothed, using wavelet approximation, and sampled. In addition, the concept of key feature points (KFPs) is introduced to utilize a priori information about the classification of the images in the database in optimizing the representation of the objects within each class. The proposed technique achieves a recognition rate of 88.3% on the MPEG-7 core experiment part B. On the Kimia-99 and Kimia-216 datasets, a precision average of 95.6% is attained. Retrieval rates of 94.2% and 95.6% are achieved on the gray-scale and binary versions of the ETH-80 dataset, respectively.

Managing uncertainty: modeling users in location-tracking applications

Uncertainty-management techniques that ignore the distinctiveness of individuals will either fail or incur a high cost in system resources. Location-tracking applications must consider the individual users characteristics, habits, and preferences to estimate his or her location more effectively. We discuss human-controlled moving objects, called roving users. A typical RU application tracks each RUs location to answer queries about the persons whereabouts at any particular time. We also discuss about the belief networks that models the user habits. We also discuss about the belief networks that models the user habits.

An Efficient Shape Representation and Description Technique

In this paper, we present a novel approach to shape representation and description based on the combination of the Hilbert space filling curve and wavelet analysis. Our objective is to capitalize on the localization-preserving nature of the Hilbert space filling curve and the approximation power of the wavelet transform. The object image is scanned using the Hilbert curve and the resulting vector is smoothed using the wavelet transform and sampled. The technique is O(N) for both representation and comparison. We present some experimental results on the MPEG-7 dataset, Kimia-99 dataset, ETH-80 dataset, and a logo dataset.

A Template-Based Shape Representation Technique

In this paper we present a novel approach to shape representation based on correlating a set of object Regions of Interest (RoI) with a set of shape templates. The resultant correlations are the shape features used to build a Template-based Shape Feature Vector (TSFV) that represents the shape of the object. For each class of objects, a set of Main Shape Features (MSFs) is determined so that only the most descriptive features are used when comparing shapes. The proposed technique is tested on two benchmark databases, Kimia-99 and Kimia-216 and is shown to produce competitive results.

A Roving User Modeling Framework for Location Tracking Applications

With the advent of GPS systems, location tracking has become an essential component of fleet management systems. Reliable location estimation of the location of each member of the fleet is critical for optimum operation of organizations such as the police force, taxi-cab, and trucking companies. In this paper a framework for modeling drivers in such environments for location tracking purposes is proposed. The frameworks goal is to increase the LTSs ability to accurately answer queries about the whereabouts of its fleet members in the past, present, and the future

Significantly improving scan-based shape representations using rotational key feature points

In a previous paper we have presented the idea of representing the shape of a 2D object by scanning it following a Hilbert curve then performing wavelet smoothing and sampling. We also introduced the idea of using only a subset of the resulting signature for comparison purposes. We called that set the Key Feature Points (KFPs). In this paper we introduce the idea of taking the KFPs over a number of views of the original shape. The proposed improvement results in a significant increase in recognition rates when applied to the MPEG-7 and ETH-80 data sets when the Hilbert scan is used. Similar improvement is achieved when the raster scan is used.

Shape Matching Using a Novel Warping Distance Measure

This paper presents a novel distance measure, the Minimum Landscape Distance (MLD). MLD is a warping distance measure that provides a non-linear mapping between the elements in one sequence to those of another. Each element in one sequence is mapped to that with the highest neighborhood structural similarity (landscape) in the other sequence within a window. Different window sizes are tested on a number of datasets and a linear relationship between the window size and the sequence size is discovered. Experimental results obtained on the Kimia-99 and Kimia-216 datasets show that MLD is superior to the Euclidean, correlation, and Dynamic Time Warping (DTW) distance measures.

A view-based 3D object shape representation technique

In this paper we present a novel approach to 3D shape representation and matching utilizing a set of shape representations for 2D views of the object. The proposed technique capitalizes on the localizationpreserving nature of the Hilbert space filling curve and the approximation capabilities of the Wavelet transform. Each 2D view of the object is represented by a concise 1D representation that can be used to search an image database for a match. The shape of the 3D image is represented by the set of 1D representations of its 2D views. Experimental results, on a subset of the Amsterdam Library of Object Images (ALOI) dataset, are provided.

Moving object modelling approach for lowering uncertainty in location tracking systems

This paper introduces the concept of Moving Object (MO) modelling as a means of managing the uncertainty in the location tracking of human moving objects travelling on a network. For previous movements of the MOs, the uncertainty stems from the discrete nature of location tracking systems, where gaps are created among the location reports. Future locations of MOs are, by definition, uncertain. The objective is to maximize the estimation accuracy while minimizing the operating costs.

ROOTS, The ROving Objects Trip Simulator

This paper introduces a new trip simulator, ROOTS. ROOTS creates moving objects with distinct characteristics in terms of driving style and route preference. It also creates a road network and associates each road with some characteristics. The route taken and the moving object speeds during the trip are determined based on both the characteristics of the moving object, those of the road being travelled, and other contextual data such as weather conditions and time of day.