Otman A. Basir

Farthest point distance: A new shape signature for Fourier descriptors

Shape description is an important task in content-based image retrieval (CBIR). A variety of techniques have been reported in the literature that aims to represent objects based on their shapes. Each of these techniques has its pros and cons. Fourier descriptor (FD) is one of these techniques a simple, yet powerful technique that offers attractive properties such as rotational, scale, and translational invariance. Shape signatures, which constitute an essential component of Fourier descriptors, reduce 2-D shapes to 1-D functions and hence facilitate the process of deriving invariant shape features using the Fourier transform. A good number of shape signatures have been reported in the literature. These shape signatures lack important shape information, such as corners, in their representations. This information plays a major role in distinguishing between different shapes. In this paper, we present the farthest point distance (FPD), a novel shape signature that includes corner information to enhance the performance of shape retrieval using Fourier descriptors. The signature is calculated at each point on a shape contour. This signature yields distances calculated between the different shape corners, and captures points within the shape at which the human focuses visual attention in order to classify shapes. To reach a comprehensive conclusion about the merit of the proposed signature, the signature is compared against eight popular signatures using the well-known MPEG-7 database. Furthermore, the proposed signature is evaluated against standard boundary- and region-based techniques: the curvature scale space (CSS) and the Zernike moments (ZM). The FPD signature has demonstrated superior overall performance compared with the other eight signatures and the two standard techniques.

Phase-based optimal image thresholding

Abstract The automatic binarization of gray-level images or the automatic determination of an optimum threshold value that separates objects from their background is still a difficult and challenging problem in many image processing applications. The difficulty may arise due to a number of factors, including, poor contrast, high noise to signal ratio, complex patterns, and/or variable modalities in the gray-scale histograms. In this paper an algorithm for determining an optimum image thresholding value is proposed. Phase correlation between the gray-level image and its binary counterpart is defined as a function of the thresholding parameter. The optimum thresholding problem is then constructed as a problem of optimization where the objective is to find a threshold value that maximizes the phase correlation between the two images. Experimental results to compare the proposed algorithm to the various thresholding techniques are also presented.

A New Shape Signature for Fourier Descriptors

Shape-based image description is an important approach to content-based image retrieval (CBIR). A variety of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its advantages and disadvantages. Fourier descriptor (FD), a simple yet powerful technique, has attractive properties such as rotational, scale, and translational invariance. In this paper we investigate this technique and present a novel shape registration method for extracting Fourier descriptors. When evaluated against curvature scale space (CSS) and Zernike moments (ZM) in shape-based image retrieval, the proposed technique exhibits superior performance.

Invariant curvature-based Fourier shape descriptors

Shape descriptors have demonstrated encouraging potential for retrieving images based on image content, and a number of them have been reported in the literature. Nevertheless, most of the reported descriptors are still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on a sound theoretical foundation and also have the advantages of computational efficiency and attractive invariance properties. This paper proposes a new curvature-based Fourier descriptor (CBFD) for shape retrieval. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as curvature-scale image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the curvature-scale image. This method allows the descriptor to capture the detailed dynamics of the shape curvature and enhance the efficiency of the shape-matching process. Experiments using the widely known MPEG-7 databases in conjunction with a created noisy database have been conducted in order to compare the performance of the proposed descriptor with six commonly used shape-retrieval descriptors: curvature-scale-space descriptor (CSSD), angular radial transform descriptors (ARTD), Zernike moment descriptors (ZMD), radial Tchebichef moment descriptors (RTMD), generic Fourier descriptor (GFD), and the 1-D Fourier descriptor (1-FD). The performance of the proposed descriptor has surpassed that of many of these notable descriptors.

Shape-based image retrieval using pair-wise candidate co-ranking

Shape-based image retrieval is one of the most challenging aspects in Content-Based Image Retrieval (CBIR). A variety of techniques are reported in the literature that aim to retrieve objects based on their shapes; each of these techniques has its advantages and disadvantages. In this paper, we propose a novel scheme that exploits complementary benefits of several shape-based image retrieval techniques and integrates their assessments based on a pairwise co-ranking process. The proposed scheme can handle any number of CBIR techniques; however, three common techniques are used in this study: Invariant Zernike Moments (IZM), Multi-Triangular Area Representation (MTAR), and Fourier Descriptor (FD). The performance of the proposed scheme is compared with that of each of the selected techniques. As will be demonstrated in this paper, the proposed co-ranking scheme exhibits superior performance.

A novel curvature-based shape Fourier Descriptor

Shape descriptors have demonstrated encouraging potential in retrieving images based on image content. A number of shape descriptors have been reported in the literature. Nevertheless, most of the reported descriptors still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on sound theoretical foundation, and possess computational efficiency and attractive invariance properties. In this paper, we propose a novel Fourier descriptor based on contour curvature. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as Curvature-Scale Image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the Curvature-Scale Image. This allows the descriptor to capture detailed dynamics of the shape curvature and enhance the efficiency of the shape matching process. Experiments using images from the MPEG-7 database have been conducted to compare the performance of the proposed descriptor with the Curvature- Scale-Space Descriptor (CSSD), the Generic Fourier Descriptor (GFD), and the 1-D Fourier Descriptor (1-FD). The proposed descriptor demonstrated superior performance.

Information fusion in a cooperative multi-agent system for web information retrieval

As an attempt to solve some contemporary web information retrieval problems, a construction of a cooperative multiagent system is proposed. This. paper introduces the system and presents the use of a, unique aggregation and fusion technique that is employed to attain reliable delivery performance. The intelligent methodology to fuse agents decisions, the team consensus approach, models the interaction and bring a society into a consensus. After each agent in the group gathers information relevant to a users query, the group engages in an uncertainty estimation stage. This process allows each agent to assess its self-uncertainty and the conditional uncertainties of other agents. The procedure facilitates the computation of a weighting scheme that operates recursively on information collected by these agents until the group reaches a consensus. Whenever a new task is received, the uncertainty estimates of agent are updated and used to compute a new weighting scheme.

Opportunistic calibration of smartphone orientation in a vehicle

Modern smartphones are globally ubiquitous. As such, an increasing number of drivers have their smartphone in their vehicle while driving. These phones are equipped with powerful sensing, processing, and communication capabilities. This provides an opportunity to deploy smartphones in modern telematics and mobile telemetry technologies to enable the collection of driving data. Such data can be exploited to obtain insights regarding the vehicle driving patterns as well as the drivers skills. These insights are valuable in many applications including the usage-based insurance, young driver coaching, and fleet management solutions. However, the sensory data provided by a smartphone must be reoriented with respect to the vehicle to be utilized in such applications. This requires the orientation of the smartphone relative to the vehicle reference system to be estimated through a calibration process. Furthermore, the orientation of a smartphone can vary at any time during a trip due to extraneous factors such as user interaction. This makes the orientation calibration process a challenging task. This paper describes an opportunistic calibration method that continuously monitors a smartphone orientation and compensates for its variation, as necessary. The proposed method relies on the probabilistic fusion of built-in sensors; in particular, the GPS, accelerometer, gyroscope, and magnetometer. The extensive experiments conducted using real-world driving data illustrate the effectiveness of the proposed opportunistic calibration method.

A consensus-based fusion algorithm in shape-based image retrieval

Shape-based image retrieval techniques are among the most successful content-based image retrieval (CBIR) approaches. In recent years, the number of shape-based image retrieval techniques has dramatically increased; however, each technique has both advantages and shortcomings. This paper proposes a consensus-based fusion algorithm to integrate several shape-based image retrieval techniques so as to enhance the performance of the image retrieval process. In this algorithm, several techniques work as a team: they exchange their ranking information based on pair-wise co-ranking to reach a consensus that will improve their final ranking decisions. Although the proposed algorithm handles any number of CBIR techniques, only three common techniques are used to demonstrate its effectiveness. Several experiments were conducted on the widely used MPEG-7 database. The results indicate that the proposed fusion algorithm significantly improves the retrieval process.

The optimum automatic thresholding using the phase of Zernike moments

A new technique for automatic thresholding of images has been introduced. This technique is based on maximizing the correlation between Zernike moments phases of the gray-level and binary images of the same objects. This technique of gray level thresholding is unimodal. Thresholding using Zernike moments would be of interest to pattern recognition applications where Zernike moments are used as features. The experimental results show that correlating the phases of Zernike moments yields the optimal threshold values. These results also indicate the robustness and stability of the technique when dealing with noisy sample images.