In performing surveillance using a sensor network, data association and registration are two essential processes which associate data from different sensors and align them in a common coordinate ...system. While these two processes are usually addressed separately, they actually affect each other. That is, registration requires correctly associated data, and data with sensor biases will result in wrong association. We present a novel joint sensor association, registration, and fusion approach for multisensor surveillance. In order to perform registration and association together, the expectation-maximization (EM) algorithm is incorporated with the Kalman filter (KF) to give simultaneous state and parameter estimates. Computer simulations are carried out to evaluate the performances of the proposed joint association, registration, and fusion method based on EM-KF.
Measuring similarity is of a great interest in many research areas such as in data sciences, machine learning, pattern recognition, text analysis and information retrieval to name a few. Literature ...has shown that possibility is an attractive notion in the context of distinguishability assessment and can lead to very efficient and computationally inexpensive learning schemes. This paper focuses on determining the similarity between two possibility distributions. A review of existing similarity measures within the possibilistic framework is presented first. Then, similarity measures are analyzed with respect to their capacity to satisfy a set of required properties that a similarity measure should own. Most of the existing possibilistic similarity measures produce undesirable outcomes since they generally depend on the application context.A new similarity measure, called InfoSpecificity, is introduced and the similarity measures are categorized into three main methods: morphic-based, amorphic-based and hybrid. Two experiments are being conducted using four benchmark databases. The aim of the experiments is to compare the efficiency of the possibilistic similarity measures when applied to real data. Empirical experiments have shown good results for the hybrid methods, particularly with the InfoSpecificity measure. In general, the hybrid methods outperform the other two categories when evaluated on small-size samples, i.e., poor-data context (or poor-informed environment) where possibility theory can be used at the greatest benefit.
In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint ...Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS's highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.
High-level information fusion is the ability of a fusion system to capture awareness and complex relations, reason over past and future events, utilize direct sensing exploitations and tacit reports, ...and discern the usefulness and intention of results to meet system-level goals. This authoritative book serves a practical reference for developers, designers, and users of data fusion services that must relate the most recent theory to real-world applications. This unique volume provides alternative methods to represent and model various situations and describes design component implementations of fusion systems. Designers find expert guidance in applying current theories, selecting algorithms and software components, and measuring expected performance of high-level fusion systems.
In multi-sensor tracking, registration is expected to be performed at the track level instead of the measurement level especially for the distributed sensor networks. However, registration at the ...track level becomes more difficult due to the implicit sensor biases hidden behind the local tracks. We propose a pseudo-measurement approach to solve the simultaneous registration and fusion problem at the track level. A pseudo-measurement equation is derived from the local trackers, which explicitly reveals the relationship between the pseudo-measurements and the sensor biases in a closed-form expression. The resulting registration model then allows us to formulate the track registration and fusion as a maximum likelihood (ML) estimation problem. We propose using the expectation maximization (EM) approach to perform track registration and fusion simultaneously. Both batch and recursive EM algorithms are developed, accompanied by the performance analysis. Simulation results demonstrate that both EM algorithms are capable of providing accurate estimates. Moreover, we apply the proposed method to an air surveillance radar network which suffers from relatively serious registration problems. The proposed method is verified to effectively fuse and register the tracks generated by local radars and to provide a consistent air picture.
Both image registration and fusion can be formulated as estimation problems. Instead of estimating the registration parameters and the true scene separately as in the conventional way, we propose a ...maximum likelihood approach for joint image registration and fusion in this paper. More precisely, the fusion performance is used as the criteria to evaluate the registration accuracy. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. The expectation maximization algorithm is employed to solve this joint optimization problem. The Cramer-Rao bound (CRB) is then derived. Our experiments use several types of sensory images for performance evaluation, such as visual images, IR thermal images, and hyperspectral images. It is shown that the mean square error of estimating the registration parameters using the proposed method is close to the CRBs. At the mean time, an improved fusion performance can be achieved in terms of the edge preservation measure QAB / F , compared to the Laplacian pyramid fusion approach.
Recently, a measure of "total uncertainty" (TU) in Dempster-Shafer theory, based on the pignistic distribution called ambiguity measure (AM), have been modified. The resulting new measure has been ...simply referred as modified AM (MAM). In the literature, it has been shown that AM, in addition to showing some undesirable behaviors, has important drawbacks related to two essential properties for such measures: 1) subadditivity and 2) monotonicity. The MAM measure has been developed to solve the AM subadditivity problem, but this paper demonstrates that MAM suffers the same drawback as AM with respect to monotonicity. A measure of uncertainty that cannot meet the monotonicity requirement has an important drawback for its exploitation in operational contexts such as in analytics, information fusion, and decision support. This paper aims at identifying and discussing drawbacks of this type of measures (AM, MAM). Our main motivation is to insist upon the important requirement of monotonicity that a TU measure should possess to improve its potential of being used and trusted in applications. This discussion is due time since the monotonicity problem needs first to be solved to avoid building too high expectations for usefulness and potential exploitation of such measures in operational communities.
•A novel region-growing segmentation method based on possibilistic theory is proposed.•Region-growing process is iteratively performed at the possibilistic knowledge representation level.•Possibility ...theory allows adequate semantic knowledge modeling without huge constraints.•Validation is done in the context of pixel classification using both real and synthetic data.•Proposed approach shows remarkable stable behaviour during quantitative assessment.
This paper presents an image segmentation method imitating human focusing visual attention in image interpretation using possibilistic knowledge modeling concepts. The proposed pixel level method consists on the Iterative Possibilistic Knowledge Diffusion (IPKD) on immediate neighbourhood pixels. The advantage of this mechanism is to provide iterative diffusion of per-pixel certain knowledge to surrounding pixels in order to progressively refine the segmentation process. The diffusion process is achieved using image smoothing techniques such as Nagao and Gabor filtering, mean filtering and anisotropic diffusion. Those diffusion techniques are then compared in the possibilistic knowledge representation space. The merit of a possibilistic knowledge representation, rather than a grey-level sensor based representation, is demonstrated by both experimental and synthetic data. Producing the lowest error rates, possibilistic knowledge diffusion using Nagao filter is adopted for the approach assessment. Experimental results using synthetic images as well as mammographic images from MIAS (Mammographic Image Analysis Society) data-base, are performed in order to assess the efficiency of the proposed segmentation method according to the visual criterion as well as some quantitative criteria. IPKD's performance (in terms of recognition rate, 94.37% and global predictive rate, 92.18%) is compared with three relevant reference methods: level-set, Fuzzy C-Mean and region growing methods. The IPKD approach outperforms the other three methods, respectively, at the recognition rates of 89.77%, 84.43% and 88.11% and at the global predictive rates of 87.86%, 89.72% and 84.04%. Noise-sensitivity experiments have been conducted on synthetic as well as on real images. The proposed IPKD approach outperforms the three reference methods and in addition, exhibits a desired stability behaviour.
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In this study, we present a novel approach to estimating the Hurst exponent of time series data using a variety of machine learning algorithms. The Hurst exponent is a crucial parameter in ...characterizing long-range dependence in time series, and traditional methods such as Rescaled Range (R/S) analysis and Detrended Fluctuation Analysis (DFA) have been widely used for its estimation. However, these methods have certain limitations, which we sought to address by modifying the R/S approach to distinguish between fractional Lévy and fractional Brownian motion, and by demonstrating the inadequacy of DFA and similar methods for data that resembles fractional Lévy motion. This inspired us to utilize machine learning techniques to improve the estimation process. In an unprecedented step, we train various machine learning models, including LightGBM, MLP, and AdaBoost, on synthetic data generated from random walks, namely fractional Brownian motion and fractional Lévy motion, where the ground truth Hurst exponent is known. This means that we can initialize and create these stochastic processes with a scaling Hurst/scaling exponent, which is then used as the ground truth for training. Furthermore, we perform the continuous estimation of the scaling exponent directly from the time series, without resorting to the calculation of the power spectrum or other sophisticated preprocessing steps, as done in past approaches. Our experiments reveal that the machine learning-based estimators outperform traditional R/S analysis and DFA methods in estimating the Hurst exponent, particularly for data akin to fractional Lévy motion. Validating our approach on real-world financial data, we observe a divergence between the estimated Hurst/scaling exponents and results reported in the literature. Nevertheless, the confirmation provided by known ground truths reinforces the superiority of our approach in terms of accuracy. This work highlights the potential of machine learning algorithms for accurately estimating the Hurst exponent, paving new paths for time series analysis. By marrying traditional finance methods with the capabilities of machine learning, our study provides a novel contribution towards the future of time series data analysis.
This paper proposes an approach referred as: iterative refinement of possibility distributions by learning (IRPDL) for pixel-based image classification. The IRPDL approach is based on the use of ...possibilistic reasoning concepts exploiting expert knowledge sources as well as ground possibilistic seeds learning. The set of seeds is constructed by incrementally updating and refining the possibility distributions. Synthetic images as well as real images from the RIDER Breast MRI database are being used to evaluate the IRPDL performance. Its performance is compared with three relevant reference methods: region growing, semi-supervised fuzzy pattern matching, and Markov random fields. The IRDPL performance (in terms of recognition rate, 87.3%) is close to the Markovian method (88.8%) that is considered to be the reference in pixel-based image classification. IRPDL outperforms the other two methods, respectively, at the recognition rates of 83.9% and 84.7%. In addition, the proposed IRPDL requires fewer parameters for the mathematical representation and presents a reduced computational complexity.
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