Sharif M. A. Bhuiyan

Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation

A novel approach for bidimensional empirical mode decomposition (BEMD) is proposed in this paper. BEMD decomposes an image into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). In each iteration of the process, two-dimensional (2D) interpolation is applied to a set of local maxima (minima) points to form the upper (lower) envelope. But, 2D scattered data interpolation methods cause huge computation time and other artifacts in the decomposition. This paper suggests a simple, but effective, method of envelope estimation that replaces the surface interpolation. In this method, order statistics filters are used to get the upper and lower envelopes, where filter size is derived from the data. Based on the properties of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD). Simulation results demonstrate that FABEMD is not only faster and adaptive, but also outperforms the original BEMD in terms of the quality of the BIMFs.

Image Segmentation and Shape Analysis for Road-Sign Detection

This paper proposes an automatic road-sign recognition method based on image segmentation and joint transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular, triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs); 2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the unknown signs with the known reference road signs stored in the database. Experimental results on real-life images show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to translation, rotation, scale, and partial occlusions.

A novel approach of fast and adaptive bidimensional empirical mode decomposition

Bidimensional empirical mode decomposition (BEMD) techniques are associated with high computation time and other artifacts because of the application of two dimensional (2D) scattered data interpolation methods. In this paper, order statistics filters are employed to get the upper and lower envelopes in the BEMD process, instead of the surface interpolation. Based on the achieved characteristics of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD). Simulation results demonstrate that besides reducing the computation time, FABEMD outperforms the original BEMD in terms of the quality in some cases.

A customized Gabor filter for unsupervised color image segmentation

This paper presents work on accurate image segmentation utilizing local image characteristics. Image features are measured by employing an appropriate Gabor filter with adaptively chosen size, orientation, frequency and phase for each pixel. An image property called phase divergence is used for the selection of the appropriate filter size. Characteristic features related to the change in brightness, color, texture and position are extracted for each pixel at the selected size of the filter. In order to cluster the pixels into different regions, the joint distribution of these pixel features is modeled by a mixture of Gaussians utilizing three variants of the expectation maximization (EM) algorithm. The three different versions of EM used in this work for unsupervised clustering are: (1) penalized EM, (2) penalized stochastic EM, and (3) penalized inverse EM. Given the desired number of Gaussian mixture components, all three EM algorithms estimate the parameters of the mixture of Gaussians model that represents the joint distribution of pixel features. We determine the value of the number of models that best suits the natural number of clusters present in the image based on the Schwarz criterion, which maximizes the posterior probability of the number of groups given the samples of observation. This segmentation algorithm has been tested on the images of the Berkeley segmentation benchmark and the performance has demonstrated the effectiveness, accuracy and superiority of the proposed method.

BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION USING VARIOUS INTERPOLATION TECHNIQUES

Scattered data interpolation is an essential part of bidimensional empirical mode decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and the lower envelopes, respectively. The number of two-dimensional intrinsic mode functions resulting from the decomposition and their properties are highly dependent on the method of interpolation. Though a few methods of interpolation have been tested and/or applied to the BEMD process, many others remain to be tested. This paper evaluates the performance of some of the widely used surface interpolation techniques to identify one or more good choices of such methods for envelope estimation in BEMD. The interpolation techniques studied in this paper include various radial basis function interpolators and Delaunay triangulation based interpolators. The analysis is done first using a synthetic texture image and then using two different real texture images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters or factors of the BEMD process.

Automatic target detection in forward-looking infrared imagery via probabilistic neural networks

This paper presents a technique for automatic detection of the targets in forward-looking infrared (FLIR) imagery. Mathematical morphology is applied for the preliminary selection of possible regions of interest (ROI). An efficient clutter rejecter module based on probabilistic neural network is proposed, which is trained by using both target and background features to ensure excellent classification performance by moving the ROI in several directions with respect to the center of the detected target patch. Experimental results using real-life FLIR imagery confirm the excellent performance of the detector and the effectiveness of the proposed clutter rejecter module.

New two-stage correlation-based approach for target detection and tracking in forward-looking infrared imagery using filters based on extended maximum average correlation height and polynomial distance classifier correlation

A novel approach is proposed to recognize and track multiple identical and/or dissimilar targets in forward-looking infrared (FLIR) image sequences using a combination of an extended maximum average correlation height (EMACH) filter and polynomial distance classifier correlation filter (PDCCF). The EMACH filter and PDCCF are trained a priori using representative training images of targets with expected size and orientation variations. In the first step, the input scene is correlated with all EMACH filters (one for each desired or expected target class). Based on the regions with higher correlation peak values in the combined correlation output, a sufficient number of regions of interest (ROIs) are selected from the input scene. In the second step, a PDCCF is applied to these ROIs to identify target types and reject clutter and background. Moving-target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in a few frames and then reappearing in later frames. This method yields robust performance for challenging FLIR imagery in terms of accurate detection and classification as well as tracking of the targets.

Embedded-Zerotree-Wavelet-Based Data Denoising and Compression for Smart Grid

In this paper, an embedded zerotree wavelet transform (EZWT)-based technique is employed for the data denoising and compression in the smart grid (SG). The EZWT is a simple but effective data compression algorithm. This technique does not need any training, prestored tables or codebooks, and prior knowledge of the data source. In this paper, the analysis and subsequent compression and denoising properties of the EZWT are examined for power system signals in SG. The proposed approach is evaluated using phasor measurement units and power system data. Experimental results demonstrate that the presented method both compresses the signal and depresses the noise contained in the signal. Comparative compression and noise removal results are presented with the wavelet transform.

Study of Bidimensional Empirical Mode Decomposition Method for Various Radial Basis Function Surface Interpolators

Scattered data interpolation is an essential part of bidimensional empirical mode decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and lower envelopes, respectively. Because of the properties of radial basis function (RBF) interpolators, they are good candidates for use in BEMD. However, only one or two of the RBF interpolators have been utilized for BEMD so far. This paper employs seven RBF interpolators for BEMD, compares their performances, and finds out the useful ones for BEMD. The analysis is done using synthetic and real texture images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters of the BEMD process. The study is believed to work as a guideline in the area of BEMD based image analysis.

Distortion invariant road sign detection

This paper proposes an universal method to detect and recognize road signs of any countries with any color or any of the existing shapes (e.g. circular, rectangular, triangular, pentagonal and octagonal). The presented system is invariant to transformation (e.g. translation, rotation, scale and occlusion). There are three main stages in the proposed algorithm: 1) segmentation based on the color features to find the the region of interests (ROIs), 2) traffic sign detection by using two novel shape classification criteria, and 3) recognition of the road sign using distortion invariant fringe-adjusted joint transform correlation (FJTC) for matching the unknown signs with the known reference road signs stored in the database. Experimental results on real life images demonstrate that the proposed framework is invariant to translation, rotation, scale and partial occlusions.