Bidimensional Empirical Mode Decomposition Research Articles

Wall heat flux in a supersonic shock wave/turbulent boundary layer interaction

The characteristics of wall heat flux (WHF) beneath a supersonic turbulent boundary layer interacting with an impinging shock wave with a 33.2° angle at Mach 2.25 are analyzed using direct numerical simulation. It is found that the QP85 scaling, defined as the ratio of the mean WHF and wall pressure, changes across the interaction. The probability density function of the WHF fluctuations normalized by the local root-mean-squared value is similar to that of wall shear stress. Comparing the WHF and wall pressure spectra shows that the low-frequency shock unsteadiness exhibits little influence on the spectrum. The space–time correlation of the fluctuating WHF reveals that both the streamwise correlation length scale and the convection velocity experience a sharp decrease in the separation region and subsequent recovery in the downstream region. Moreover, the mean WHF in an incident shock interaction is decomposed for the first time. An analysis of the velocity and temperature fluctuations based on bidimensional empirical mode decomposition is performed to evaluate the contribution of turbulent structures with specific spanwise length scales to the mean WHF generation. The decomposed results indicate that the contribution associated with the large-scale structures in the outer region is greatly amplified by the shock interaction and has the leading role in the generation downstream of the interaction.

Segmentation and classification of brain tumors from MRI images based on adaptive mechanisms and ELDP feature descriptor

Generally, soft tissue information of the brain is analyzed using Magnetic Resonance Imaging (MRI). An unusual growth of cells (tumor) in or around the brain may affect its overall functionality. Among brain tumors, gliomas that appear frequently in adults pose various challenges to radiologists. If they are not recognized properly in the initial phases, patients may face severe health issues including death. So, early and precise diagnosis of gliomas is significant for increasing the survival rate of patients. Recently, several computer-aided diagnosis (CAD) approaches have been developed for the detection and classification of gliomas. However, it is a challenging issue due to the noise as well as low sensitive boundary pixels appearing in gliomas. Considering this idea, we present a new approach based on adaptive methodologies and local texture descriptors. Here, primarily, we apply a median filter to remove noise from brain MRI images. Then, we employ adaptive decomposition strategies, namely bi-dimensional empirical mode decomposition (BEMD), and modified quasi-bivariate variational mode decomposition (MQBVMD) to attain meaningful sub-images. Later, we extract hidden texture details from the sub-images using an ELDP feature descriptor and then use a support vector machine (SVM) to distinguish brain MRI images as low-grade (LG) and high-grade (HG). Finally, we identify the infected region of gliomas with the help of adaptive K-means clustering and morphological operations. Using these sequences of operations, the proposed framework obtained 94.44% classification accuracy and 83.14% of dice similarity coefficient (DSC) value which is relatively high when compared to existing approaches.

BEMD Based Cross Bilateral Filtering Technique for Speckle Reduction in Ultrasound Images

In this paper, Bidimensional Empirical Mode Decomposition (BEMD) based Cross Bilateral Filter (CBF) technique for speckle reduction in ultrasound images has been proposed. The reference image is obtained by denoising the noisy image using pixel-wise Wiener filtering. Then, both the noisy image and the reference image are decomposed into a set of Intrinsic Mode Functions (IMFs) and the residue image using BEMD technique. CBF is applied between noisy image IMFs and the corresponding reference image IMFs. The image is reconstructed back with these modified IMFs and the residue. The proposed method exploits the edge information in the reference image for improving the quality of the denoised image. The performance of the proposed method has been tested for real ultrasound images and simulated images having noise of different variance. The experimental results show that the proposed algorithm performs better than other state-of-art methods in terms of Edge Keeping Index (EKI), Correlation Coefficient (CC), Figure of Merit (FOM), Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Signal to Noise Ratio (SNR) for synthetic images. The algorithm gives better performance for real ultrasound images in terms of Mean to Variance Ratio (MVR) and Equivalent Number of Looks (ENL).

Pansharpening Scheme Using Bi-dimensional Empirical Mode Decomposition and Neural Network

The pansharpening is a combination of multispectral (MS) and panchromatic (PAN) images that produce a high-spatial-spectral-resolution MS images. In multiresolution analysis–based pansharpening schemes, some spatial and spectral distortions are found. It can be reduced by adding spatial detail images of the PAN image into MS images. In the convolution neural network– (CNN) based method, the lowpass filter image extracted by the CNN model when MS and PAN images are directly applied into the input. The feature values are very high and reduce the conversion efficiency. In the proposed scheme, bi-dimensional empirical mode decomposition is used to extract the spatial detail information of the PAN image to reduce the feature values of the input. This extracted PAN image information is applied to the CNN to produce the non-linear changes in the image pixels and transformed into the perfect spatial detail image. It identifies the spatial and spectral detail quantity for the proposed scheme and it also varies with the different datasets automatically of the same satellite images. Simulation results in the context of qualitative and quantitative analysis demonstrate the effectiveness of proposed scheme applied on datasets collected by different satellites.

Despeckling of SAR Images Using BEMD-Based Adaptive Frost Filter

In image processing, removal of speckle noise from a satellite image is a challenging task for the researchers. There are various approaches for speckle noise reduction. Generally, the speckle noises are scattered, in satellite images, medical images and synthetic aperture radar (SAR) images. This paper introduces a bidimensional empirical mode decomposition (BEMD)-based adaptive filtering method for despeckling of SAR image. The noisy SAR image is decomposed into different bidimensional intrinsic mode function (BIMF) levels using BEMD and then filtering is performed on the first BIMF level, as it contains the high-frequency noise component. This adaptation process effectively filters out the noisy image component without destroying the original image component. A BEMD-based adaptive Frost filter is introduced in this paper for despeckling of SAR images. The despeckling performances of our proposed filtering method are further analyzed by visual evaluation and also using performance parameters comparatively. Our reconstructed images show better performance quantitatively and qualitatively compared to other filters.

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

A New Biomedical Image Denoising Method Using an Adaptive Multi-resolution Technique

In the world of digital image processing, image denoising plays a vital role, where the primary objective was to distinguish between a clean and a noisy image. However, it was not a simple task. As a consequence of everyone's understanding of the practical challenge, a variety of methods have been presented during the last few years. Of those, wavelet transformer-based approaches were the most common. But wavelet-based methods have their own limitations in image processing applications like shift sensitivity, poor directionality, and lack of phase information, and they also face difficulties in defining the threshold parameters. As a result, this study provides an image de-noising approach based on Bi-dimensional Empirical Mode Decomposition (BEMD). This project's main purpose is to disintegrate noisy images based on their frequency and construct a hybrid algorithm that uses existing de-noising techniques. This approach decomposes the noisy picture into numerous IMFs with residue, which were subsequently filtered independently based on their specific properties. To quantify the success of the proposed technique, a comprehensive analysis of the experimental results of the benchmark test images was conducted using several performance measurement matrices. The reconstructed image was found to be more accurate and pleasant to the eye, outperforming state-of-the-art denoising approaches in terms of PSNR, MSE, and SSIM.

Deep Multiple Instance Learning for Automatic Breast Cancer Assessment Using Digital Mammography

Breast cancer causes serious public health problems; it is the most common cancer among women worldwide. Screening and early detection of signs of breast cancer increase the chance of survival. Early diagnosis is a crucial task for radiologists and physicians. Therefore, many Computer-Aided Detection and Diagnosis (CADx) systems are being developed to ensure the survival of radiologists’ decisions. In this paper, we describe a framework to automate assessment of suspicious regions, detected in screening mammography, without having carried out additional examinations, especially unnecessary biopsies in the case where the suspect regions are benign tumors. The setup of the proposed framework is ordered as follows: Regions of interest (ROIs) have been segmented using a modified K-means algorithm; the Bi-dimensional empirical mode decomposition (BEMD) algorithm is applied to derive many layers (Bi-dimensional Intrinsic Mode Function BIMF) from ROIs. Then, textural features are extracted from the obtained ROIs. First, directly from segmented ROI, second from the ROI and its sub-layers (BIMFs + Residue). The features extracted in the second time have been grouped into a bag descriptive of the ROI under consideration. This bag is the input parameter of the classification algorithm based on the support vector machine (which has been confirmed to be beneficial for the classification of breast cancer). The average obtained sensitivity, specificity, accuracy and AUC rates, are, respectively 98.60%, 98.65%, 98.62%, and 98.23%. Generally, the experimental results in: INbreast, DDSM, and MIAS datasets demonstrate the robustness and the efficiency of the developed framework compared to previous works in the literature and have shown a significant advance.

Deep Neural Network Driven Automated Underwater Object Detection

Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.

BEMD-3DCNN-based method for COVID-19 detection

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the most effective medicine to help and protect patients. Importantly, a rapid diagnostic and detection system is a priority and should be developed to stop COVID-19 from spreading. Medical imaging techniques have been used for this purpose. Current research is focused on exploiting different backbones like VGG, ResNet, DenseNet, or combining them to detect COVID-19. By using these backbones many aspects cannot be analyzed like the spatial and contextual information in the images, although this information can be useful for more robust detection performance. In this paper, we used 3D representation of the data as input for the proposed 3DCNN-based deep learning model. The process includes using the Bi-dimensional Empirical Mode Decomposition (BEMD) technique to decompose the original image into IMFs, and then building a video of these IMF images. The formed video is used as input for the 3DCNN model to classify and detect the COVID-19 virus. The 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) module, and then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows learning from different feature maps. In our experiments, we used 6484 X-ray images, of which 1802 were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed technique achieved the desired results on the selected dataset. Additionally, the use of the 3DCNN model with contextual information processing exploited CAA networks to achieve better performance.

Color Zero-Watermarking Algorithm for Medical Images Based on BEMD-Schur Decomposition and Color Visual Cryptography

With the widespread use of medical images in telemedicine, personal information may be leaked. The traditional zero-watermarking technology has poor robustness under large-scale attacks. At the same time, most of the zero-watermarking information generated is a binary sequence with a single information structure. In order to effectively solve the poor robustness problem of traditional zero-watermarking under large-scale attacks, a color zero-watermarking algorithm for medical images based on bidimensional empirical mode decomposition (BEMD)-Schur decomposition and color visual cryptography is proposed. Firstly, the color carrier image and the color copyright logo are decomposed into R, G, and B three color components, respectively, and the feature value of each sub-block are extracted by wavelet transform, BEMD decomposition, block operation, and Schur decomposition. Then, the R, G, and B components of the copyright logo are scrambled by Arnold scramble and converted into binary watermark information. Finally, a color visual cryptography scheme is proposed to generate two color shared images based on the carrier characteristics and copyright information. One shared image is used to generate a color zero-watermark, and the other is used for copyright authentication phase. Experimental results show that this algorithm has strong robustness and stability in resisting large-scale noise attacks, filtering attacks, JPEG compression, cropping attacks, and translation attacks at different positions. Compared with similar zero-watermarking algorithms, the robust performance is improved by about 10%, and it can adapt to more complex network environments.

Method with high accuracy for phase retrieval in Fourier FPP based on the modified FCM and variational image decomposition

Phase retrieval with high accuracy remains one of the most challenging problems in Fourier fringe projection profilometry (FPP). The variational image decomposition TV-Hilbert- L 2 model has been proved to be a powerful tool for phase retrieval. In this paper, we first propose a modified fuzzy c-means (FCM) clustering algorithm to remove background from fringe projection patterns. In order to further improve the accuracy, we combine the modified FCM and the variational image decomposition TV-Hilbert- L 2 model and propose a new method to get the fringe part from the single frame fringe projection pattern. We evaluate the performance of this method via application to two simulated and one experimental fringe projection patterns and comparison with the Fourier transform, morphological operation-based bi-dimensional empirical mode decomposition, and variational image decomposition TV-Hilbert- L 2 model. The experiment results show that our method improves the accuracy of phase retrieval for TV-Hilbert- L 2 model and can achieve similar accuracy to the phase-shifting method.

Characteristics of reattached boundary layer in shock wave and turbulent boundary layer interaction

The reattached boundary layer in the interaction of an oblique shock wave with a flat-plate turbulent boundary layer at Mach number 2.25 is studied by means of Direct Numerical Simulation (DNS). The numerical results are carefully compared with available experimental and DNS data in terms of turbulence statistics, wall pressure and skin friction. The coherent vortex structures are significantly enhanced due to the shock interaction, and the reattached boundary layer is characterized by large-scale structures in the outer region. The space-time correlation of fluctuating wall shear stress and streamwise velocity fluctuation reveals that the structural inclination angle exhibits a gradual decrease during the recovery process. The scale interactions are analyzed by using a two-point amplitude modulation correlation. A possible mechanism is proposed to account for the strong amplitude modulation in the downstream region. Moreover, the mean skin-friction is decomposed to understand the physically informed contributions. Unlike the upstream Turbulent Boundary Layer (TBL), the contribution associated with the Turbulence Kinetic Energy (TKE) production is greatly amplified, while the spatial growth contribution induced by the pressure gradient largely inhibits skin-friction generation. Based on bidimensional empirical mode decomposition, the turbulence kinetic energy production contribution is further split into different terms with specific spanwise length scales.

An optimized pulse coupled neural network image de-noising method for a field-programmable gate array based polarization camera.

The quality of polarization images is easy to be affected by the noise in the image acquired by a polarization camera. Consequently, a de-noising method optimized with a Pulse Coupled Neural Network (PCNN) for polarization images is proposed for a Field-Programmable Gate Array (FPGA)-based polarization camera in this paper, in which the polarization image de-noising is implemented using an adaptive PCNN improved by Gray Wolf Optimization (GWO) and Bi-Dimensional Empirical Mode Decomposition (BEMD). Unlike other artificial neural networks, PCNN does not need to be trained, but the parameters of PCNN such as the exponential decay time constant, the synaptic junction strength factor, and the inherent voltage constant play a critical influence on its de-noising performance. GWO is able to start optimization by generating a set of random solutions as the first population and saves the optimized solutions of PCNN. In addition, BEMD can decompose a complicated image into different Bi-Dimensional Intrinsic Mode Functions with local stabilized characteristics according to the input source image, and the decomposition result is able to lower the complexity of heavy noise image analysis. Moreover, the circuit in the polarization camera is accomplished by FPGA so as to obtain the polarization image with higher quality synchronously. These two schemes are combined to attenuate different types of noises and improve the quality of the polarization image significantly. Compared with the state-of-the-art image de-noising algorithms, the noise in the polarization image is suppressed effectively by the proposed optimized image de-noising method according to the indices of peak signal-to-noise ratio, standard deviation, mutual information, structural similarity, and root mean square error.

Video block and FABEMD features for an effective and fast method of reporting near-duplicate and mirroring videos

Near-duplicate video content has taken the large storage space in the age of big data. Without respecting the copyright ethic, social media users mirror, resize, and/or hide certain online video content and re-upload it as new data. This research aims to avoid the complex and high-dimensional matching and present an efficient approach for detecting near-duplicate videos, this detection is based on feature extraction using visual, motion, and high-level features. Fast and adaptive bidimensional empirical mode decomposition is used to preserve the relevant data to the furthest extent possible during the low/high-frequency transition and vice-versa. In addition, for a generic model, the invariant moments are added to the aforementioned features in order to reinforce them against different video transformations such as rotating and scaling. Furthermore, the video frames are divided into blocks with a fixed number of features, this set of features is represented by a signature, where its mean and standard deviation represents a single video map allowing easy similarity computation. The F1-score and accuracy are used to evaluate the results of this study; the relevant results are ranked by Top_{1} for the best result, and the five top-ranked results are presented by Top_{5}. Further, our result of Top_{1} reached over 80% on F1-score, with a difference of ±4% from the Top_{5} results, and it is over 90% on Accuracy using different datasets, such as UCF11, UCF50, and HDMB51.

Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian Mixture Model approach

Monitoring objects and observing changes in the undersea environment are important for exploring resources and studying ecology. These conditions are challenging because the depth of the sea varies the transmission of light in the water medium intensely. Many object detection algorithms exist so far to observe the changes and for the detection of foreground objects. A new approach of underwater object detection is proposed to differentiate the foreground object from its background. It evolves with the idea of blob generation based on features extracted from underwater scenes using the Bi-dimensional Empirical Mode Decomposition (BEMD) Algorithm. The features such as weight factor, Hurst exponent, and texture strength decide the blob. These extracted features are then accustomed to the generic Gaussian Mixture Model (GMM) to provide a basis for an object monitoring system. The proposed model is primarily developed and tested for fish detection in underwater sequences. The proposed approach is compared with state-of-art object detection schemes and analysed both qualitatively and quantitatively.

Combination of ELM and BEMD for Target Recognition of SAR Images

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

Direct numerical simulation of supersonic turbulent expansion corner with shock impingement

This study conducted a direct numerical simulation of an incident shock wave impinging at an angle of 33.2° on a 12° supersonic turbulent expansion corner at Mach 2.25 to determine the influence of expansion on the physics of interaction. The point of nominal impingement was located at the tip of the expansion corner. This scenario was compared in detail with interactions in the case of a flat plate under the same inflow conditions. The expansion led to a significant reduction in the wall pressure and the size of the separation bubble. The pre-multiplied spectra of the fluctuating wall pressure indicated that the motion induced by the low-frequency shock was strongly inhibited by the presence of an expansion corner. The root mean-squared wall pressure declined rapidly downstream of the corner, and relaxed at a nearly constant level very close to its upstream value. The evolution of the reattached boundary layer was analyzed in terms of its velocity profile, map of anisotropic invariance, and turbulent kinetic energy, and a quick recovery process was clearly identified. Moreover, the analysis of decomposition of the mean skin friction revealed the dominant contribution of the turbulent kinetic energy regardless of the effect of expansion. Unlike in the case of flat-plate interaction, the component of negative spatial growth became positive owing to large positive streamwise heterogeneity, and could be neglected. Bidimensional empirical-mode decomposition was used to decompose the fluctuations into four modes with specific spanwise length scales, and the primary mechanism for the generation of skin friction was linked to small-scale structures in the near-wall region.

Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform.

Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD - we have considerably increased algorithm's application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data.

Development of data driven adaptive edge detectors for image processing

In most vision processing activities, the early stage involves identifying the features in an image that provide cues to structure and properties of the object in the scene. Most common features in an image or in a scene arel edges. Edges arel significant local changes in intensity within anlimage. Most important goal of edge detection is to produce a line drawing from anlimage representing the scene. The significant features of an image such as line, curve and corners can be extracted from edges. During the stage of discovering and exploring the information from an image of that scene, edge detection is the most important and early-stage activity and as such it is prominent active area in image processing. Most popular edge detection algorithm such as Robert, Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG), etc. are currently in use. This paper emphasis on an experimental study of limitations of conventional edge detectors and to devise a novel approach to resolve the conflicting issues i.e., limitations of these edge detectors in adaptive space utilizing novel methods such as Bi-dimensional Empirical Mode Decomposition (BEMD), Image Empirical Mode Decomposition (IEMD), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Multivariate Decomposition techniques. Further, to study the performance of these modified edge detectors on the images of complex scenes which are of societal and agricultural importance.