In the recent years the rapid growth of multimedia content makes the image retrieval a challenging research task. Content Based Image Retrieval (CBIR) is a technique which uses features of image to ...search user required image from large image dataset according to the user’s request in the form of query image. Effective feature representation and similarity measures are very crucial to the retrieval performance of CBIR. The key challenge has been attributed to the well known semantic gap issue. The machine learning has been actively investigated as possible solution to bridge the semantic gap. The recent success of deep learning inspires as a hope for bridging the semantic gap in CBIR. In this paper, we investigate deep learning approach used for CBIR tasks under varied settings from our empirical studies; we find some encouraging conclusions and insights for future research.
This paper proposes two exposure based recursive histogram equalization methods for image enhancement. The proposed methods are very effective for images acquired in low light condition like ...underwater sequences or night vision images. The first method is recursive exposure based sub-image histogram equalization (R-ESIHE) that recursively performs ESIHE 20 method till the exposure residue among successive iteration is less than a predefined threshold. The second method is named as recursively separated exposure based sub image histogram equalization (RS-ESIHE) that performs the separation of image histogram recursively; separate each new histogram further based on their respective exposure thresholds and equalize each sub histogram individually. The experimental results show that low exposure image enhancement problem was not addressed by earlier HE based methods, has been efficiently handled by these new methods. The performance evaluation of new methods is done in terms of image information content as well as visual quality inspection. The proposed methods outperforms earlier HE based contrast enhancement algorithms specifically for low light images.
Present-day demand and supply of connectivity necessitate the rapid production of Microwave (MW) filter units. The production of these filters is then followed by the step of utmost importance in the ...assembly line, viz., the ‘tuning of the filter’, as tuning is crucial to meeting the selectivity requirements of the band. Since the advent of filters, tuning has always been done manually, and hence it is considered a bottleneck by experts in the field. Thus, the need to automate the system is highly implied. The goal of the current work is to outline various MW filter tuning techniques that have been advocated by the community of researchers. The limitations of the said research works and their comparative analysis are also encapsulated in tabular form in the present paper. The paper ends with the implementation of an Expert-Based Hybrid Deep Learning Algorithm to fully automate the filter tuning process.
In hazy weather, the image in the scene suffers from noise which makes them less visible and to detect an object in hazy weather becomes a challenging task in computer vision. To have noise free ...image, many researchers have devised denoising techniques for enhancing visibility of images. Denoising is to remove the random variation from images and preserve the image features. As hazy images cause lots of visibility issues, this paper proposes removing haze and enhancing visibility of bad weather images with improved efficacy using an unsupervised neural network autoencoder that compress the data using machine learning and learns through Convolutional Neural Network (CNN). It has been observed that to have increased accuracy, the image classification and analysis is most effective using CNN. An end-to-end decoder training model is used to achieve the quality images. Further, various optimizers are compared to have better accuracy. The quality of images identified by estimation of performance such as RMSE and PSNR values are evaluated over single image and images from existing datasets and our own dataset. In the proposed method, RMSE value comes out to be 0.0373 for image from BSD500 dataset for specific image compared with other state of art approaches. The proposed model is intended in addition to other active, or progressive methods and the suggested method exceeds. The performance quality of images is explored applying measurable metrics. The images are taken from the datasets O-Haze, I-Haze, BSDS500, RESIDE, FRIDA and some from google.
•Exposure based Sub Image HE proved to be very effective for contrast enhancement.•The histogram clipping is combined with HE to provide control on over enhancement.•This method proved to be very ...effective for enhancing under exposed images.•The entropy measures of the ESIHE method outperform other HE based methods.
This paper presents a novel Exposure based Sub-Image Histogram Equalization (ESIHE) method for contrast enhancement for low exposure gray scale image. Exposure thresholds are computed to divide the original image into sub-images of different intensity levels. The histogram is also clipped using a threshold value as an average number of gray level occurrences to control enhancement rate. The individual histogram of sub images is equalized independently and finally all sub images are integrated into one complete image for analysis. The simulation results show that ESIHE outperforms other conventional Histogram Equalization (HE) methods in terms of image visual quality, entropy preservation and better contrast enhancement.
This paper presents a new method for the detection of voltage disturbances caused by various reasons in households and industries. The aim of the paper is to improve the power quality disturbance ...methods and implement it in real-time with a very high accuracy and use existing mitigation techniques after classification of the signal. The method involves a novel idea to use the quaternions for the purpose of signal tracking. The envelope formed by Hilbert envelope detection method is used as an input for the quaternions. The quaternions produce a spike whenever there is a deviation from the pure sine wave giving very accurate results in a very short period of time. Combination of SVM and DSmT classifier is used for recognizing and classification of different signals such as sags, swells, transients and other power quality disturbances, hence can be used to monitor the power quality.
Background
Preeclampsia, a condition very frequently linked to maternal and fetal deaths worldwide, remains a pressing concern due to delays in recognition and response. Effective screening tests for ...early detection of high-risk cases and appropriate preventive measures are currently lacking. Well planned prenatal care, timely detection, monitoring, and appropriate management are vital to prevent preeclampsia-related fatalities.
Methods
This prospective study evaluated the use of shear wave elastography (SWE) in identifying placental structural issues caused by preeclampsia in high-risk pregnancies. A total of 143 high-risk pregnant women with singleton pregnancies and an anterior placental position were included in the study.
Results
Women with preeclampsia exhibited significantly elevated SWE values in both center (27.98 ± 16.12 vs. 4.57 ± 6.57 kPa) and peripheral areas of the placenta (29.14 ± 16.12 vs. 4.80 ± 7.70 kPa) when compared to non-preeclampsia women (
p
= 0.000). Cutoff values of 8.70 kPa and 8.15 kPa at the Center and edge of the placenta respectively, accurately predicted preeclampsia in second-trimester pregnancies, with 84.62% sensitivity and 94% specificity. However no significant difference was observed between elastography values obtained from the center and edge of the placenta.
Conclusions
In conclusion, shear wave elastography can help diagnose preeclampsia early by assessing placental stiffness.
In this work, a new fractionally delayed biorthogonal wavelet is designed for recognition of voltage sag causes. This work addresses the classification of voltage sag causes into three categories, ...i.e., fault events (namely line-to-ground fault, line-to-line fault, double-line-to-ground fault and symmetrical fault), induction motor starting and transformer energization. Fractionally delayed biorthogonal Coiflet wavelet of order 2 (named as Coiflet fraclet) is designed using Lagrange interpolation and employed for the multiresolution analysis of voltage sag signals. To achieve higher classification accuracy, event information is derived from both time-frequency domain and frequency domain. Frequency information is obtained by applying multiple signal classification (MUSIC) algorithm on voltage sag signals. Both the time-frequency domain and frequency domain features are concatenated to implement the feature level fusion for strengthening the feature set. The feature set obtained after feature level fusion is used for training the recursive reduced kernel based extreme learning machine which addresses the problem of highly complex structure due to huge dataset of large dimensions. The classifier has shown robustness to the presence of noise also and classified the voltage sag causes with high accuracy.
Particle filters (PFs) are sequential Monte Carlo methods that use particle representation of state-space model to implement the recursive Bayesian filter for non-linear and non-Gaussian systems. ...Owing to this property, PFs have been extensively used for object tracking in recent years. Although PFs provide a robust object tracking framework, they suffer from shortcomings. Particle degeneracy and particle impoverishment brought by the resampling step result in abysmal construction of posterior probability density function (PDF) of the state. To overcome these problems, this work amalgamates two characteristics of population-based heuristic optimisation algorithms: exploration and exploitation with PF implementing dynamic resampling method. The aim of optimisation is to distribute particles in high-likelihood area according to the cognitive effect and improve quality of particles, while the objective of dynamic resampling is to maintain diversity in the particle set. This work uses very efficient spider monkey optimisation to achieve this. Furthermore, to test the efficiency of the proposed algorithm, experiments were carried out on one-dimensional state estimation problem, bearing only tracking problem, standard videos and synthesised videos. Metrics obtained show that the proposed algorithm outplays simple PF, particle swarm optimisation based PF, and cuckoo search based PF, and effectively handles different challenges inherent in object tracking.
