Adaptive Window Technique Research Articles

Adaptive window technique for lifetime-based temperature and velocity simultaneous measurement using thermographic particle tracking velocimetry with a single camera

Adaptive window technique for lifetime-based temperature and velocity simultaneous measurement using thermographic particle tracking velocimetry with a single camera

Decision-Theoretic Rough Sets based automated scheme for object and background classification in unevenly illuminated images

This paper addresses the problem of classification of object and background in unevenly illuminated images using Decision-Theoretic Rough Set (DTRS) framework. The proposed scheme employs adaptive windowing technique to partition the image into different windows. Thereafter, the proposed DTRS based method is applied on each window to find out the optimal threshold that is used for classification of the window. Determination of optimal threshold of a given window is dependent on the optimal granule size used for the window. The problem of determination of optimal granule size and optimal threshold is cast in optimization framework. The optimal threshold obtained for each window is used to classify the window and the classification of the entire image is the union of classifications over all the windows. Manual tuning of parameters is not required to determine the optimal threshold. The proposed scheme is tested on different images considered from Berkeley image database. The performance of the proposed scheme is compared with other granular and non-granular computing based schemes. Evaluation of different quantitative measures demonstrates the improved performance of the proposed schemes over others.

Real-Time Acceleration Estimation of a Slider Crank Mechanism with an Enhanced Adaptive Windowing Technique

In this paper, a real-time implementation of a second-order adaptive windowing technique for acceleration estimation is presented for a slider crank mechanism. It is shown that there are some critical parameters which should be carefully set to proper values for a satisfactory acceleration estimation. In this technique, the size of the window is automatically enlarged until the position errors will not be out of the error bands defined by the user. In order to show the effectiveness of the second-order adaptive windowing technique in real-time, a test setup composed of a slider-crank mechanism is used. In the performed tests, only the crank angle is measured via an incremental encoder having a 1024 pulse per revolution, and then, the slider acceleration is estimated by the help of some kinematic calculations using the outputs of the second-order adaptive windowing technique. The estimated acceleration results are directly compared with an acceleration sensor having a measurement range with ±6g with a nominal 0-200 Hz bandwidth attached on the slider. It is found that the root-mean-square value of the acceleration errors along the entire motion profile is at tolerable level so that this effective acceleration estimation technique could be applied to various on-line robotic applications as a soft sensor.

Automatic human ear detection approach using modified adaptive search window technique

The human ear biometric recognition plays an important role in the forensics specialty and has significant impact for biometrician scientists and researchers. Actually, many ear recognition researches showed promised results, but some issues such as manual detection process, efficiency and robustness aren’t attained a certain level of maturity. Therefore, the enhancement developing approaches still continuous to achieve limited successes. We propose an efficient, reliable and simple automatic human ear detection approach. This approach implement two stages: preprocessing and ear landmarks detection. We utilized the image contrast, Laplace filter and Gaussian blurring techniques to made enhancement on all images (increasing the contrast, reduce the noisy and smoothing processes). After that, we highlighted the ear edges by using the Sobel edge detector and determining the only white pixels of ear edges by applying the image substation method. The improvement focused on using the modified adaptive search window (ASW) to detect the ear region. Furthermore, our approach is tested on Indian Institute of Technology (IIT) Delhi standard ear biometric public dataset. Experimental results presented a well average detection rate 96% for 493 image samples from 125 persons and computational time almost ≈ 0.485 seconds which is evaluated with other previous works.

Application of a Krylov subspace method for an efficient solution of acoustic transfer functions

Solving acoustic radiation problems, arising from systems including fluid–structure interaction, is of interest in many engineering applications. Computing frequency response functions over a large frequency range is a concern in such applications. A method which solves the Helmholtz equation for multiple frequencies in one step is the matrix-Padé-via-Lanczos connection for unsymmetric systems, as presented by Wagner et al. [1]. The present work is based on Ref. [1] and presents a method for efficiently computing frequency responses over a frequency range for coupled structural-acoustic problems, where the structure and the acoustic near field are discretized with finite elements and an analytical Dirichlet-to-Neumann map approximates the far field. The method is based on a Krylov-subspace projection technique which derives a matrix-valued Padé approximation for a restricted area in the near field and the pressure field on a spherical boundary. On the spherical boundary, where the finite domain is truncated, the non-local modified Dirichlet-to-Neumann operator is applied as a low-rank update matrix. The present contribution extends this method and incorporates new techniques for a more stable model reduction through the Lanczos algorithm and a novel weighted adaptive windowing technique. Further, structural damping is incorporated, for computing the acoustic radiation of a harmonically excited plate. These computed results are compared with acoustic measurements in an anechoic chamber and verified with computational results obtained with a commercial code that uses the perfectly matched layer method.

Adaptive Windowing for ICI Mitigation in Vehicular Communications

The performance of orthogonal frequency division multiplexing systems in vehicular environments suffers from intercarrier interference (ICI) and the inherent non stationarity of the channel statistics. Receiver windowing constitutes an effective technique for enhancing the banded structure of the frequency-domain channel matrix, thus improving the effectiveness of a banded equalizer for ICI mitigation. However, its optimality has been verified only for stationary channels with perfectly known statistics. In non stationary channels, the second-order statistics have to be tracked and the optimal performance can be achieved at the expense of cubic complexity over the number of the subcarriers. To overcome this limitation, an adaptive windowing technique is proposed that is able to track directly an optimal receiver window in terms of average signal-to-interference noise ratio, requiring only linear complexity. Extensive simulation results verify both the ability of the proposed approach to track the time varying channel statistics and its increased robustness to channel estimation errors that are common in vehicular environments.

A Framework for Fast and Robust Visual Odometry

Knowledge of the ego-vehicle’s motion state is essential for assessing the collision risk in advanced driver assistance systems or autonomous driving. Vision-based methods for estimating the ego-motion of vehicle, i.e., visual odometry, face a number of challenges in uncontrolled realistic urban environments. Existing solutions fail to achieve a good tradeoff between high accuracy and low computational complexity. In this paper, a framework for ego-motion estimation that integrates runtime-efficient strategies with robust techniques at various core stages in visual odometry is proposed. First, a pruning method is employed to reduce the computational complexity of Kanade–Lucas–Tomasi (KLT) feature detection without compromising on the quality of the features. Next, three strategies, i.e., smooth motion constraint, adaptive integration window technique, and automatic tracking failure detection scheme, are introduced into the conventional KLT tracker to facilitate generation of feature correspondences in a robust and runtime efficient way. Finally, an early termination condition for the random sample consensus (RANSAC) algorithm is integrated with the Gauss–Newton optimization scheme to enable rapid convergence of the motion estimation process while achieving robustness. Experimental results based on the KITTI odometry data set show that the proposed technique outperforms the state-of-the-art visual odometry methods by producing more accurate ego-motion estimation in notably lesser amount of time.

Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes.

Free‐breathing cardiac MR with a fixed navigator efficiency using adaptive gating window size

A respiratory navigator with a fixed acceptance gating window is commonly used to reduce respiratory motion artifacts in cardiac MR. This approach prolongs the scan time and occasionally yields an incomplete dataset due to respiratory drifts. To address this issue, we propose an adaptive gating window approach in which the size and position of the gating window are changed adaptively during the acquisition based on the individual's breathing pattern. The adaptive gating window tracks the breathing pattern of the subject throughout the scan and adapts the size and position of the gating window such that the gating efficiency is always fixed at a constant value. To investigate the image quality and acquisition time, free breathing cardiac MRI, including both targeted coronary MRI and late gadolinium enhancement imaging, was performed in 67 subjects using the proposed navigator technique. Targeted coronary MRI was acquired from eleven healthy adult subjects using both the conventional and proposed adaptive gating window techniques. Fifty-six patients referred for cardiac MRI were also imaged using late gadolinium enhancement with the proposed adaptive gating window technique. Subjective and objective image assessments were used to evaluate the proposed method. The results demonstrate that the proposed technique allows free-breathing cardiac MRI in a relatively fixed time without compromising imaging quality due to respiratory motion artifacts.

Image-guided adaptive gating of lung cancer radiotherapy: a computer simulation study

The purpose of this study is to investigate the effect that image-guided adaptation of the gating window during treatment could have on the residual tumor motion, by simulating different gated radiotherapy techniques. There are three separate components of this simulation: (1) the ‘Hokkaido Data’, which are previously measured 3D data of lung tumor motion tracks and the corresponding 1D respiratory signals obtained during the entire ungated radiotherapy treatments of eight patients, (2) the respiratory gating protocol at our institution and the imaging performed under that protocol and (3) the actual simulation in which the Hokkaido Data are used to select tumor position information that could have been collected based on the imaging performed under our gating protocol. We simulated treatments with a fixed gating window and a gating window that is updated during treatment. The patient data were divided into different fractions, each with continuous acquisitions longer than 2 min. In accordance to the imaging performed under our gating protocol, we assume that we have tumor position information for the first 15 s of treatment, obtained from kV fluoroscopy, and for the rest of the fractions the tumor position is only available during the beam-on time from MV imaging. The gating window was set according to the information obtained from the first 15 s such that the residual motion was less than 3 mm. For the fixed gating window technique the gate remained the same for the entire treatment, while for the adaptive technique the range of the tumor motion during beam-on time was measured and used to adapt the gating window to keep the residual motion below 3 mm. The algorithm used to adapt the gating window is described. The residual tumor motion inside the gating window was reduced on average by 24% for the patients with regular breathing patterns and the difference was statistically significant (p-value = 0.01). The magnitude of the residual tumor motion depended on the regularity of the breathing pattern suggesting that image-guided adaptive gating should be combined with breath coaching. The adaptive gating window technique was able to track the exhale position of the breathing cycle quite successfully. Out of a total of 53 fractions the duty cycle was greater than 20% for 42 fractions for the fixed gating window technique and for 39 fractions for the adaptive gating window technique. The results of this study suggest that real-time updating of the gating window can result in reliably low residual tumor motion and therefore can facilitate safe margin reduction.

Adaptive spectral window sizes for extraction of diagnostic features from optical spectra

We present an approach to adaptively adjust the spectral window sizes for optical spectra feature extraction. Previous studies extracted features from spectral windows of a fixed width. In our algorithm, piecewise linear regression is used to adaptively adjust the window sizes to find the maximum window size with reasonable linear fit with the spectrum. This adaptive windowing technique ensures the signal linearity in defined windows; hence, the adaptive windowing technique retains more diagnostic information while using fewer windows. This method was tested on a data set of diffuse reflectance spectra of oral mucosa lesions. Eight features were extracted from each window. We performed classifications using linear discriminant analysis with cross-validation. Using windowing techniques results in better classification performance than not using windowing. The area under the receiver-operating-characteristics curve for windowing techniques was greater than a nonwindowing technique for both normal versus mild dysplasia (MD) plus severe high-grade dysplasia or carcinama (SD) (MD+SD) and benign versus MD+SD. Although adaptive and fixed-size windowing perform similarly, adaptive windowing utilizes significantly fewer windows than fixed-size windows (number of windows per spectrum: 8 versus 16). Because adaptive windows retain most diagnostic information while reducing the number of windows needed for feature extraction, our results suggest that it isolates unique diagnostic features in optical spectra.

Adaptive Window Zero-Crossing-Based Instantaneous Frequency Estimation

We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).

Investigation of smoothing techniques for positron emission tomography imaging

Recently, many techniques have been introduced to improve on the reconstruction and enhancement of biomedical images, in particular, positron emission tomography. Often these techniques either perform poorly in the presence of noise or are computationally expensive. Nonintensive computational techniques are presented that aim to reduce noise while preserving edges working in conjunction with the filtered convolution back-projection method of reconstruction. First we present an adaptive windowing technique that bases window size on the count value. Then a maximum likelihood technique is developed. Finally, a method of image enhancement based on directional operators and template matching attempts to diminish noise while preserving edges. These techniques are applied both in the image domain and in the sinogram domain and then evaluated using a similarity measure against the original Shepp-Logan phantom.

Localisation and separation of simultaneous voices with two microphones

A new way of localising two simultaneous voices, called the f-p method, is introduced. Test results of investigations carried out in the free-field (i.e. on a flat roof with no obstructions) are presented. The f-p method applies a neural, N-tuple, adaptive windowing technique to new binary image representation of the phase information derived from two microphones. It is closely related to a psychoacoustic theory of the human interaural time difference localisation mechanism. The microphones need not be fixed for the purposes of speech enhancement, and so the method has, in principle, the same spatial freedoms as the human system. A crucial factor in the success of the device is the presence of gaps in the spectrum of each source at different times and frequencies. The voices can, on the free-field case, be almost completely separated by using the localisation information. In the two-voice free-field case each voice is localised approximately seven times per second. The method involves no complex calculations and it is likely that it could be implemented in real-time, to allow tracking of moving sources, with a DSP board and a fast microprocessor. There is room for improvement in the values of the f-p parameters.

Adaptive estimators for filtering noisy images

A new estimation criterion called the minimum-error minimum correlation (MEMC) criterion is implemented in conjunction with an adaptive windowing technique. The image statistics are calculated within the window, the dimensions of which vary depending on the local statistics. The MEMC estimator produces sharper and hence visually more pleasing restorations, while the adaptive windows tend to isolate regions of the image that are locally stationary. Since most of the error after restoration is in the vicinity of edges, a postprocessing step of filtering along the edges is applied. These image restorations are compared to both fixed and adaptive minimum mean squared error estimators.