False Features Research Articles

Fast Algorithm for Multimodal Image Registration with DTCWT

Objective: To perform image registration with sub band features computed using dual tree complex wavelets and reducing the computation time in registration. Methods/Statistical Analysis: The input images are preprocessed with Gaussian filters; features are extracted from dual tree sub bands. The dominant features are retained with feature selection process to minimize complexity in registration. Translation of images based on similar features is carried out using singular value decomposition. A refinement phase further registers the images considering similarities in features. The proposed algorithm is modeled in MATLAB, registration processes are modeled from fundamental equations. Test images are considered for validation. The test images are geometrically transformed with knows sets of translation, scale and rotational parameters. The two images are registered and the corresponding transformation parameters are compared. Critical points in the images are considered as reference points for validation. Findings: The proposed algorithm performs registration of all images with less than 10% error. The PSNR computed for all sets of data base is greater than 32dB. With dual tree sub bands, the unique features with 150, 450 and 750 orientations are captured thus the features are twice more compared with DWT features. The feature selection algorithm considers the orientations in all three directions thus improving registration process. SVD algorithm optimizes the complexity in transformation process and refinement phase. Elimination of false features and use of appropriate sub band features has reduced the registration time by 76% thus meeting the requirement for real time applications in micro air vehicles. Application/Improvements: The improvement in computation time and registration accuracy of this algorithm is suitable for MAVs moving with speed of 20-30kmph. Multilevel decomposition will further improve the registration process, hardware accelerators can reduce computation time to few milliseconds.

Improving software product line using an ontological approach

Software product line (SPL) is an emergent strategy for generating software products. The variability and commonality of SPL is illustrated by feature models (FMs). The quality of software products relies on the correctness of SPL. The overall benefits of software product line engineering (SPLE) are reduced by various kinds of defects such as dead features and false optional features in an FM. These defects can be inherited in the software products built from a defective product line model (PLM). In this paper, the problem of enhancing the quality of software products derived from SPLE is handled. An ontological based approach is proposed following first-order logic (FOL) rules to identify defects namely dead features and false optional features. The classification of cases for these defects in FMs that represent variability of SPL is defined. The presented approach has been explained with the help of an FM derived from the standard case in product line (PL) community. The initial empirical evaluation of the proposed approach analyses 35 FMs with different sizes. The results obtained exhibit that the proposed approach is accurate, effective, scalable up to 200 features and therefore improves SPL.

Enhanced RGB-D Mapping Method for Detailed 3D Indoor and Outdoor Modeling

RGB-D sensors (sensors with RGB camera and Depth camera) are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks including limited measurement ranges (e.g., within 3 m) and errors in depth measurement increase with distance from the sensor with respect to 3D dense mapping. In this paper, we present a novel approach to geometrically integrate the depth scene and RGB scene to enlarge the measurement distance of RGB-D sensors and enrich the details of model generated from depth images. First, precise calibration for RGB-D Sensors is introduced. In addition to the calibration of internal and external parameters for both, IR camera and RGB camera, the relative pose between RGB camera and IR camera is also calibrated. Second, to ensure poses accuracy of RGB images, a refined false features matches rejection method is introduced by combining the depth information and initial camera poses between frames of the RGB-D sensor. Then, a global optimization model is used to improve the accuracy of the camera pose, decreasing the inconsistencies between the depth frames in advance. In order to eliminate the geometric inconsistencies between RGB scene and depth scene, the scale ambiguity problem encountered during the pose estimation with RGB image sequences can be resolved by integrating the depth and visual information and a robust rigid-transformation recovery method is developed to register RGB scene to depth scene. The benefit of the proposed joint optimization method is firstly evaluated with the publicly available benchmark datasets collected with Kinect. Then, the proposed method is examined by tests with two sets of datasets collected in both outside and inside environments. The experimental results demonstrate the feasibility and robustness of the proposed method.

Verification of SPL Feature Model by using Bayesian Network

Feature Tree represents all the features along with their relationship of a Software Product Line. Any defect in feature model can diminish the benefits of product line approach. Hence, the analysis of feature model plays a key role towards the success of any Software Product Line. This paper presents various analysis rules for cardinality-based feature model of both dead and false optional features. These rules are then verified by using Bayesian Network Based inference mechanism. Such verification not only confirms the analysis rules of the feature trees but also ensures the applicability of probabilistic information into the feature trees.

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data.

False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful.

False positive or negative results of shear-wave elastography in differentiating benign from malignant breast masses: analysis of clinical and ultrasonographic characteristics.

Shear-wave elastography (SWE) has the potential to improve diagnostic performance of conventional ultrasound (US) in differentiating benign from malignant breast masses. To investigate false positive or negative results of SWE in differentiating benign from malignant breast masses and to analyze clinical and imaging characteristics of the masses with false SWE findings. From May to October 2013, 166 breast lesions of 164 consecutive women (mean age, 45.3 ± 10.1 years) who had been scheduled for biopsy were included. Conventional US and SWE were performed in all women before biopsy. Clinical, ultrasonographic morphologic features and SWE parameters (pattern classification and standard deviation [SD]) were recorded and compared with the histopathology results. Patient and lesion factors in the "true" and "false" groups were compared. Of the 166 masses, 118 (71.1%) were benign and 48 (28.9%) were malignant. False SWE features were more frequently observed in benign masses. False positive rates of benign masses and false negative rates of malignancy were 53% and 8.2%, respectively, using SWE pattern analysis and were 22.4% and 10.3%, respectively, using SD values. A lesion boundary of the masses on US (P = 0.039) and younger patient age (P = 0.047) were significantly associated with false SWE findings. These clinical and ultrasonographic features need to be carefully evaluated in performance and interpretation of SWE examinations.

Fidelity imaging for atomic force microscopy

Atomic force microscopy is widely employed for imaging material at the nanoscale. However, real-time measures on image reliability are lacking in contemporary atomic force microscopy literature. In this article, we present a real-time technique that provides an image of fidelity for a high bandwidth dynamic mode imaging scheme. The fidelity images define channels that allow the user to have additional authority over the choice of decision threshold that facilitates where the emphasis is desired, on discovering most true features on the sample with the possible detection of high number of false features, or emphasizing minimizing instances of false detections. Simulation and experimental results demonstrate the effectiveness of fidelity imaging.

識別性能の予測に基づく選択的特徴量を用いたばら積み部品の認識

This paper introduces a reliable 3D position and pose recognition method for complicated scenes including randomly stacked objects. The proposed method achieves a reliable recognition by using a small number of effective features selected by evaluating performance of discrimination. The performance of discrimination is evaluated by two abilities. One is the stability of features, the other is separability of false features. For evaluating performance of discrimination, features are extracted from synthesized 3D scenes that are generated by using physics-based simulator and the 3D Computer Graphics (3D-CG) techniques. An object model's features that have high performance of discrimination in the feature space are selected by using synthesized 3D scenes' features, and selected features are used for the matching process. Experimental results using real scenes including randomly stacked objects show that the proposed method's recognition success rate is from 81.7% to 93.9% higher than that of the Vector Pair Matching (VPM) method.

Using surface electromyography (SEMG) to classify low back pain based on lifting capacity evaluation with principal component analysis neural network method.

Low back pain (LBP) is a leading cause of disability. The population with low back pain is continuously growing in the recent years. This study tries to distinguish LBP patients with healthy subjects by using the objective surface electromyography (SEMG) as a quantitative score for clinical evaluations. There are 26 healthy and 26 low back pain subjects who involved in this research. They lifted different weights by static and dynamic lifting process. Multiple features are extracted from the raw SEMG data, including energy and frequency indexes. Moreover, false discovery rate (FDR) omitted the false positive features. Then, a principal component analysis neural network (PCANN) was used for classifications. The results showed the features with different loadings (including 30%, and 50% loading) on lifting which can be used for distinguishing healthy and back pain subjects. By using PCANN method, more than 80% accuracies are achieved when different lifting weights were applied. Moreover, it is correlated between some EMG features and clinical scales, on exertion, fatigue, and pain. This technology can be potentially used for the future researches as a computer-aid diagnosis tool of LBP evaluation.

δ18O in the tropical conifer Agathis robusta records ENSO-related precipitation variations.

Long-lived trees from tropical Australasia are a potential source of information about internal variability of the El Niño-Southern Oscillation (ENSO), because they occur in a region where precipitation variability is closely associated with ENSO activity. We measured tree-ring width and oxygen isotopic composition (O) of -cellulose from Agathis robusta (Queensland Kauri) samples collected in the Atherton Tablelands, Queensland, Australia. Standard ring-width chronologies yielded low internal consistency due to the frequent presence of false ring-like anatomical features. However, in a detailed examination of the most recent 15 years of growth (1995–2010), we found significant correlation between O and local precipitation, the latter associated with ENSO activity. The results are consistent with process-based forward modeling of the oxygen isotopic composition of -cellulose. The O record also enabled us to confirm the presence of a false growth ring in one of the three samples in the composite record, and to determine that it occurred as a consequence of anomalously low rainfall in the middle of the 2004/5 rainy season. The combination of incremental growth and isotopic measures may be a powerful approach to development of long-term (150+ year) ENSO reconstructions from the terrestrial tropics of Australasia.

Dynamically Removing False Features in Pyramidal Lucas-Kanade Registration

Pyramidal Lucas-Kanade (LK) optical flow is a real-time registration technique widely employed by a variety of cutting edge consumer applications. Traditionally, the LK algorithm is applied selectively to image feature points that have strong spatial variation, which include outliers in textured areas. To detect and discard the falsely selected features, previous methods generally assess the goodness of each feature after the flow computation is completed. Such a screening process incurs additional cost. This paper provides a handy (but not obvious) tool for the users of the LK algorithm to remove false features without degrading the algorithm's efficiency. We propose a confidence predictor, which evaluates the ill-posedness of an LK system directly from the underlying data, at a cost lower than solving the system. We then incorporate our confidence predictor into the course-to-fine LK registration to dynamically detect false features and terminate their flow computation at an early stage. This improves the registration accuracy by preventing the error propagation and maintains (or increases) the computation speed by saving the runtime on false features. Experimental results on state-of-the-art benchmarks validate that our method is more accurate and efficient than related works.

Research on Jamming Method of False Target Based on Micro-Motion Modulation

Micro-Doppler is a unique feature of radar target, and has been applied to target recognition of ISAR widely, but it can also destroy the quality of the target image at the same time. So a novel jamming method of false target base on Micro-Doppler modulation is presented in the paper. The phase of captured radar transmitting signal is been modulated, which can generate false Micro-Doppler features. The micro-Doppler imaging model of the rotating target is analyzed, and the jamming model based on Micro-Motion modulation is given. Finally, the simulation of jamming experiment is carried out. The results of simulation prove the method is corrective and effective.

Regularization Design and Control of Change Admission in Prior-Image-based Reconstruction.

Nearly all reconstruction methods are controlled through various parameter selections. Traditionally, such parameters are used to specify a particular noise and resolution trade-off in the reconstructed image volumes. The introduction of reconstruction methods that incorporate prior image information has demonstrated dramatic improvements in dose utilization and image quality, but has complicated the selection of reconstruction parameters including those associated with balancing information used from prior images with that of the measurement data. While a noise-resolution tradeoff still exists, other potentially detrimental effects are possible with poor prior image parameter values including the possible introduction of false features and the failure to incorporate sufficient prior information to gain any improvements. Traditional parameter selection methods such as heuristics based on similar imaging scenarios are subject to error and suboptimal solutions while exhaustive searches can involve a large number of time-consuming iterative reconstructions. We propose a novel approach that prospectively determines optimal prior image regularization strength to accurately admit specific anatomical changes without performing full iterative reconstructions. This approach leverages analytical approximations to the implicitly defined prior image-based reconstruction solution and predictive metrics used to estimate imaging performance. The proposed method is investigated in phantom experiments and the shift-variance and data-dependence of optimal prior strength is explored. Optimal regularization based on the predictive approach is shown to agree well with traditional exhaustive reconstruction searches, while yielding substantial reductions in computation time. This suggests great potential of the proposed methodology in allowing for prospective patient-, data-, and change-specific customization of prior-image penalty strength to ensure accurate reconstruction of specific anatomical changes.

An Ontological Rule-Based Approach for Analyzing Dead and False Optional Features in Feature Models

Feature models are a common way to represent variability requirements of software product lines by expressing the set of feature combinations that software products can have. Assuring quality of feature models is thus of paramount importance for assuring quality in software product line engineering. However, feature models can have several types of defects that disminish benefits of software product line engineering.Two of such defects are dead features and false optional features. Several state-of-the-art techniques identify these defects, but only few of them tackle the problem of identifying their causes. Besides, the explanations they provide are cumbersome and hard to understand by humans. In this paper, we propose an ontological rule-based approach to: (a) identify dead and false optional features; (b)identify certain causes of these defects; and (c) explain these causes in natural language helping modelers to correct found defects. We represent our approach with a feature model taken from literature. A preliminary empirical evaluation of our approach over 31 FMs shows that our proposal is effective, accurate and scalable to 150 features.

Two Measures for Enhancing Data Association Performance in SLAM

Data association is one of the key problems in the SLAM community. Several data association failures may cause the SLAM results to be divergent. Data association performance in SLAM is affected by both data association methods and sensor information. Two measures of handling sensor information are introduced herein to enhance data association performance in SLAM. For the first measure, truncating strategy of limited features, instead of all matched features, is used for observation update. These features are selected according to an information variable. This truncating strategy is used to lower the effect of false matched features. For the other measure, a special rejecting mechanism is designed to reject suspected observations. When the predicted robot pose is obviously different from the updated robot pose, all observed sensor information at this moment is discarded. The rejecting mechanism aims at eliminating accidental sensor information. Experimental results indicate that the introduced measures perform well in improving the stability of data association in SLAM. These measures are of extraordinary value for real SLAM applications.

Fractal mechanism for characterizing singularity of mode shape for damage detection

Damage is an ordinary physical phenomenon jeopardizing structural safety; damage detection is an ongoing interdisciplinary issue. Waveform fractal theory has provided a promising resource for detecting damage in plates while presenting a concomitant problem: susceptibility to false features of damage. This study proposes a fractal dimension method based on affine transformation to address this problem. Physical experiments using laser measurement demonstrate that this method can substantially eliminate false features of damage and accurately identify complex cracks in plates, providing a fundamental mechanism that brings the merits of waveform fractal theory into full play in structural damage detection applications.

Shear-wave elastography in the diagnosis of solid breast masses: what leads to false-negative or false-positive results?

To investigate the factors that have an effect on false-positive or false-negative shear-wave elastography (SWE) results in solid breast masses. From June to December 2012, 222 breast lesions of 199 consecutive women (mean age: 45.3 ± 10.1years; range, 21 to 88years) who had been scheduled for biopsy or surgical excision were included. Greyscale ultrasound and SWE were performed in all women before biopsy. Final ultrasound assessments and SWE parameters (pattern classification and maximum elasticity) were recorded and compared with histopathology results. Patient and lesion factors in the 'true' and 'false' groups were compared. Of the 222 masses, 175 (78.8%) were benign, and 47 (21.2%) were malignant. False-positive rates of benign masses were significantly higher than false-negative rates of malignancy in SWE patterns, 36.6% to 6.4% (P < 0.001). Among both benign and malignant masses, factors showing significance among false SWE features were lesion size, breast thickness and lesion depth (all P < 0.05). All 47 malignant breast masses had SWE images of good quality. False SWE features were more significantly seen in benign masses. Lesion size, breast thickness and lesion depth have significance in producing false results, and this needs consideration in SWE image acquisition. • Shear-wave elastography (SWE) is widely used during breast imaging • At SWE, false-positive rates were significantly higher than false-negative rates • Larger size, breast thickness, depth and fair quality influences false-positive SWE features • Smaller size, larger breast thickness and depth influences false-negative SWE features.

Increasing Sensitivity in Determining Chemical Shifts in One Dimensional Lorentzian NMR Spectra

An algorithm is presented for one-dimensional NMR systems that employs nonlinear, non-Fourier methods to convert noisy time-dependent free induction decay (FID) data to a denoised frequency spectrum that gives reliable chemical shifts and coupling constants when the spectrum is Lorentzian. It is formulated in a way that increases frequency sensitivity and resolution and, for nuclei of low natural abundance, potentially avoids enrichment totally or in part. The algorithm should also be of use in analytical chemistry where enrichment is not possible. In effect, the useful limit of detection is significantly lowered. The algorithm uses new "phasing" and "feature stability upon accumulation" methods to reliably separate signal from noise at low signal-to-noise ratios where the Fourier spectrum requires many more transients to be definitive as to what is signal and what is noise. The long-standing problem of "false features" that plagued many prior attempts to employ nonlinear methods is thereby resolved for Lorentzian spectra. Examples are reported, and the limitations of the algorithm are discussed.

Potential problems in collection and data processing of luminescence signals

Luminescence studies are central to a very wide range of disciplines, both as a primary experiment and in a minor role for additional discrimination between samples. Unfortunately when luminescence studies are not the central objective and expertise the data collection, instrumental corrections and data analysis are not always being totally, or correctly, employed. There are often historical reasons for this but with modern equipment one can readily make the requisite compensations. The problems are outlined with emphasis on spectral and polarisation response of spectrometers and detectors. Typical data processing errors are noted with demonstrations of their consequent effects on the signals. These include the fact that the peak in the wavelength presentation can significantly differ from the true energy centre of a Gaussian emission band. There can be failure to totally compensate for the spectral sensitivity of the detection system, as well as the incorrect use of band de-convolution on the wavelength representation, and energy plots where only the wavelength axis has been corrected; these mistakes all distort the true spectra. Not only are these analyses physically incorrect, but they are misleading, and introduce false features. This brief review indicates why such processing errors can generate spectral differences cited in the luminescence literature that are from measurement, rather than differences between source materials.

A novel approach to minimize false discovery rate in genome-wide data analysis

BackgroundHigh-throughput technologies, such as DNA microarray, have significantly advanced biological and biomedical research by enabling researchers to carry out genome-wide screens. One critical task in analyzing genome-wide datasets is to control the false discovery rate (FDR) so that the proportion of false positive features among those called significant is restrained. Recently a number of FDR control methods have been proposed and widely practiced, such as the Benjamini-Hochberg approach, the Storey approach and Significant Analysis of Microarrays (SAM).MethodsThis paper presents a straight-forward yet powerful FDR control method termed miFDR, which aims to minimize FDR when calling a fixed number of significant features. We theoretically proved that the strategy used by miFDR is able to find the optimal number of significant features when the desired FDR is fixed.ResultsWe compared miFDR with the BH approach, the Storey approach and SAM on both simulated datasets and public DNA microarray datasets. The results demonstrated that miFDR outperforms others by identifying more significant features under the same FDR cut-offs. Literature search showed that many genes called only by miFDR are indeed relevant to the underlying biology of interest.ConclusionsFDR has been widely applied to analyzing high-throughput datasets allowed for rapid discoveries. Under the same FDR threshold, miFDR is capable to identify more significant features than its competitors at a compatible level of complexity. Therefore, it can potentially generate great impacts on biological and biomedical research.AvailabilityIf interested, please contact the authors for getting miFDR.