Edge-like Features Research Articles

On the Use of Ocean Surface Doppler Velocity for Oceanic Front Extraction From Chinese Gaofen-3 SAR Data

Oceanic front extraction from synthetic aperture radar (SAR) images is of great significance to the study of marine ecosystems. At present, the methods for oceanic front extraction are usually based on SAR intensity images, which treat oceanic fronts as edge-like features in the SAR images. However, affected by radar parameters and sea state, sometimes, oceanic front signatures may not be clearly visible in the SAR intensity images. Therefore, existing methods are limited. In order to solve this problem, a method combining intensity and Doppler information for oceanic front extraction is proposed. Using Chinese Gaofen-3 single-look complex (SLC) data, three cases where oceanic front signatures are clearly visible, partially visible, and extremely weak in the SAR intensity images are investigated, which demonstrate how the Doppler velocity gradient across a front can be leveraged to enhance the extraction of oceanic fronts from SAR data. The results show that the Doppler data, as supplementary information, not only can complement the oceanic fronts extracted from SAR intensity images, but also can be used as a reference for the oceanic fronts extracted from SAR intensity images.

Kinematic edge detection using finite impulse response filters

Various physics observables can be determined from the localization of distinct edge-like features in distributions of measurement values. In this paper, we address the observation that neither differentiating nor fitting the measured distributions is robust against significant fluctuations in the experimental data. We propose the application of Finite Impulse Response (FIR) filters instead. To demonstrate the method, we consider the typical case in particle physics in which the precise localization of kinematic edges, often blurred by e.g. background contributions and detector effects, is crucial for determining particle masses. We show that even for binned data, typical for high energy physics, the optimal FIR filter kernel can be approximated by the first derivative of a Gaussian (FDOG). We study two highly complementary supersymmetric scenarios that, if realized in nature, could be observed at a future high-energy e+e− collider such as the International Linear Collider (ILC) or the Compact Linear Collider (CLIC). The first scenario considers the production of ẽ±−pairs while the second focuses on the χ̃1± and χ̃20-pair production. We demonstrate that the FIR filter method for edge extraction is superior to previously employed methods in terms of robustness and precision.

Analysis of edge and corner points using parabolic dictionaries

Last decade saw the creation of a number of directional representation dictionaries that desire to address the weaknesses of the classical wavelet transform that arise due to its limited capacity for the analysis of edge-like features of two-dimensional signals. Salient features of these dictionaries are directional selectivity and anisotropic treatment of the axes, achieved through the parabolic scaling law. In this paper we will examine the adequacy of such dictionaries for the analysis of edge- and corner-like features of 2D regions through a comprehensive framework for directional parabolic dictionaries, called the continuous parabolic molecules. This work builds on a family of earlier studies and aims to give a broader perspective through the level of generality.

Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

For classification purposes, the proposed guideline is a choose Mean and, where a class patch is likely to contain edge-like features within it, Con. Cor is an alternative for Mean in these situati...

Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

ABSTRACTTexture measurements quantitatively describe relationships of DN values of neighbouring pixels. The output is a continuous measure of spatial information that may be used for further processing. Spatial relationships are not necessarily correlated with spectral data for a given class, and including a measure of them improves classification accuracy. This research develops a guideline for choosing among the Haralick (Grey Level Co-occurrence Matrix [GLCM]) set of texture measures. These guidelines are derived using a variety of land covers and spatial scales (window sizes).Principal component analysis (PCA) of eight GLCM measures was performed for three Landsat TM and ETM+ images: a mid-latitude agricultural and natural vegetation scene, a glacier–rock–sea ice scene, and a desert scene with dunes and structurally complex rocks. PCA was performed separately for neighbourhoods consisting of squares with 25, 13, and 5 pixels on a side to demonstrate robustness to different spatial scales. PCA loadings show that contrast (Con), dissimilarity, entropy (Ent), and GLCM variance are most commonly associated with visual edges of land-cover patches; homogeneity, GLCM mean, GLCM correlation (GLCM Cor), and angular second moment are associated with patch interiors. Edge-highlighting textures account for most dataset variance but fail to differentiate among classes. Eigenchannels highlighting patch interior characteristics rely on GLCM mean and to some extent GLCM Cor. These two textures do contribute to distinguishing individual class signatures for classification purposes. Ent does not appear consistently in edge or interior groupings. Ent is interpreted as important to the textures of particular classes, but which classes is not generalized from one scene to another. Con is effective for outlining patch edges and may serve for object formation in geographic object-based image analysis (GEOBIA).For classification purposes, the proposed guideline is a choose Mean and, where a class patch is likely to contain edge-like features within it, Con. Cor is an alternative for Mean in these situations, Dis may similarly be used in place of Con. For more detailed texture study, add Ent. This guideline does not constitute a complete texture analysis but may allow confident use of GLCM texture to enhance the efficiency of Landsat-based classification.

Adaptive and robust statistical methods for processing near-field scanning microwave microscopy images.

Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave |S11| images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical.

Sensitivity of edge illumination X-ray phase-contrast imaging

Recently, we developed a theoretical model that can predict the signal-to-noise ratio for edge-like features in phase-contrast images. This model was then applied for the estimation of the sensitivity of three different X-ray phase-contrast techniques: propagation-based imaging, analyser-based imaging and grating interferometry. We show here how the same formalism can be used also in the case of the edge illumination (EI) technique, providing results that are consistent with those of a recently developed method for the estimation of noise in the retrieved refraction image. The new model is then applied to calculate, in the case of a given synchrotron radiation set-up, the optimum positions of the pre-sample aperture and detector edge to maximize the sensitivity. Finally, an example of the extremely high angular resolution achievable with the EI technique is presented.

Towards understanding the influence of data-richness on interpretational confidence in image interpretation

Geological interpretations of aeromagnetic and gravity images are highly subjective but are rarely accompanied by a quantitative confidence assessment, which is a key limitation on the usefulness of the results. This paper outlines a method with which the relative level of data richness can be assessed quantitatively, leading to an improved understanding of spatial variations in interpretational confidence. Simple rules were used to quantify the likely influence of several major sources of uncertainty. These were: 1) the level of geological constraint, using the local abundance of outcropping rock and the quality of geological mapping; 2) the interpretability of the data, considering the strength of edge-like features and the degree of directionality of these features, a proxy for structural complexity; 3) data collection and processing errors, including gridding errors, derived from the statistical error returned during kriging, and the influence of anisotropic line data collection on the detection of gradients. From these individual sources of uncertainty an overall data richness map was generated through a weighted summation of these grids. Weightings were assigned so as to best match the result to the interpreter's perception of interpretational confidence. This method produced a map of data richness, which reflects the opportunity that the data provided to the interpreter to make a correct interpretation. An example from central Australia indicated that the data influences were preserved over a moderate range of weighting factors, and that strong bias was required to override these. In addition to providing a confidence assessment, this method also provides a way to test the potential benefits of additional data collection.

Semiautomated quantification of the influence of data richness on confidence in the geologic interpretation of aeromagnetic maps

Geologic interpretations of aeromagnetic maps are highly subjective but are rarely accompanied by a quantitative confidence assessment, which is a key limitation on the usefulness of the results. Here, we outline a method with which the relative level of data richness can be assessed quantitatively, leading to an improved understanding of spatial variations in interpretational confidence. Simple rules were used to quantify the likely influence of several major sources of uncertainty. These were: (1) the level of geologic constraint, using the local abundance of outcropping rock and the quality of geologic mapping; (2) the interpretability of the aeromagnetic data, considering the strength of edge-like features and the degree of directionality of these features, a proxy for structural complexity; (3) data collection and processing errors, including gridding errors, derived from the statistical error returned during kriging, and the influence of anisotropic line data collection on the detection of gradients. From these individual sources of uncertainty, an overall data richness map was generated through a weighted summation of these grids. Weightings were assigned so as to best match the result to the interpreter’s perception of interpretational confidence. This method produced a map of data richness, which reflects the opportunity that the data provided to the interpreter to make a correct interpretation. An example from central Australia indicated that the data influences were preserved over a moderate range of weighting factors, and that strong bias was required to override these. In addition to providing a confidence assessment, this method also provides a way to test the potential benefits of additional data collection.

Rendering edge enhancement tactile phenomenon by friction variation in dynamic touch

Variable friction tactile displays have been recently used to render virtual textures and gratings. Neural basis of perceptual mechanism of detection of edge-like features resulting in discrimination of virtual gratings during active touching these tactile actuators is studied using a finite-element biomechanical model of human fingertip. The predicted neural response of the mechanoreceptors, i.e. the computed strain energy density at the location of selected mechanoreceptors as a measure of neural discharge rate of the corresponding receptors, to local reduction of friction between fingerpad and surface are shown to exhibit a similar shape as the edge enhancement phenomenon, particularly in a sudden burst at the boundary of variable friction regions. This phenomenon is supposed to account for the illusion of virtual edges rendered through the modification of contact forces. The presence of this sudden burst under varied model parameters was investigated. It was shown that while the appearance of this phenomenon in simulation results was invariant to model parameters, associated alteration of the edge enhancement ratio might be considered for the purpose of the tuning of the variable friction tactile display.

Sharing Visual Features for Multiclass and Multiview Object Detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection.

Motion stereo for mobile robots

A method allowing a mobile robot to measure accurately its distance from external objects is introduced. It only requires a single uncalibrated camera and a dynamic vision system. The method was evaluated in an optical laboratory and also in outdoor-experiments. Even in outdoor-experiments with a vehicle moving at a speed of 30 km/h, errors of less than 1% of the true distance were achieved in real time. The accuracy achievable with the method depends on the nature of the image features used as a basis for the measurement. Laboratory experiments have been performed to investigate quantitatively the characteristics of the feature detectors under conditions of reduced lighting and, consequentially, reduced SNR, and with image features not matching the feature detectors perfectly. The best results can be achieved by using edge-like features. Point-like features do have certain desirable characteristics, but point detectors are more sensitive to noise than edge detectors. Hence, the accuracy achieved with corner detectors was less than the one achieved with edge detectors. Nevertheless, even with corner detectors the resulting error was less than 1%, despite the fact that the image of the target object did not contain good corners.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Linear image features in stereopsis

Most proposed algorithms for solving the stereo correspondence problem have used matching based in some way on linear image features. Here the geometric effect of a change in viewing position on the output of a linear filter is modeled. A simple local computation is shown to provide confidence intervals for the difference between filter outputs at corresponding points. Examples of the use of the confidence interval are provided. For some widely used filters, the confidence intervals are tightest at isolated vertical step edges, lending support to the idea of using edge-like features in stereopsis. However, the same conclusion does not apply to image regions with more complicated variation on the scale of the filter support.

Application of Combined Median- Averaging Filters to Scintigraphic Image Processing

Statistical and deterministic properties of median filters are briefly discussed and their inherent advantages as a prospective tool in scintigraphic data processing are pointed out. The ability of median filters of suppressing impulse noise while the edge-like features of an image are preserved, is demonstrated on phantom data. The residual high-frequency noise remaining after median filtering can be subsequently reduced by standard smoothing procedures. A simple algorithm, made up of the superposition of a median and an averaging filter, is presented and shown to be a promising candidate in the quest for fast and easy-to-implement processing routine.

Detection of Global Edges in Textured Images

Recently geologists have been recognizing the importance of line or edge-like features called lineaments that are commonly detected in Landsat terrain reconnaissance. Lineaments are piecewise discontinuous global lines or edges of low curvature, and they generally have widely varying widths or edge characteristics that make them difficult to detect.