Multi-scale Contour Research Articles

The Dynamic Energy-Momentum Tensor and the Logarithmic Singularity of a Generally Accelerating Edge Dislocation

Abstract The near-field logarithmic singularities in the field quantities associated with the acceleration of an arbitrarily moving edge dislocation are calculated based on a conservation law involving the dynamic energy-momentum tensor integrated over a domain enclosed by a multi-scale contour (an annulus of inner radius ϵ02 and outer radius ϵ0). The existence of the logarithmic singularities is obtained solely from the conservation law and the leading 1/r terms in the near fields of the stress and the velocity (which are those of the steady-state motion with velocity the instantaneous velocity in the accelerating motion). From the equations of motion and the symmetry in the second partial derivatives of the displacements for y≠0 we obtain that all six logarithmic terms of the near-field expansions are independent of the angle in the polar coordinates. All logarithmic terms in the near-field expansion of the strains and velocity in an arbitrarily moving edge dislocation (subsonically) are evaluated.

Quantitative Relations between Morphostructural Similarity Degree and Map Scale Change in Contour Clusters in Multi-Scale Map Space

This paper aims to propose a new approach to calculate the quantitative relations between morphostructural similarity degree and map scale change in multi-scale contour clusters for automatic contour generalization. Terrain lines were extracted by pre-processing of unclosed contour lines, and an indirect quantitative expression method of morphostructural similarity relation was proposed based on terrain line hierarchical trees. Thirteen groups of multi-scale contour clusters with different drainage areas of loess geomorphy were employed to explore the changing regularity of morphostructural similarity indices with map scale. Finally, the quantitative relations between morphostructural similarity degree and map scale change were calculated using 52 groups of points. The results show that power function is the best fit among the candidate functions, and the quantitative relations between the morphostructural similarity degree and map scale change can be expressed using the same power function, which facilitates the automation of contour generalization.

Method for continuous multiscale representation of terrain contours using T‐spline technique

AbstractContours are the main graphical element for characterizing three‐dimensional terrain on a topographic map. Continuous multiscale representation of contours is crucial to meet users' needs for detailed information between fixed‐scale contours. Wherein shape of the contour should ideally progressively change as the scale continuously changes. However, the lack of a bridge between scale and shape deformation degrees is an obstacle in obtaining continuous multiscale contours. A novel method for continuous multiscale representation of contour based on T‐spline (CMRTS) is proposed. Multiscale contours are generated through interpolation of the T‐spline and constraint of the relationship between scale and shape similarity. First, a T‐spline surface is established using two fixed‐scale contours. Second, the T‐spline surface intersects a set of horizontal planes with gradual heights to generate intersecting lines. Finally, the quantitative relationship between the scale and shape similarity is determined to align each intersecting line with the corresponding scale and achieve continuous multiscale contours. A case study in Chongqing, China implements and evaluates the proposed method. Results indicate that shape changes of contours generated by CMRTS are more natural and progressive than those by two typical methods. Our method shows high potential and practicability for mapping continuous multiscale contours for real‐world terrain applications.

Quantitative Relations between Topological Similarity Degree and Map Scale Change of Contour Clusters in Multi-Scale Map Spaces

Quantitative relations between topological similarity degree and map scale change of multi-scale contour clusters are vital to the automation of map generalization. However, no method has been proposed to calculate the relations. This paper aims at filling the gap by proposing a new approach. It firstly constructed a directed contour tree by pre-processing of unclosed contours, and then developed a quantitative expression of topological relations of contour cluster based on directed contour tree. After this, it employed 108 groups of multi-scale contour clusters with different geomorphological types to explore the changing regularity of topological indices with map scale. Last, it used 416 points to calculate the quantitative relations between topological similarity degree and map scale change by curve fitting method. The results show that the quantitative expression of multi-scale topological indexes is closely related to the contour interval change, and power function is the best fit among the candidate functions.

No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction

Accurately assessing image quality is a challenging task, especially without a reference image. Currently, most of the no-reference image quality assessment methods still require reference images in the training stage, but reference images are usually not available in real scenes. In this paper, we proposed a model named MSIQA inspired by biological vision and a convolution neural network (CNN), which does not require reference images in the training and testing phases. The model contains two modules, a multi-scale contour prediction network that simulates the contour response of the human optic nerve to images at different distances, and a central attention peripheral inhibition module inspired by the receptive field mechanism of retinal ganglion cells. There are two steps in the training stage. In the first step, the multi-scale contour prediction network learns to predict the contour features of images in different scales, and in the second step, the model combines the central attention peripheral inhibition module to learn to predict the quality score of the image. In the experiments, our method has achieved excellent performance. The Pearson linear correlation coefficient of the MSIQA model test on the LIVE database reached 0.988.

Tensor pooling-driven instance segmentation framework for baggage threat recognition

Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects, we propose to derive proposals according to the hierarchy of the regions defined by the contours. The proposed framework is rigorously validated on three public datasets, dubbed GDXray, SIXray, and OPIXray, where it outperforms the state-of-the-art methods by achieving the mean average precision score of 0.9779, 0.9614, and 0.8396, respectively. Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.

A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set

Multi-scale spatial representation has been widely used in geographic information and online mapping systems. Terrain contour, which provides a reference for understanding and monitoring the Earth’s surface, is an important data category. For the multi-scale representation of contour lines, simplification is a fundamental step in providing different levels of detail for linear features. However, achieving a global continuous multi-scale simplification of contours remains a challenge. Therefore, based on the concept of level set, a novel contour simplification method labeled the continuous changing surface model (CCSM) was proposed in this paper. The CCSM was built by using a non-uniform rational B-spline constrained with characteristics and was then intersected with a set of horizontal planes with progressive height values. The generated intersection lines are considered continuous multi-scale simplified contours. Experiments were conducted on a 1:50,000 real contour dataset to verify the effectiveness of CCSM. Results showed that the changes in the shape of the simplified contours generated by CCSM are more natural and progressive than those generated by two other significant simplification methods. CCSM can also effectively balance local and global structures and has potential applications in obtaining a continuous multi-scale representation of terrain contours.

MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

Neural dynamics of spreading attentional labels in mental contour tracing

Behavioral and neural data suggest that visual attention spreads along contour segments to bind them into a unified object representation. Such attentional labeling segregates the target contour from distractors in a process known as mental contour tracing. A recurrent competitive map is developed to simulate the dynamics of mental contour tracing. In the model, local excitation opposes global inhibition and enables enhanced activity to propagate on the path offered by the contour. The extent of local excitatory interactions is modulated by the output of the multi-scale contour detection network, which constrains the speed of activity spreading in a scale-dependent manner. Furthermore, an L-junction detection network enables tracing to switch direction at the L-junctions, but not at the X- or T-junctions, thereby preventing spillover to a distractor contour. Computer simulations reveal that the model exhibits a monotonic increase in tracing time as a function of the distance to be traced. Also, the speed of tracing increases with decreasing proximity to the distractor contour and with the reduced curvature of the contours. The proposed model demonstrated how an elaborated version of the winner-takes-all network can implement a complex cognitive operation such as contour tracing.

Multi-scale contour detection model based on fixational eye movement mechanism

Physiological evidence has shown that classical receptive field (CRF) responses in the primary visual cortex (V1) can be suppressed by its surrounding region, called the non-classical receptive field (nCRF). Currently, the contour detection model based on the physiological characteristics of the V1 region is mainly used to suppress texture and highlight contour information through the inhibition of nCRF features. However, the effect of eye movement on inhibition is not considered in the inhibition calculation of such models. Inspired by the fixational eye movement (FEyeM) mechanism, we propose a multi-scale contour detection model based on fixational eye movement (MsFem) and the surrounding suppression mechanism. A bank of filters was proposed to simulate the influence of FEyeMs on nCRF, and multi-scale cues were utilized to improve the fine and coarse contour extraction and texture inhibition. The experiments showed that MsFem outperformed some biologically motivated ones in retaining the small-scale target contour information and suppressing the large-scale background textures.

3D Flow Entropy Contour Fitting Segmentation Algorithm Based on Multi-Scale Transform Contour Constraint

Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.

Multiscale Contour Steered Region Integral and Its Application for Cultivar Classification

In this paper, a multiscale contour steered region integral (MCSRI) method is proposed to classify highly similar shapes with flexible interior connection architectures. A component distance map (CDM) is developed to robustly characterize the flexible interior connection structure, shape of the exterior contour, and their inter-relationship in a shape image. A novel multiscale region transform (MReT) is proposed to perform region integral over different contour-steered strips at all possible scales to effectively integrate patch features, and thus enables a better description of the shape image in a coarse-to-fine manner. It is applied to solve a challenging problem of classifying cultivars from leaf images, which is a new attempt in both biology and computer vision research communities. A soybean cultivar leaf vein database (SoyCultivarVein), which is the first cultivar leaf vein database, is created and presented for performance evaluation. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art methods in similar shape classification and the possibility of cultivar recognition via leaf pattern analysis, which may lead to a new research interest towards fine-level shape analysis on cultivar classification.

Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks.

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments for low-level applications on BSDS, PASCAL Context, PASCAL Segmentation, and NYUD to evaluate boundary detection performance, showing that COB provides state-of-the-art contours and region hierarchies in all datasets. We also evaluate COB on high-level tasks when coupled with multiple pipelines for object proposals, semantic contours, semantic segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that COB also improves the results for all tasks.

A Multiscale Particle Filter and Winding Number Constrained for Contour Detection

paper compares the basic contour detection algorithms. A contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscaleedgelet structure naturally embeds semi-local information and is the basic element of the recursive Bayesian modeling. The underlying model is estimated using a sequential Monte Carlo approach, and the soft contour detection map is retrieved from the approximated trajectory distribution. The winding number constrained contour detection (WNCCD) is an energy minimization framework based on winding number constraints. In this framework, both region cues, such as color/texture homogeneity, and contour cues, such as local contrast and continuity, are represented in a joint objective function, which has both region and contour labels. This technique is based on the topological concept of winding number. Using a fast method for winding number computation, a small number of linear constraints are derived to ensure label consistency. Experiments conducted on the Berkeley Segmentation data sets show that the Multi Scale Particle Filter Contour Detector method performs a comparable result with the winding number constrained contour detection method. Keywordsfiltering, sequential Monte Carlo methods, statistical model, multiscale contour detection, BSDS

Multi-scale contour flexibility shape signature for Fourier descriptor

Shape signature and Fourier descriptor are common techniques for shape description and they are widely used in pattern recognition and computer vision applications. In this paper, a novel shape signature is proposed, namely, multi-scale contour flexibility shape signature. After the discrete Fourier transform is performed on the multi-scale contour flexibility shape signature, the Fourier descriptor will be obtained. As a contour line function, contour flexibility based Fourier descriptor not only describes the whole deformation characteristics of the two dimensional shape profiles, but also reflects the local deformation characteristics of the contour sampling points. Thus, the proposed method incorporates the global and local features of the shape. Multi-scale technique could solve the problem of elastic parameter selection skillfully and describe the shape features from coarse to fine. In addition, contour flexibility is also easy to be calculated. Experiments conducted in the MPEG-7 shape database show that the best retrieval results are achieved by the multi-scale contour flexibility based Fourier descriptor compared with other representative shape signatures based Fourier descriptor.

A Novel Medical Image Registration Based on Contour Features and MultiScale Information

Medical image registration is the important technique in the clinical medicine field. A novel hierarchical registration method of the medical images based on multiscale information and contour line is proposed in the paper. At First, contour lines of the couple images are extracted based on the edge features obtained by Canny operator, and contour lines of the couple images are resample in order to reduce the calculation cost in the registration process. Secondly, the Principal Axes method is used to accomplish the rough registration based on the resampled contour lines. Thirdly, multiscale image serials obtained by down-sample transform are used to accomplish the couple images fine registration. Experiment results show that the method not only can achieve more accurate registration results, but also can reduce the computational time greatly. The accurate registration results also can be achieved in the noisy environment.

A multi-scale method for automatically extracting the dominant features of cervical vertebrae in CT images

Localization of the dominant points of cervical spines in medical images is important for improving the medical automation in clinical head and neck applications. In order to automatically identify the dominant points of cervical vertebrae in neck CT images with precision, we propose a method based on multi-scale contour analysis to analyzing the deformable shape of spines. To extract the spine contour, we introduce a method to automatically generate the initial contour of the spine shape, and the distance field for level set active contour iterations can also be deduced. In the shape analysis stage, we at first coarsely segment the extracted contour with zero-crossing points of the curvature based on the analysis with curvature scale space, and the spine shape is modeled with the analysis of curvature scale space. Then, each segmented curve is analyzed geometrically based on the turning angle property at different scales, and the local extreme points are extracted and verified as the dominant feature points. The vertices of the shape contour are approximately derived with the analysis at coarse scale, and then adjusted precisely at fine scale. Consequently, the results of experiment show that we approach a success rate of 93.4% and accuracy of 0.37mm by comparing with the manual results.

Shape classification via image-based multiscale description

We introduce a new multiscale Fourier-based object description in 2-D space using a low-pass Gaussian filter (LPGF) and a high-pass Gaussian filter (HPGF), separately. Using the LPGF at different scales (standard deviation) represents the inner and central part of an object more than the boundary. On the other hand using the HPGF at different scales represents the boundary and exterior parts of an object more than the central part. Our algorithms are also organized to achieve size, translation and rotation invariance. Evaluation indicates that representing the boundary and exterior parts more than the central part using the HPGF performs better than the LPGF-based multiscale representation, and in comparison to Zernike moments and elliptic Fourier descriptors with respect to increasing noise. Multiscale description using HPGF in 2-D also outperforms wavelet transform-based multiscale contour Fourier descriptors and performs similar to the perimeter descriptors without any noise.

Multiscale principal contour direction for varying lighting face recognition

Proposed is a new type of image feature for varying lighting face recognition, namely, multiscale principal contour direction (MPCD). It can capture ‘true’ neighbourhood contour direction information and is robust to different illumination conditions. In addition, MPCD is constructed for extracting illumination invariant by applying the nonsubsampled contourlet transform (NSCT) to multiscale contour analysis. To evaluate its performance, a series of experiments are performed on two well-known illumination databases (i.e. Yale B and CMU PIE), showing the efficacy of the method.

Multiscale contour matching with basis functions

A multiscale contour matching method using basis functions is presented. In this scheme, curves are parametrized by the curve length s and s′, and contours are matched by finding a matching function s′=f(s). A set of basis functions, which are monotonic and continuous in the interval [0,1], are designed. The sum of these weighted basis functions approximates f(s). The weight of each function can be computed through optimization of the objective function. Multiscale schemes can make matching insensitive to initial value. Experimental validation is carried out with a sequence of human active contours and the result shows this method is effective.