We present an approach for polarimetric Synthetic Aperture Radar (SAR) image region boundary detection based on the use of B-Spline active contours and a new model for polarimetric SAR data: the GHP distribution. In order to detect the boundary of a region, initial B-Spline curves are specified, either automatically or…
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48 results for “Contour Detection”
Defines surfaces with specific contour patterns.
problem Characterizing surfaces with concentric or parallel -contours.
method Presented examples and characterization given.
result Characterization of surfaces with concentric or parallel -contours.
Algorithm finds function contours using multiple approximations.
problem Locating contours of expensive-to-evaluate functions.
method Uses multiple biased and noisy approximations to locate contours efficiently by maximizing entropy reduction.
result Maximizes reduction of contour entropy per unit cost.
Dynamic steerable blocks improve deep networks by learning filter invariances.
problem Pixel-based filters ignore image properties, leading to suboptimal performance.
method Developed frame-based ResNets and Densenets, which are steerable under predefined transformations.
result Dynamic steerable blocks outperform other approaches on contour detection datasets.
New method uses anisotropic mean curvature flow for contour recognition.
problem Contour recognition in images.
method Coupling anisotropic mean curvature flow with external charges for curve motion.
result Stable numerical approximation for contour recognition.
FineFool attacks deep models by focusing on object contours, improving attack performance.
problem Adversarial attacks on deep learning models, especially those that focus on perturbation size and success rate.
method FineFool uses attention to focus on object contours, producing more efficient and imperceptible perturbations.
result FineFool achieves better attack performance compared to state-of-the-art attacks, including higher success rate and smaller perturbations.
Projection of crosscapmath.DG
Study bifurcation diagrams of crosscap contours.
problem Understanding bifurcations in crosscap images.
method Determined precise bifurcation diagrams of crosscap contours.
result Three types of equivalences are crucial for bifurcations.
A contour integral method recently proposed by Weideman [IMA J. Numer. Anal., to appear] for integrating semi-discrete advection-diffusion PDEs, is extended for application to some of the important equations of mathematical finance. Using estimates for the numerical range of the spatial operator, optimal contour parame…
Describing shapes by suitable measures in object segmentation, as proposed in [24], allows to combine the advantages of the representations as parametrized contours and indicator functions. The pseudo-Riemannian structure of optimal transport can be used to model shapes in ways similar as with contours, while the Kanto…
Develops deep neural networks for efficient salient object detection.
problem Efficiency and accuracy in detecting salient objects of various scales and semantic information.
method Hybrid contrast-oriented deep neural networks combining fully convolutional and segment-level spatial pooling streams, with an attentional module for fusion.
result Significantly outperforms state-of-the-art methods on six benchmark datasets.
Investigate the local geometry of smooth surfaces in 4-space via contact with 2-planes and apparent contours.
problem Local geometry of smooth surfaces in 4-space
method Contact with 2-planes and apparent contour
result Prove connections between singularities of parallel projections, orthogonal projections, and height functions.
In this paper we consider planar sections and visual contours of co-dimension one affine immersions. The main theorem says that the third order Taylor expansion of the difference between the visual contour and planar section functions is exactly the cubic form. We also consider parameterizations on two dimensional affi…
Metrics stabilize persistent homology in data analysis.
problem Stabilizing invariants for characterizing connectivity structures in data.
method Using contour functions to define metrics for rank invariants.
result Optimal contours provide robust descriptors of spatial patterns.
Paper proposes a new Wasserstein distance for mixtures of radially contoured distributions.
problem Generalization of Wasserstein distance to non-elliptically contoured distributions.
method Relaxed formulation for mixtures of radially contoured distributions without marginal consistency.
result The new distance yields more stable error and better color distribution in image transfer tasks.
Improved F0 contour estimation from noisy speech with higher frequency resolution.
problem Estimating the fundamental frequency contour of speech from noisy audio.
method Regression model using recurrent deep neural networks trained on supervised learning.
result Improves F0 contour estimation by more than 25% at SNRs between -10 dB and +10 dB.
We develop mask iterative hard thresholding algorithms (mask IHT and mask DORE) for sparse image reconstruction of objects with known contour. The measurements follow a noisy underdetermined linear model common in the compressive sampling literature. Assuming that the contour of the object that we wish to reconstruct i…
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case studycs.CV
This study automates blood cell classification using computer vision.
problem Automated detection and classification of white blood cells.
method Color-based segmentation, morphological processing, feature extraction, PCA, Unsupervised and Supervised learning algorithms, Deep Convolutional Neural Networks.
result Identification of robust algorithms for automated blood cell classification.
U-Net network trained for skin lesion segmentation in ISIC challenge.
problem Skin lesion segmentation in medical imaging.
method Trained a U-Net network with adjusted loss function and post-processing.
result Improved accuracy in segmenting skin lesions.
New method uses contours of segmented images for X-ray classification.
problem Classifying X-ray images of segmented radiography.
method Develops a new approach for image analysis of multivariate planar curves, addressing alignment issues.
result Demonstrates the robustness and appeal of the proposed method through detection of cardiomegaly and numerical experiments.
A new method for analyzing shapes using FDA techniques.
problem Statistical shape analysis of deformed contours.
method Functional Data Analysis (FDA) with basis expansion and principal component analysis.
result Successfully identifies deformation parameters and captures contour distributions.
Properly immersed curves in arbitrary surfaces via apparent contours on spines of traversing flowsmath.GT
The paper studies curves in surfaces using flow-spines and apparent contours.
problem Understanding curves in arbitrary surfaces using flow-spines and apparent contours.
method By considering generic curves and their apparent contours relative to a traversing flow, the paper reconstructs curves and allows them to vary up to homotopy.
result A finite set of local moves on decorated graphs allows for the reconstruction and variation of curves within a fixed generic flow.
IrisNet tracks tongue contours in ultrasound videos using deep learning.
problem Automatic and real-time tracking of tongue contours in ultrasound videos.
method Convolutional neural network mimicking peripheral vision.
result Outstanding generalization on various ultrasound tongue datasets.
Improves spline quality and accuracy in computational microscopy.
problem Detecting slender, overlapping structures in microscopy images.
method Differentiable rendering approach for spline refinement.
result Achieves high reliability and sub-pixel accuracy.
The contour map of estimation error of Expected Shortfall (ES) is constructed. It allows one to quantitatively determine the sample size (the length of the time series) required by the optimization under ES of large institutional portfolios for a given size of the portfolio, at a given confidence level and a given esti…
ICSGLD improves efficiency in posterior sampling for big data.
problem Efficient posterior sampling for large datasets.
method Embarrassingly parallel multiple-chain CSGLD with efficient interactions.
result ICSGLD is more efficient than a single-chain CSGLD.
Paper introduces kernel methods for detecting anomalous changes in remote sensing imagery.
problem Detecting anomalous changes in remote sensing imagery.
method Nonlinear extension of Gaussian and elliptically contoured distribution algorithms using reproducing kernel Hilbert space.
result Improved detection accuracy and reduced false-alarm rates compared to linear formulations.
The paper finds surfaces closest to being flat that span a given contour.
problem Finding surfaces in that are as flat as possible while spanning a given contour.
method The approach involves minimizing the total Gaussian curvature squared and solving a system of PDEs.
result The optimal surface is shown to be controlled by a biharmonic equation with specific boundary conditions.
Completeness of the eigenfunctions of a quantum mechanical system is crucial for its probability interpretation. By using the method of contour integral we give properly normalized eigenfunctions for both discrete and continuum spectrum of the Morse potential, and explicitly prove the completeness relation. As an appli…
Pipeline detects pulmonary embolisms from sparse CT images.
problem Manual diagnosis of pulmonary embolisms is laborious and prone to errors.
method Two-stage pipeline using AI, sparse annotations, and robust models.
result Achieved AUC scores of 0.94 on validation and 0.85 on test sets for severe PEs.
Proposes a new network to improve nuclei segmentation in histopathology images.
problem Challenges in separating overlapped nuclei in histopathology images.
method Introduces a bending loss regularized network to minimize contour points with large curvatures.
result Outperforms six state-of-the-art approaches on five quantitative metrics.
Elliptically-Contoured Tensor-variate Distributions with Application to Improved Image Learningstat.ME
Improved image learning using elliptically contoured tensor-variate distributions.
problem Inadequate statistical analysis for tensor-valued data, especially with heavier or lighter tails.
method Developed a family of elliptically contoured tensor-variate distributions and derived their properties and procedures for estimation.
result Tensor-variate classification rules and tensor-on-tensor regression better predict and characterize data than TVN-based methods.
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Ed…
Optimizes learning Hilbert-Schmidt operators between Sobolev spaces.
problem Statistical limits of learning mappings between infinite-dimensional function spaces.
method Minimax optimal regularization and multilevel training.
result Multilevel kernel operator learning achieves optimal learning rate.
New methods improve accuracy in predicting complex systems.
problem Improving accuracy in predicting complex physical systems from simulators.
method Proposes two new methods of design approaches that sequentially select input settings.
result Demonstrates effectiveness of the proposed methods through numerical examples.
The contour maps of the error of historical resp. parametric estimates for large random portfolios optimized under the risk measure Expected Shortfall (ES) are constructed. Similar maps for the sensitivity of the portfolio weights to small changes in the returns as well as the VaR of the ES-optimized portfolio are also…
Study on Gaussian random fields' singularities on manifolds.
problem Understanding singularities of Gaussian random fields on manifolds.
method Computed expected values of singularities under various conditions.
result Explicit formulae for singularities under different constraints.
Parallel Mappercs.CV
Parallel Mapper algorithm for efficient topological data analysis.
problem Efficient parallel processing of Mapper for topological data analysis.
method Provable correct parallel algorithm for Mapper execution on multiple processors.
result Demonstrates the efficiency of parallel Mapper compared to sequential implementations.
The paper tightens bounds on distances between Reeb graphs.
problem Certifying quasi-universality of distances between Reeb graphs.
method Establishes tight bi-Lipschitz bounds for various distances.
result Proves strict universality of the functional contortion distance for contour trees and coincides with interleaving distance for merge trees.
Study of curves and surfaces from single-direction projections.
problem Obtaining complete shape information from a single view.
method Theoretical study of differential geometric information from multiple orthogonal projections.
result Formulae for recovering certain information on curves or surfaces from their projections.
Study improves tongue contour recognition in ultrasound images using transfer learning.
problem Difficulty in recognizing tongue gestures due to low-contrast and noisy ultrasound images.
method Transfer learning with U-net and fine-tuning on different ultrasound datasets.
result Fine-tuned U-net models perform well on images from different ultrasound devices.
It is proposed a new code for contours of plane images. This code was applied for optical character recognition of printed and handwritten characters. One can apply it to recognition of any visual images.
Deep learning improves LV segmentation and volume estimation from cardiac MRI.
problem Accurate LV segmentation and volume estimation for cardiac MRI.
method Image preprocessing, U-Net architecture, postprocessing, end-to-end analytics pipeline.
result Improved accuracy in LV segmentation and volume estimation.
Wilson loops in supersymmetric Yang-Mills theory correspond at strong coupling to extremal surfaces in . We study a class of extremal surfaces known as special Legendrian submanifolds. The "hemisphere" corresponding to the circular Wilson loop is an example of a special Legendrian submanifold, and w…
Combines geometric and topological info for complex object boundaries.
problem Boundary estimation for images with complex topologies.
method Topological clustering for initialization of Bayesian active contour model.
result Smart initialization reduces user dependence and improves accuracy.
We consider a complex Plateau problem for strongly pseudoconvex contours in non Kähler manifolds. A positive solution in the case of manifolds carrying a pluriclosed Hermitian metric forms is given. For the general case we propose a conjecture.
Wavelet features improve image clustering and segmentation accuracy.
problem Noise and lack of spatial context in pixel intensity-based methods.
method Modified K-means, Fuzzy c-means, and ACWE algorithms incorporating Wavelet features.
result Wavelet-based algorithms converge to different segmentation results based on frequency information.
New method improves combinatorial optimization by overcoming inefficient sampling.
problem Wandering in contours: sampling similar solutions for long periods.
method Reheated Gradient-based Discrete Sampling with a novel mechanism.
result Superior performance across various combinatorial optimization problems.
A new method encodes data structure from neighborhood-similarity graphs without bias.
problem Distortion and bias in high-dimensional data analysis.
method Directly encodes data point neighborhood similarities as a sparse graph, avoiding assumptions and iterative schemes.
result Efficacy demonstrated in natural and synthetic data applications.