Image recoloring using geodesic distance based color harmonization

In this paper, we present a computationally simple yet effective image recoloring method based on color harmonization. Our method permits the user to obtain recolored results interactively by rotating a harmonious template after completing color harmonization. Two main improvements are made in this paper. Firstly, we give a new strategy for finding the most harmonious scheme, in terms of finding the template which best matches the hue distribution of the input image. Secondly, in order to achieve spatially coherent harmonization, geodesic distances are used to move hues lying outside the harmonious sectors to inside them. Experiments show that our approach can produce higher-quality visually pleasing recolored images than existing methods. Moreover, our method is simple and easy to implement, and has good runtime performance.

Segmentation of Haustral Folds and Polyps on Haustral Folds in CT Colonography Using Complementary Geodesic Distance Transformation

Computer-aided detection(CAD) for CT colonography refers to a scheme that automatically detects polyps in CT images of colon. Current CAD schemes already have a relatively high sensitivity and a low false positive rate. However, misdiagnosis and missed diagnosis are still common to happen, mainly due to the existence of haustral folds(HFs). An innovative idea of segmenting semilunar HFs from the smooth colonic wall and then using different methods to detect polyps on HFs and those on the smooth colonic wall is proposed in this paper to reduce the false positives and false negatives caused by HFs. For the polyps on HFs, a novel segmentation method is specially developed based on complementary geodesic distance transformation(CGDT). The proposed method is tested on four different models and real CT data. The property of CGDT is proved and our method turns out to be effective for HF segmentation and polyp segmentation. The encouraging experimental results primarily show the feasibility of the proposed method and its potential to improve the detection performance of CAD schemes.

Interactive Liver Segmentation Algorithm Based on Geodesic Distance and V-Net

Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.

Topological distance-constrained feature descriptor learning model for vessel matching in coronary angiographies

Background Feature matching technology is vital to establish the association between virtual and real objects in virtual reality and augmented reality systems.Specifically,it provides them with the ability to match a dynamic scene.Many image matching methods,of which most are deep learning-based,have been proposed over the past few decades.However,vessel fracture,stenosis,artifacts,high background noise,and uneven vessel gray-scale make vessel matching in coronary angiography extremely difficult.Traditional matching methods perform poorly in this regard.Methods In this study,a topological distance-constrained feature descriptor learning model is proposed.This model regards the topology of the vasculature as the connection relationship of the centerline.The topological distance combines the geodesic distance between the input patches and constrains the descriptor network by maximizing the feature difference between connected and unconnected patches to obtain more useful potential feature relationships.Results Matching patches of different sequences of angiographic images are generated for the experiments.The matching accuracy and stability of the proposed method is superior to those of the existing models.Conclusions The proposed method solves the problem of matching coronary angiographies by generating a topological distance-constrained feature descriptor.

A NATURAL GRADIENT ALGORITHM FOR THE SOLUTION OF LYAPUNOV EQUATIONS BASED ON THE GEODESIC DISTANCE

A new framework based on the curved Riemannian manifold is proposed to calculate the numerical solution of the Lyapunov matrix equation by using a natural gradient descent algorithm and taking the geodesic distance as the objective function. Moreover, a gradient descent algorithm based on the classical Euclidean distance is provided to compare with this natural gradient descent algorithm. Furthermore, the behaviors of two proposed algorithms and the conventional modified conjugate gradient algorithm are compared and demonstrated by two simulation examples. By comparison, it is shown that the convergence speed of the natural gradient descent algorithm is faster than both of the gradient descent algorithm and the conventional modified conjugate gradient algorithm in solving the Lyapunov equation.

The Fermat-Torricelli problem on surfaces

In this note a simple proof of the famous Fermat-Torricelli problem is given. For the vertices of a given triangle, Fermat asks for a fourth point such that the sum of its Euclidean distances to the three given points is minimized. Many authors present geometric approaches to the Fermat-Torricelli problem. We solve the problem by analytic and geometrical method and extend it to the sphere, we also characterize the median point P on the general regular surface.

Data-driven facial animation based on manifold Bayesian regression

Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces. We propose a solution named manifold Bayesian regression. First a novel distance metric, the geodesic manifold distance, is introduced to replace the Euclidean distance. The problem of facial animation can be formulated as a sparse warping kernels regression problem, in which the geodesic manifold distance is used for modelling the topology and discontinuities of the face models. The geodesic manifold distance can be adopted in traditional regression methods, e.g. radial basis functions without much tuning. We put facial animation into the framework of Bayesian regression. Bayesian approaches provide an elegant way of dealing with noise and uncertainty. After the covariance matrix is properly modulated, Hybrid Monte Carlo is used to approximate the integration of probabilities and get deformation results. The experimental results showed that our algorithm can robustly produce facial animation with large motions and complex face models.

Stochastic Contrast Measures for SAR Data: A Survey

“Contrast”is an generic denomination for“difference”.Measures of contrast are a powerful tool in image processing and analysis,e.g.,in denoising,edge detection,segmentation,classification,parameter estimation,change detection,and feature selection.We present a survey on techniques that aim at measuring the contrast between(i)samples of SAR imagery,and(ii)samples and models,with emphasis on those that employ the statistical properties of the data.

Generation of Performance-Driven Facial Expressions

Coordinates of the key facial feature points can be captured by motion capture system OPTOTRAK with real-time character and high accuracy. The facial model is considered as an undirected weighted graph. By iteratively subdividing the related triangle edges, the geodesic distance between points on the model surface is finally obtained. The RBF （Radial Basis Functions） interpolation technique based on geodesic distance is applied to generate deformation of the facial mesh model. Experimental results demonstrate that the geodesic distance can explore the complex topology of human face models perfectly and the method can generate realistic facial expressions.