Pseudo Contours Research Articles

A novel contour feature denoising method for real geometrical parameters measurement of multi-core fiber

The multi-core fiber (MCF) plays an important role in expanding capacity. The geometrical parameters and its changes will greatly influence the transmission characteristics of the fiber and the performance of the MCF based devices. The traditional denoising method for analyzing geometrical parameters of the fiber is complex, and the high quality of the fiber image is demanded, which result in poor applicability. In this paper, a novel contour feature denoising method for real geometrical parameters measurement of the fiber is proposed. Compared with the traditional denoising method, the advantage of the proposed method is that denoising the contour image reduces algorithm redundancy, and through the positional relationship between the useful and the pseudo contours, it can effectively remove the influence of noise on the measurement results. The geometrical parameters of two commercial seven-core fibers (SCF) are analyzed by the proposed method, the results show that the real geometrical cross-section of the fiber can be exactly exhibited, and the measurement error of the geometrical parameters is reduced by 4%.

Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

Large-Scale Point Cloud Contour Extraction via 3-D-Guided Multiconditional Residual Generative Adversarial Network

As one of the most important features for human perception, contours are widely applied in graphics and mapping applications. However, it is considerably challenging to extract contours from large-scale point clouds due to the irregular distribution of point clouds. In this letter, we propose a 3-D-guided multiconditional residual generative adversarial network (3-D-GMRGAN), the first deep-learning framework to generate contours for large-scale outdoor point clouds. To make the network handle huge amounts of points, we operate contours in the parametric space rather than raw point space, associated with a parametric chamfer distance. Then, to gather contour features from potential positions and avoid the huge solution space, we propose a guided residual generative adversarial framework, by utilizing a simple feature-based method to get the “over extraction” potential contour distribution. Experiments demonstrate that the proposed method is able to generate contours efficiently for large-scale point clouds, with fewer outliers and pseudo contours compared with state-of-the-art approaches.

Directive local color transfer based on dynamic look-up table

Color transfer in image processing usually suffers from misleading color mapping and loss of details. This paper presents a novel directive local color transfer method based on dynamic look-up table (D-DLT) to solve these problems in two steps. First, a directive mapping between the source and the reference image is established based on the salient detection and the color clusters to obtain directive color transfer intention. Then, dynamic look-up tables are created according to the color clusters to preserve the details, which can suppress pseudo contours and avoid detail loss. Subjective and objective assessments are presented to verify the feasibility and the availability of the proposed approach. Experimental results demonstrate that our proposed method has better performance on natural color images than classical color transfer algorithms. Furthermore, the reference image can be extended to color blocks instead of images.

Infrared image enhancement with learned features

Due to the variation of imaging environment and limitations of infrared imaging sensors, infrared images usually have some drawbacks: low contrast, few details and indistinct edges. Hence, to promote the applications of infrared imaging technology, it is essential to improve the qualities of infrared images. To enhance image details and edges adaptively, we propose an infrared image enhancement method under the proposed image enhancement scheme. On the one hand, on the assumption of high-quality image taking more evident structure singularities than low-quality images, we propose an image enhancement scheme that depends on the extractions of structure features. On the other hand, different from the current image enhancement algorithms based on deep learning networks that try to train and build the end-to-end mappings on improving image quality, we analyze the significance of first layer in Stacked Sparse Denoising Auto-encoder and propose a novel feature extraction for the proposed image enhancement scheme. Experiment results prove that the novel feature extraction is free from some artifacts on the edges such as blocking artifacts, “gradient reversal”, and pseudo contours. Compared with other enhancement methods, the proposed method achieves the best performance in infrared image enhancement.

Simultaneous probability statements for Bayesian P-splines

P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparametric regression models. Recently, a Bayesian version for P-splines has been developed on the basis of Markov chain Monte Carlo simulation techniques for inference. In this work, we adopt and generalize the concept of Bayesian contour probabilities to additive models with Gaussian or multicategorical responses. More specifically, we aim at computing the maximum credible level (sometimes called Bayesian p-value) for which a particular parameter vector of interest lies within the corresponding highest posterior density (HPD) region. We are particularly interested in parameter vectors that correspond to a constant, linear or, more generally, a polynomial fit. As an alternative to HPD regions, simultaneous credible intervals could be used to define pseudo contour probabilities. Efficient algorithms for computing contour and pseudo contour probabilities are developed. The performance of the approach is assessed through simulation studies. Two applications on the determinants of undernutrition in developing countries and the health status of trees show how contour probabilities may be used in practice to assist the analyst in the model building process.

SU-GG-T-33: Accurate Localization of the Needle Position During the Real Time Seed Implant Procedure in OR

Purpose: Accurately localize the needle tip position to improve the dosimetry accuracy of real time prostate seed implant in the OR. Method and Materials: Variseed seed implant software version 7.2 was utilized to study the needle identification process. To accurately determine the needle position under the artifact reflection of ultrasoundimages in the real time planning mode is challenging. We have developed a simplified method to study the true location of the implemented needles during the real time based seed implant procedure. A zero mark pseudo contour was introduced to identify the needle end dwell position with the axial view of the prostate base. It was identified to correlate the true needle position before dropping the seeds. Bladder wall interface on the sagittal view also justified for the needle identification in finding actual seed location. Rotating the ultrasound probe along with the zero mark slice correlation helps to clarify the trajectory of the needles. Results: With the implant views needle correlation; we have performed the variation study from the optimized plan versus the plan after finishing the seeds dropping. Less than 1% variation of PTV coverage (criteria V100 > 90%, D90 < 115% prescription for prostate, V125 < 0 cc for both urethra and rectum) is achieved with great accuracy. Optimization criteria have not been disturbed by our method, and the post implant CTdosimetry shows great coverage for prostate day 1 and day 30 studies. Conclusion: Real time prostate implant procedure provides invaluable dosimetry benefit compared to the pre‐planned implant method. However, needle tip position correlation during the implant process presents challenging uncertainty for both pre‐plan and real time plan process. We developed a simple method to improve the confidence level of the real time implant dosimetry, with implant needle accurately projected during the seed loading process.