scholarly journals Holographic Element-Based Effective Perspective Image Segmentation and Mosaicking Holographic Stereogram Printing

2019 ◽  
Vol 9 (5) ◽  
pp. 920 ◽  
Author(s):  
Fan Fan ◽  
Xiaoyu Jiang ◽  
Xingpeng Yan ◽  
Jun Wen ◽  
Song Chen ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Digital Holography ◽  
Image Plane ◽  
Direct Writing ◽  
High Quality ◽  
Holographic Stereogram ◽  
Reconstruction Image ◽  
Perspective Image ◽  
Printing Method

Effective perspective image segmentation and mosaicking (EPISM) method is an effective holographic stereogram printing method, but a mosaic misplacement of reconstruction image occurred when focusing away from the reconstruction image plane. In this paper, a method known as holographic element-based effective perspective image segmentation and mosaicking is proposed. Holographic element (hogel) correspondence is used in EPISM method as pixel correspondence is used in direct-writing digital holography (DWDH) method to generate effective perspective images segments. The synthetic perspective image for holographic stereogram printing is obtained by mosaicking all the effective perspective images segments. Optical experiments verified that the holographic stereogram printed by the proposed method can provide high-quality reconstruction imagery and solve the mosaic misplacement inherent in the EPISM method.

Printing Method of Holographic Stereogram Reconstructed with White Light Based on Effective Perspective Image Segmentation and Mosaicking

2020 ◽  
Vol 57 (12) ◽  
pp. 120901
Author(s):  
韩超 Han Chao ◽  
蒋晓瑜 Jiang Xiaoyu ◽  
樊帆 Fan Fan ◽  
王晨卿 Wang Chenqing ◽  
张腾 Zhang Teng ◽  
...  
Keyword(s):  
Image Segmentation ◽  
White Light ◽  
Holographic Stereogram ◽  
Perspective Image ◽  
Printing Method

General Hogel-Based Effective Perspective Image Segmentation and Mosaicking Method for Holographic Stereogram Printing

2019 ◽  
Vol 46 (12) ◽  
pp. 1209001
Author(s):  
樊帆 Fan Fan ◽  
闫兴鹏 Yan Xingpeng ◽  
李沛 Li Pei ◽  
张腾 Zhang Teng ◽  
韩超 Han Chao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Holographic Stereogram ◽  
Perspective Image

Optimal Scale of Hierarchical Image Segmentation with Scribbles Guidance for Weakly Supervised Semantic Segmentation

2021 ◽  
pp. 2154026
Author(s):  
Zaid Al-Huda ◽  
Donghai Zhai ◽  
Yan Yang ◽  
Riyadh Nazar Ali Algburi
Keyword(s):  
Image Segmentation ◽  
Graphical Model ◽  
Semantic Segmentation ◽  
Saliency Map ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
High Quality ◽  
Optimal Scale ◽  
Supervised Segmentation ◽  
Weakly Supervised

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.

Interactive Medical Image Segmentation Method with only Positive Label Examples

2014 ◽  
Vol 989-994 ◽  
pp. 1088-1092
Author(s):  
Chen Guang Zhang ◽  
Yan Zhang ◽  
Xia Huan Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
High Quality ◽  
Neighbor Graph ◽  
Foreground Region ◽  
Training Examples ◽  
Positive Label ◽  
Negative Training

In this paper, a novel interactive medical image segmentation method called SMOPL is proposed. This method only needs marking some pixels on foreground region for segmentation. To do this, SMOPL characterize the inherent correlations among foreground and background pixels as Hilbert-Schmidt independence. By maximizing the independence and minimizing the smoothness of labels on instance neighbor graph simultaneously, SMOPL gets the sufficiently smooth confidences of both positive and negative classes in absence of negative training examples. Then a image segmentation can be obtained by assigning each pixel to the label for which the greatest confidence is calculated. Experiments on real-world medical images show that SMOPL is robust to get a high-quality segmentation with only positive label examples.

One-Step Holographic Stereogram Printing Method Based on Effectivel Perspective Images′ Segmentation and Mosaicking Method

2019 ◽  
Vol 46 (2) ◽  
pp. 0209002
Author(s):  
陈祎贝 Chen Yibei ◽  
闫兴鹏 Yan Xingpeng ◽  
苏健 Su Jian ◽  
张腾 Zhang Teng ◽  
陈卓 Chen Zhuo ◽  
...  
Keyword(s):  
Holographic Stereogram ◽  
One Step ◽  
Printing Method

scholarly journals Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1396 ◽  
Author(s):  
Jin Liu ◽  
Yanxin Li ◽  
Ruth Wilkins ◽  
Farrah Flegal ◽  
Joan H.M. Knoll ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Estimation Error ◽  
Chromosome Morphology ◽  
Average Dose ◽  
High Quality ◽  
Metaphase Cell ◽  
Three Samples ◽  
Cellular Debris ◽  
Segmentation Methods ◽  
Chromosome Fragments

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs arising primarily from sister chromatid separation, chromosome fragmentation, and cellular debris. This reduced FPs by an average of 55% and was highly specific to these abnormal structures (≥97.7%) in three samples. Additional filters selectively removed images with incomplete, highly overlapped, or missing metaphase cells, or with poor overall chromosome morphologies that increased FP rates. Image selection is optimized and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same image segmentation filtering procedures to both calibration and test samples reduced the average dose estimation error from 0.4 Gy to <0.2 Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing for multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations.

A 3D-printing method of fabrication for metals, ceramics, and multi-materials using a universal self-curable technique for robocasting

2020 ◽  
Vol 7 (4) ◽  
pp. 1083-1090 ◽  
Author(s):  
Danwei Zhang ◽  
Win Jonhson ◽  
Tun Seng Herng ◽  
Yong Quan Ang ◽  
Lin Yang ◽  
...  
Keyword(s):  
3D Printing ◽  
Complex Geometries ◽  
High Quality ◽  
Printing Technique ◽  
3D Printing Technique ◽  
Printing Method

A universal 3D printing technique for metals, ceramics and multi-materials with complex geometries for resultant dense high-quality structures.

scholarly journals STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation

2021 ◽  
Author(s):  
Nicola K Dinsdale ◽  
Mark Jenkinson ◽  
Ana IL Namburete
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
High Quality ◽  
Vast Number ◽  
Large Numbers ◽  
Segmentation Algorithms ◽  
Simultaneous Training ◽  
Training And Pruning

Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be learned in the optimisation process. Here, we develop the STAMP algorithm to allow the simultaneous training and pruning of a UNet architecture for medical image segmentation with targeted channelwise dropout to make the network robust to the pruning. We demonstrate the technique across segmentation tasks and imaging modalities. It is then shown that, through online pruning, we are able to train networks to have much higher performance than the equivalent standard UNet models while reducing their size by more than 85% in terms of parameters. This has the potential to allow networks to be directly trained on datasets where very low numbers of labels are available.

Sign in / Sign up

Export Citation Format

Share Document