Automatic bounding-box-labeling method of occluded objects in virtual image data

2020 ◽  
Author(s):  
Xinyue Wang ◽  
Lingzhong Meng ◽  
Yunzhi Xue
Keyword(s):  
Image Data ◽  
Virtual Image ◽  
Bounding Box ◽  
Occluded Objects ◽  
Labeling Method

scholarly journals Segmentation Comparing Eggs Watermarking Image and Original Image

2017 ◽  
Vol 6 (1) ◽  
pp. 47-53
Author(s):  
Anton Yudhana ◽  
Sunardi Sunardi ◽  
Shoffan Saifullah
Keyword(s):  
Image Processing ◽  
Small Area ◽  
Image Data ◽  
Watermark Image ◽  
Original Image ◽  
Segmentation Method ◽  
Bounding Box ◽  
Chicken Eggs ◽  
The Impact

The research used watermarking techniques to obtain the image originality. The aims of the research were to identify small area in eggs properly and compared preprocessing, the methods, and the results of image processing. The study has been improved from the previous papers by combined all methods and analysis was obtained.This study was conducted by using centroid and the bounding box for determining the object and the small area of chicken eggs. The segmentation method was used to compare the original image and the watermarked image. Image processing using image data that are subject watermark to maintain the authenticity of the images used in the study will the impact in delivering the desired results. In the identification of chicken eggs using watermark image using several methods are expected to provide results as desired. Segmentation also deployed to process the Image and counted the objects. The results showed that the process of segmentation and objects counting determined that the original image and watermarked image had the same value and recognized eggs. Identification had determined percentage of 100% for all the samples.

scholarly journals Robust Object Recognition under Partial Occlusions Using NMF

2008 ◽  
Vol 2008 ◽  
pp. 1-14 ◽  
Author(s):  
Daniel Soukup ◽  
Ivan Bajla
Keyword(s):  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Image Data ◽  
Data Representation ◽  
Training Data ◽  
Occluded Objects ◽  
Recognition Phase ◽  
The Matrix ◽  
Handwritten Digit ◽  
Image Object

In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation attracted the attention of computer vision community. These methods are considered as a convenient part-based representation of image data for recognition tasks with occluded objects. A novel modification in NMF recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated for two image databases, ORL face database, and USPS handwritten digit database. We have studied the behavior of four types of distances between a projected unknown image object and feature vectors in NMF subspaces generated for training data. One of these metrics also is a novelty we proposed. In the recognition phase, partial occlusions in the test images have been modeled by putting two randomly large, randomly positioned black rectangles into each test image.

The accuracy of quantitative parameters in99mTc-MAG3 dynamic renography: a national audit based on virtual image data

2014 ◽  
Vol 36 (2) ◽  
pp. 146-154 ◽  
Author(s):  
Gustav Brolin ◽  
Lars Edenbrandt ◽  
Göran Granerus ◽  
Anna Olsson ◽  
David Afzelius ◽  
...  
Keyword(s):  
Image Data ◽  
National Audit ◽  
Virtual Image ◽  
Quantitative Parameters

scholarly journals Grain Size Characterization of Ceramic Matrix Composites

2021 ◽  
Vol 11 (4) ◽  
pp. 317-322
Author(s):  
Gao Xiang ◽  
◽  
Tan Rong ◽  
Guanghui Li ◽  
Leijiang Yao
Keyword(s):  
Grain Size ◽  
Materials Science ◽  
Image Data ◽  
Ceramic Matrix Composites ◽  
Ceramic Matrix ◽  
Matrix Composites ◽  
Bounding Box ◽  
Size Characterization ◽  
Bounding Boxes

In the field of materials science, the mesoscopic geometry of materials is of great significance for the research and development of materials and materials. This paper mainly focuses on the image data of existing ceramic matrix composites, and studies the characterization method of grain image of ceramic matrix, which realizes the accurate characterization of grain size. It has important practical research on the mesostructure of ceramic matrix composites. Value. Taking the SEM grain image of 5μm resolution of self-toughening silicon nitride (Si3N4) ceramic as an example, the grain image is segmented by median filtering, image binarization and watershed algorithm, and then used to directional bounding box (Oriented). The Bounding Boxes, OBB) algorithm finds the rectangular outline bounding box of the grain, enabling accurate measurement and statistics of the grain size.

scholarly journals An Efficient High-Quality Medical Lesion Image Data Labeling Method Based on Active Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 144331-144342
Author(s):  
Jiancun Zhou ◽  
Rui Cao ◽  
Jian Kang ◽  
Kehua Guo ◽  
Yangting Xu
Keyword(s):  
Active Learning ◽  
Image Data ◽  
High Quality ◽  
Labeling Method

scholarly journals 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

2020 ◽  
Author(s):  
Daria Kern ◽  
Andre Mastmeyer
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Image ◽  
Image Data ◽  
Random Regression ◽  
Feature Engineering ◽  
Anatomical Structures ◽  
Bounding Box ◽  
Medical Image Data ◽  
2D And 3D

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.<br>

A marked bounding box method for image data reduction and reconstruction of sole patterns

2012 ◽  
Author(s):  
Xingyue Wang ◽  
Jianhua Wu ◽  
Qingmin Zhao ◽  
Jian Cheng ◽  
Yican Zhu
Keyword(s):  
Data Reduction ◽  
Image Data ◽  
Bounding Box ◽  
Box Method

scholarly journals 3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

2020 ◽  
Author(s):  
Daria Kern ◽  
Andre Mastmeyer
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Image ◽  
Image Data ◽  
Random Regression ◽  
Feature Engineering ◽  
Anatomical Structures ◽  
Bounding Box ◽  
Medical Image Data ◽  
2D And 3D

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.<br>

Dynamical Scattering in Crystalline Biological Specimens. I. Criteria of Validity for Scattering Approximations

1975 ◽  
Vol 33 ◽  
pp. 192-193
Author(s):  
Robert M. Glaeser ◽  
Bing K. Jap
Keyword(s):  
Biological Sciences ◽  
Electron Microscopy ◽  
Electron Microscope ◽  
Electron Diffraction ◽  
Born Approximation ◽  
Materials Science ◽  
Image Data ◽  
Dynamical Effect ◽  
Crystallographic Structure ◽  
Electron Microscope Image

The dynamical scattering effect, which can be described as the failure of the first Born approximation, is perhaps the most important factor that has prevented the widespread use of electron diffraction intensities for crystallographic structure determination. It would seem to be quite certain that dynamical effects will also interfere with structure analysis based upon electron microscope image data, whenever the dynamical effect seriously perturbs the diffracted wave. While it is normally taken for granted that the dynamical effect must be taken into consideration in materials science applications of electron microscopy, very little attention has been given to this problem in the biological sciences.

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