scholarly journals Image Search System for Data Sets of Small Body Exploration with a 3D Polygon Shape Model

2012 ◽  
Vol 10 (ists28) ◽  
pp. Tk_7-Tk_14
Author(s):  
Wataru KAWAMAE ◽  
Naru HIRATA ◽  
Kohei KITAZATO ◽  
Hirohide DEMURA
Keyword(s):  
Small Body ◽  
Data Sets ◽  
Image Search ◽  
Search System ◽  
Shape Model

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

scholarly journals Content-Based Image Retrieval for Multiple Objects Search

2017 ◽  
Vol 17 (2) ◽  
pp. 106-118
Author(s):  
Gábor Szűcs ◽  
Dávid Papp
Keyword(s):  
Search Engines ◽  
Search Task ◽  
State Of The Art ◽  
Image Search ◽  
Multiple Objects ◽  
Search System ◽  
Search Methods ◽  
Complex Queries ◽  
Web Images ◽  
Binary Classifiers

Abstract The progress of image search engines still proceeds, but there are some challenges yet in complex queries. In this paper, we present a new semantic image search system, which is capable of multiple object retrieval using only visual content of the images. We have used the state-of-the-art image processing methods prior to the search, such as Fisher-vector and C-SVC classifier, in order to semantically classify images containing multiple objects. The results of this offline classification are stored for the latter search task. We have elaborated more search methods for combining the results of binary classifiers of objects in images. Our search methods use confidence values of object classifiers and after the evaluation, the best method is selected for thorough analysis. Our solution is compared with the famous web images search engines (Google, Bing and Flickr), and there is a comparison of their Mean Average Precision (MAP) values. It can be concluded that our system reaches the benchmark; moreover, in most cases our method outperforms the others, especially in the cases of queries with many objects.

scholarly journals Heterogeneous mass distribution of the rubble-pile asteroid (101955) Bennu

2020 ◽  
Vol 6 (41) ◽  
pp. eabc3350 ◽  
Author(s):  
D. J. Scheeres ◽  
A. S. French ◽  
P. Tricarico ◽  
S. R. Chesley ◽  
Y. Takahashi ◽  
...  
Keyword(s):  
Gravity Field ◽  
Mass Distribution ◽  
Small Body ◽  
Parent Body ◽  
Constant Density ◽  
Internal Mass ◽  
Shape Model ◽  
Rapid Rotation ◽  
Insight Into ◽  
Rubble Pile

The gravity field of a small body provides insight into its internal mass distribution. We used two approaches to measure the gravity field of the rubble-pile asteroid (101955) Bennu: (i) tracking and modeling the spacecraft in orbit about the asteroid and (ii) tracking and modeling pebble-sized particles naturally ejected from Bennu’s surface into sustained orbits. These approaches yield statistically consistent results up to degree and order 3, with the particle-based field being statistically significant up to degree and order 9. Comparisons with a constant-density shape model show that Bennu has a heterogeneous mass distribution. These deviations can be modeled with lower densities at Bennu’s equatorial bulge and center. The lower-density equator is consistent with recent migration and redistribution of material. The lower-density center is consistent with a past period of rapid rotation, either from a previous Yarkovsky-O’Keefe-Radzievskii-Paddack cycle or arising during Bennu’s accretion following the disruption of its parent body.

Preference Extraction in Image Retrieval

2011 ◽  
pp. 235-260 ◽  
Author(s):  
Pawel Rotter ◽  
Andrzej M.J. Skulimowski
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Real Life ◽  
Selection Procedure ◽  
User Preferences ◽  
Image Search ◽  
Complex Objects ◽  
Search System ◽  
Preference Information ◽  
New Approaches

In this chapter, we describe two new approaches to content-based image retrieval (CBIR) based on preference information provided by the user interacting with an image search system. First, we present the existing methods of image retrieval with relevance feedback, which serve then as a reference for the new approaches. The first extension of the distance function-based CBIR approach makes it possible to apply this approach to complex objects. The new algorithm is based on an approximation of user preferences by a neural network. Further, we propose another approach to image retrieval, which uses reference sets to facilitate image comparisons. The methods proposed have been implemented, and compared with each other, and with the earlier approaches. Computational experiments have proven that the new preference extraction and image retrieval procedures here proposed are numerically efficient. Finally, we provide a real-life illustration of the methods proposed: an image-based hotel selection procedure.

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