scholarly journals On the use of spatial relations between objects for image classification

2007 ◽  
pp. 347-356 ◽  
Author(s):  
Nicolas Tsapatsoulis ◽  
Sergios Petridis ◽  
Stavros J. Perantonis
Keyword(s):  
Image Classification ◽  
Spatial Relations

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

Semantic image classification using statistical local spatial relations model

2008 ◽  
Vol 39 (2) ◽  
pp. 169-188 ◽  
Author(s):  
Dongfeng Han ◽  
Wenhui Li ◽  
Zongcheng Li
Keyword(s):  
Image Classification ◽  
Spatial Relations

Improved preservation of the otolithic membranes

1987 ◽  
Vol 45 ◽  
pp. 914-915
Author(s):  
G. M. Cohen ◽  
J. S. Grasso ◽  
M. L. Domeier ◽  
P. T. Mangonon
Keyword(s):  
Toluidine Blue ◽  
Potassium Phosphate ◽  
Vestibular Function ◽  
Spatial Relations ◽  
White Leghorn ◽  
Supporting Cells ◽  
Semithin Sections ◽  
Micromechanical Models ◽  
Soluble Components

Any explanation of vestibular micromechanics must include the roles of the otolithic and cupular membranes. However, micromechanical models of vestibular function have been hampered by unresolved questions about the microarchitectures of these membranes and their connections to stereocilia and supporting cells. Otolithic membranes are notoriously difficult to preserve because of severe shrinkage and loss of soluble components. We have empirically developed fixation procedures that reduce shrinkage artifacts and more accurately depict the spatial relations between the otolithic membranes and the ciliary bundles and supporting cells.We used White Leghorn chicks, ranging in age from newly hatched to one week. The inner ears were fixed for 3-24 h in 1.5-1.75% glutaraldehyde in 150 mM KCl, buffered with potassium phosphate, pH 7.3; when postfixed, it was for 30 min in 1% OsO4 alone or mixed with 1% K4Fe(CN)6. The otolithic organs (saccule, utricle, lagenar macula) were embedded in Araldite 502. Semithin sections (1 μ) were stained with toluidine blue.

Differences in the Coding of Spatial Relations in Animals and Objects

1999 ◽  
Author(s):  
Eric E. Cooper ◽  
Brian E. Brooks
Keyword(s):  
Spatial Relations

Facilitation of learning spatial relations among locations in the absence of coincident visual cues

2009 ◽  
Author(s):  
Bradley R. Sturz ◽  
Debbie M. Kelly ◽  
Michael F. Brown
Keyword(s):  
Visual Cues ◽  
Spatial Relations

A Universe of Societies

2018 ◽  
Author(s):  
Didier Debaise
Keyword(s):  
Spatial Relations ◽  
The Third ◽  
The Status ◽  
History Of ◽  
Temporal And Spatial

Which kind of relation exists between a stone, a cloud, a dog, and a human? Is nature made of distinct domains and layers or does it form a vast unity from which all beings emerge? Refusing at once a reductionist, physicalist approach as well as a vitalistic one, Whitehead affirms that « everything is a society » This chapter consequently questions the status of different domains which together compose nature by employing the concept of society. The first part traces the history of this notion notably with reference to the two thinkers fundamental to Whitehead: Leibniz and Locke; the second part defines the temporal and spatial relations of societies; and the third explores the differences between physical, biological, and psychical forms of existence as well as their respective ways of relating to environments. The chapter thus tackles the status of nature and its domains.

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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