scholarly journals Video Smoke Removal from a Single Image Sequence

2021 ◽  
Author(s):  
Shiori YAMAGUCHI ◽  
Keita HIRAI ◽  
Takahiko HORIUCHI
Keyword(s):  
Image Sequence ◽  
Single Image ◽  
Smoke Removal

5239266 MRI using variable imaging parameter(s) within a single image sequence

1994 ◽  
Vol 12 (5) ◽  
pp. XI ◽  
Author(s):  
Leon Kaufman ◽  
David M Kramer ◽  
John M Coleman
Keyword(s):  
Image Sequence ◽  
Single Image ◽  
Imaging Parameter

Restoration and resolution enhancement of a single image from a vibration-distorted image sequence

1999 ◽  
Author(s):  
Adrian Stern ◽  
Elad Kempner ◽  
Avi Shukrun ◽  
Norman S. Kopeika
Keyword(s):  
Resolution Enhancement ◽  
Image Sequence ◽  
Single Image ◽  
Distorted Image

scholarly journals CLASSIFICATION OF FEATURES OF IMAGE SEQUENCES

2015 ◽  
Vol XL-5/W6 ◽  
pp. 79-81 ◽  
Author(s):  
E. V. Pugin ◽  
A. L. Zhiznyakov
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Feature Space ◽  
Image Sequence ◽  
Image Sequences ◽  
Semantic Features ◽  
Single Image ◽  
Video Cameras

Processing of image sequences is a very actual trend now. This is confirmed with a vast amount of researches in that area. The possibility of an image sequence processing and pattern recognition became available because of increased computer capabilities and better photo and video cameras. The feature extraction is one of the main steps during image processing and pattern recognition. This paper presents a novel classification of features of image sequences. The proposed classification has three groups: 1) features of a single image, 2) features of an image sequence, 3) semantic features of an observed scene. The first group includes features extracted from a single image. The second group consists of features of any kinds of image sequences. The third group contains semantic features. Reverse feature clarification method is the iterative method when on each iteration we use higher level features to extract lower level features more precisely. The proposed classification of features of image sequences solves a problem of decomposition of the source feature space into several groups. Reverse feature clarification method allows to increase the quality of image processing during iterative process.

Sonography of subcutaneous tissue cannot determine causes of lower limb edema

VASA ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 122-128 ◽  
Author(s):  
Mandy Becker ◽  
Tom Schilling ◽  
Olga von Beckerath ◽  
Knut Kröger
Keyword(s):  
Clinical Diagnosis ◽  
Lower Limb ◽  
Subcutaneous Tissue ◽  
Hepatic Disease ◽  
Clinical Use ◽  
Single Image ◽  
Heart Insufficiency ◽  
Chronic Hepatic Disease ◽  
Limb Edema ◽  
Venous Disorders

Background: To clarify the clinical use of sonography for differentiation of edema we tried to answer the question whether a group of doctors can differentiate lymphedema from cardiac, hepatic or venous edema just by analysing sonographic images of the edema. Patients and methods: 38 (70 ± 12 years, 22 (58 %) females) patients with lower limb edema were recruited according the clinical diagnosis: 10 (26 %) lymphedema, 16 (42 %) heart insufficiency, 6 (16 %) venous disorders, 6 (16 %) chronic hepatic disease. Edema was depicted sonographically at the most affected leg in a standardised way at distal and proximal calf. 38 sets of images were anonymised and send to 5 experienced doctors. They were asked whether they can see criteria for lymphedema: 1. anechoic gaps, 2. horizontal gaps and 3. echoic rims. Results: Accepting an edema as lymphedema if only one doctor sees at least one of the three criteria for lymphatic edema on each single image all edema would be classified as lymphatic. Accepting lymphedema only if all doctors see at least one of the three criteria on the distal image of the same patient 80 % of the patients supposed to have lymphedema are classified as such, but also the majority of cardiac, venous and hepatic edema. Accepting lymphedema only if all doctors see all three criteria on the distal image of the same patients no edema would be classified as lymphatic. In addition we separated patients by Stemmers’ sign in those with positive and negative sign. The interpretation of the images was not different between both groups. Conclusions: Our analysis shows that it is not possible to differentiate lymphedema from other lower limb edema sonographically.

Translucent Glass Roofs For Large-Span Coverings

2019 ◽  
pp. 23-28
Keyword(s):  
Warm Season ◽  
Natural Light ◽  
Labor Costs ◽  
Main Bearing ◽  
Large Span ◽  
Glass Panels ◽  
Technical Properties ◽  
Internal Volume ◽  
Basic Approaches ◽  
Smoke Removal

The article deals with the issues of glass use in the enclosing structures of large-span coverings, which have such advantages as ensuring the penetration of natural light, tightness, minimum labor costs for repair and maintenance. Design shortcomings: the high cost, the need for protection of the internal volume against the penetrating sun rays in the warm season (hothouse effect); arrangement of devices for operation of a roof. The key technical properties and characteristics of glass panels and pane-glass sets, constructive decisions, including interface to the main bearing structures of a large-span covering are given. Peculiarities of their design with due regard for ventilation and smoke removal, a drainage of condensate, ways of fight against frosting and snow drifts on the roof are reflected. Features of the account of loadings, the basic approaches to their calculation are considered. Various design solutions for the spatial metal trussed systems with the original nodal connections are presented. Information on modern solutions of translucent roofs using glass for large-span coverings is given.

Single Image Dehazing by Predicting Atmospheric Scattering Parameters

2020 ◽  
Vol 2020 (1) ◽  
pp. 74-77
Author(s):  
Simone Bianco ◽  
Luigi Celona ◽  
Flavio Piccoli
Keyword(s):  
Reference Image ◽  
Single Image ◽  
Scattering Parameters ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Local Structures ◽  
Full Reference ◽  
Reconstruction Quality ◽  
Single Image Dehazing

In this work we propose a method for single image dehazing that exploits a physical model to recover the haze-free image by estimating the atmospheric scattering parameters. Cycle consistency is used to further improve the reconstruction quality of local structures and objects in the scene as well. Experimental results on four real and synthetic hazy image datasets show the effectiveness of the proposed method in terms of two commonly used full-reference image quality metrics.

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