Evidence map›Paper›PMID 29693590›Full record

ArticleSensors (Basel, Switzerland)2018

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments.

Oscar García-Olalla, Enrique Alegre, Laura Fernández-Robles, Eduardo Fidalgo, Surajit Saikia

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Special Issue on Visual Sensors.Sensors (Basel, Switzerland) · 2020
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Oscar García-OlallaDepartment of Electrical, Systems and Automation, Universidad de León, 24071 León, Spain. ogaro@unileon.es.
Enrique AlegreDepartment of Electrical, Systems and Automation, Universidad de León, 24071 León, Spain. ealeg@unileon.es.ORCID 0000-0003-2081-774X
Laura Fernández-RoblesDepartment of Mechanical, Computer Science and Aerospace Engineering, Universidad de León, 24071 León, Spain. l.fernandez@unileon.es.ORCID 0000-0001-6573-8477
Eduardo FidalgoDepartment of Electrical, Systems and Automation, Universidad de León, 24071 León, Spain. efidf@unileon.es.ORCID 0000-0003-1202-5232
Surajit SaikiaDepartment of Electrical, Systems and Automation, Universidad de León, 24071 León, Spain. ssai@unileon.es.
Universidad de León · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

Indexed as

content-based image retrievaltextile localizationtextile retrievaltexture descriptiontexture retrievalvisual sensors

Identifiers

PMID29693590
PMCPMC5982789
OpenAlexW2799566101

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.