Evidence map›Paper›PMID 28458481›Full record

ArticleJournal of electronic imaging2016

Comparing object recognition from binary and bipolar edge images for visual prostheses.

Jae-Hyun Jung, Tian Pu, Eli Peli

Abstract read
In one paragraph

Article in Journal of electronic imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

3 authors.

Jae-Hyun JungHarvard Medical School, Massachusetts Eye and Ear, Department of Ophthalmology, Schepens Eye Research Institute, 20 Staniford Street, Boston, Massachusetts 02114, United States.
Tian PuHarvard Medical School, Massachusetts Eye and Ear, Department of Ophthalmology, Schepens Eye Research Institute, 20 Staniford Street, Boston, Massachusetts 02114, United States.
Eli PeliHarvard Medical School, Massachusetts Eye and Ear, Department of Ophthalmology, Schepens Eye Research Institute, 20 Staniford Street, Boston, Massachusetts 02114, United States.

Funding

MODEL-BASED IMAGE ENHANCEMENT FOR THE VISUALLY IMPAIREDR01EY005957 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI PELI, ELI · 1986 to 2013
$5.6M
NEI NIH HHS R01 EY005957
6 · The paper itself

Abstract

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition.

Indexed as

bipolar edgecontrast polaritycuspedge detectionfeature detectionobject recognition

Identifiers

PMID28458481
PMCPMC5407304

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Registered trials

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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.