Object Matching Using Boundary Descriptors

Instant Panoramic Texture Mapping with Semantic Object Matching for Large-Scale Urban Scene Reproduction

IEEE Transactions on Visualization and Computer Graphics ◽

10.1109/tvcg.2021.3067768 ◽

2021 ◽

pp. 1-1

Author(s):

Jinwoo Park ◽

Ik-beom Jeon ◽

Sung-eui Yoon ◽

Woontack Woo

Keyword(s):

Large Scale ◽

Texture Mapping ◽

Object Matching ◽

Urban Scene ◽

Semantic Object

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Rapid learning of minimally different words in five- to six-year-old children: effects of acoustic salience and hearing impairment

Journal of Child Language ◽

10.1017/s0305000915000197 ◽

2015 ◽

Vol 43 (2) ◽

pp. 310-337 ◽

Cited By ~ 2

Author(s):

MARCEL R. GIEZEN ◽

PAOLA ESCUDERO ◽

ANNE E. BAKER

Keyword(s):

Cochlear Implant ◽

Hearing Impairment ◽

Normal Hearing ◽

Task Demands ◽

Deaf Children ◽

Matching Task ◽

Object Matching ◽

Rapid Learning ◽

Minimal Pairs

AbstractThis study investigates the role of acoustic salience and hearing impairment in learning phonologically minimal pairs. Picture-matching and object-matching tasks were used to investigate the learning of consonant and vowel minimal pairs in five- to six-year-old deaf children with a cochlear implant (CI), and children of the same age with normal hearing (NH). In both tasks, the CI children showed clear difficulties with learning minimal pairs. The NH children also showed some difficulties, however, particularly in the picture-matching task. Vowel minimal pairs were learned more successfully than consonant minimal pairs, particularly in the object-matching task. These results suggest that the ability to encode phonetic detail in novel words is not fully developed at age six and is affected by task demands and acoustic salience. CI children experience persistent difficulties with accurately mapping sound contrasts to novel meanings, but seem to benefit from the relative acoustic salience of vowel sounds.

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Bayesian object matching

Machine Learning ◽

10.1007/s10994-013-5357-4 ◽

2013 ◽

Vol 92 (2-3) ◽

pp. 225-250 ◽

Cited By ~ 4

Author(s):

Arto Klami

Keyword(s):

Object Matching

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3D object matching based on local feature combined with color

2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) ◽

10.1109/iccwamtip.2013.6716590 ◽

2013 ◽

Author(s):

Shui-Ping Li ◽

Yan-Jun Li

Keyword(s):

Local Feature ◽

Object Matching ◽

3D Object

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Implementation of SIFT for detection of electronic waste

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10461 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 353

Author(s):

A Roshna Meeran ◽

V Nithya

Keyword(s):

Image Processing ◽

Electronic Waste ◽

Scale Invariant Feature Transform ◽

Recycling Process ◽

Scale Invariant ◽

Object Matching ◽

Invariant Feature ◽

Detection And Identification ◽

Feature Transform ◽

Scale Invariant Feature

The paper focuses on the investigation of image processing of Electronic waste detection and identification in recycling process of all Electronic items. Some of actually collected images of E-wastes would be combined with other wastes. For object matching with scale in-variance the SIFT (Scale -Invariant- Feature Transform) is applied. This method detects the electronic waste found among other wastes and also estimates the amount of electronic waste detected the give set of wastes. The detection of electronics waste by this method is most efficient ways to detect automatically without any manual means.

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