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Original approach for the localisation of objects in images
Abstract
An original approach is presented for the localisation of objects in an image which approach is neuronal and has two steps. In the first step, a rough localisation is performed by presenting each pixel with its neighbourhood to a neural net which is able to indicate whether this pixel and its neighbourhood are the image of the search object. This first filter does not discriminate for position. From its result, areas which might contain an image of the object can be selected. In the second step, these areas are presented to another neural net which can determine the exact position of the object in each area. This algorithm is applied to the problem of localising faces in images.
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“The idea has been applied to face location [93], address block location on envelopes [94], and hand tracking in video [95].”
From Gradient-based learning applied to document recognition (1998) · cites this paper as [94]
In the citing paper
“The use of CNNs for detection and localization (Rowley et al. 1998) can be traced back to the 1990s, with a modest number of hidden layers used for object detection (Vaillant et al. 1994;Rowley et al. 1998;Sermanet et al. 2013), successful in restricted domains such as face detec-tion.”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Vaillant et al. 1994
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Metadata
- Authors
- Vaillant, R.
- Affiliations
- Technicolor (France) (FR)
- Corresponding author
- Vaillant, R.
- Venue
- IEE Proceedings - Vision, Image, and Signal Processing
- Published
- 1994
- Volume
- 141
- Issue
- 4
- Pages
- 245
- ISSN
- 1350-245X (ISSN-L)
- OpenAlex
- W2056695679
- Cited by
- 280
- References
- 0
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 13 · 2022: 18 · 2021: 28 · 2020: 27 · 2019: 28 · 2018: 22 · 2017: 28 · 2016: 15 · 2015: 11 · 2014: 7 · 2013: 3 · 2012: 3
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEE Proceedings - Vision Image and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- object localization, image search, artificial neural networks, face detection, object position estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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