The DOI matches its Crossref record.
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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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
“Before HOG and SIFT, early work on face detection with ConvNets [38, 32] computed shallow networks over image pyramids to detect faces across scales.”
BackgroundFocal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
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
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