scispace - formally typeset
Journal ArticleDOI

Original approach for the localisation of objects in images

R. Vaillant, +2 more
- Vol. 141, Iss: 4, pp 245-250
Reads0
Chats0
TLDR
An original approach is presented for the localisation of objects in an image which approach is neuronal and has two steps and is applied to the problem of localising faces 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.

read more

Citations
More filters
Journal ArticleDOI

Gradient-based learning applied to document recognition

TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Proceedings ArticleDOI

Feature Pyramid Networks for Object Detection

TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Posted Content

Fast R-CNN

TL;DR: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection that builds on previous work to efficiently classify object proposals using deep convolutional networks.
Related Papers (5)