Feature (computer vision)

In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions. More broadly a feature is any piece of information which is relevant for solving the computational task related to a certain application. This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at hand. Definition

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Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

Rémi Guillaume Petitpierre

In this work, we present a new semantic segmentation model for historical city maps that surpasses the state of the art in terms of flexibility and performance. Research in automatic map processing is largely focused on homogeneous corpora or even individual maps, leading to inflexible algorithms. Recently, convolutional neural networks have opened new perspectives for the development of more generic tools. Based on two new maps corpora, the first one centered on Paris and the second one gathering cities from all over the world, we propose a method for operationalizing the figuration based on traditional computer vision algorithms that allows large-scale quantitative analysis. In a second step, we propose a semantic segmentation model based on neural networks and implement several improvements. Finally, we analyze the impact of map figuration on segmentation performance and evaluate future ways to improve the representational flexibility of neural networks. To conclude, we show that these networks are able to semantically segment map data of a very large figurative diversity with efficiency.

2020

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Progressive Correspondence Pruning by Consensus Learning

Hongwei Li, Mathieu Salzmann, Rui Zhao, Chen Zhao

Correspondence pruning aims to correctly remove false matches (outliers) from an initial set of putative correspondences. The pruning process is challenging since putative matches are typically extremely unbalanced, largely dominated by outliers, and the random distribution of such outliers further complicates the learning process for learning-based methods. To address this issue, we propose to progressively prune the correspondences via a local-to-global consensus learning procedure. We introduce a "pruning" block that lets us identify reliable candidates among the initial matches according to consensus scores estimated using local-to-global dynamic graphs. We then achieve progressive pruning by stacking multiple pruning blocks sequentially. Our method outperforms state-of-the-arts on robust line fitting, camera pose estimation and retrieval-based image localization benchmarks by significant margins and shows promising generalization ability to different datasets and detector/descriptor combinations.

IEEE2021

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Camera Tracking using natural feature point matching for Augmented Reality

Touradj Ebrahimi, Cecilia Torralbo Gimeno

For augmented reality applications, accurate estimation of the camera pose is required. An existing video-based marker tracking system developed at the ITS presents some limitations when the marker is completely or partially occluded. The objective of this project is to improve the tracking of the camera on those cases. For this purpose, the tracking of the marker is extended to that of natural feature points around the marker's environment. The extension to natural features works as follows. Feature point candidates are being searched in the image. Depth estimation is implemented in a recursive method triangulating points between the first frame and subsequent frames. A particle filter is used to take into account the new measures received with every frame. Particle's weights are sensible to the state of the camera tracker and the proximity of the triangulated point to the expected depth. Eventually the distribution of the depth particle filter for a given feature converges at one point. This position is added to a map of feature points used to update the camera pose. Results show that tracking was improved. The camera pose was tracked even though the marker was not visible. However a small drift in its pose is detected due to the imprecision of feature point positions. This drift is not excessive in the sense that the tracker is able to recover the right camera pose. Future work will focus on improving the accuracy of the depth estimates.

2007

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