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Concept

Image fusion

The image fusion process is defined as gathering all the important information from multiple images, and their inclusion into fewer images, usually a single one. This single image is more informative and accurate than any single source image, and it consists of all the necessary information. The purpose of image fusion is not only to reduce the amount of data but also to construct images that are more appropriate and understandable for the human and machine perception. In computer vision, multisensor image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images. In remote sensing applications, the increasing availability of space borne sensors gives a motivation for different image fusion algorithms. Several situations in image processing require high spatial and high spectral resolution in a single image. Most of the available equipment is not capable of providing s

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Publications associées (15)

Publications associées

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Personnes associées (3)

Personnes associées

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Unités associées (2)

Unités associées

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Concepts associés

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Cours associés (2)

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Learning-based approaches for feature fusion in multi-modal indoor localization

Vincent Cabrini

Due to their aptitude to capture complex dependencies, neural networks are a promising candidate for indoor localization. Omnipresent phenomena such as multi-path signal propagation, shadowing and device noise introduce non-linear effects in the data, and make conventional geometry-based methods fail even in simple environments. This semester project explores few analytical outlier rejection algorithms and new fusion methods based on neural networks and compares them with an analytical model.

2020

Chargement

Extended Depth-of-Field for Color Images in Light Microscopy: Image Fusion and 3D Visualization

Niels Quack, Daniel Sage, Michaël Unser, Dimitri Nestor Alice Van De Ville

Bright-field microscopy suffers from a relatively small depth-of-field. Typically, the specimen’s profile covers a range larger than the depth-of-field, and parts of the specimen that lie outside the object plane appear blurred. The specimen can be ‘scanned’ by moving the object along the optical axis, and different images will contain different areas that are sharp. The purpose of image fusion is to combine those images into one single image with an extended depth-of-field. One promising method is based on the wavelet transform. We show how the wavelet-based image fusion technique can be successfully applied to obtain a sharp composite color image. The selection of the proper type of wavelets can improve the results. We also introduce a way to apply this technique for color images without introducing false colors. Finally, we show how height information on the specimen, which we obtain as a side product from our algorithm, can be used for 3D visualization.

2004

Chargement

Improving the Analysis Stage of the Fusion Method

Prentice-Hall1995

Chargement

DH-405: Foundations of digital humanities

This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.

CS-503: Visual intelligence : machines and minds

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data with a primary focus on embodied intelligence and vision for active agents.