By-Catch - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

By-Catch

The Experts below are selected from a list of 534036 Experts worldwide ranked by ideXlab platform

Carsten Rother – 1st expert on this subject based on the ideXlab platform

  • Depth Super Resolution by Rigid Body Self-Similarity in 3D
    2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
    Co-Authors: Michael Hornácek, Christoph Rhemann, Margrit Gelautz, Carsten Rother

    Abstract:

    We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of ‘single-image’ super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars.

  • CVPR – Depth Super Resolution by Rigid Body Self-Similarity in 3D
    2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
    Co-Authors: Michael Hornácek, Christoph Rhemann, Margrit Gelautz, Carsten Rother

    Abstract:

    We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of ‘single-image’ super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars.

Julian Bonnerjea – 2nd expert on this subject based on the ideXlab platform

  • Scale-up of monoclonal antibody purification processes
    Journal of Chromatography B, 2007
    Co-Authors: Suzanne Aldington, Julian Bonnerjea

    Abstract:

    Mammalian cell culture technology has improved so rapidly over the last few years that it is now commonplace to produce multi-kilogram quantities of therapeutic monoclonal antibodies in a single batch. Purification processes need to be scaled-up to match the improved upstream productivity. In this chapter key practical issues and approaches to the scale-up of monoclonal antibody purification processes are discussed. Specific purification operations are addressed including buffer preparation, chromatography column sizing, aggregate removal, filtration and volume handling with examples given.

  • Scale-up of monoclonal antibody purification processes
    Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 2007
    Co-Authors: Suzanne Aldington, Julian Bonnerjea

    Abstract:

    Mammalian cell culture technology has improved so rapidly over the last few years that it is now commonplace to produce multi-kilogram quantities of therapeutic monoclonal antibodies in a single batch. Purification processes need to be scaled-up to match the improved upstream productivity. In this chapter key practical issues and approaches to the scale-up of monoclonal antibody purification processes are discussed. Specific purification operations are addressed including buffer preparation, chromatography column sizing, aggregate removal, filtration and volume handling with examples given. © 2006 Elsevier B.V. All rights reserved.

Florence Tupin – 3rd expert on this subject based on the ideXlab platform

  • Patch similarity under non Gaussian noise
    , 2016
    Co-Authors: Charles-alban Deledalle, Florence Tupin, Loïc Denis

    Abstract:

    Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from “real” data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises.

  • How to compare noisy patches? Patch similarity beyond Gaussian noise
    International Journal of Computer Vision, 2015
    Co-Authors: Charles-alban Deledalle, Loïc Denis, Florence Tupin

    Abstract:

    Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches (so-called image-based) compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. Recent progress in natural image modeling also makes intensive use of patch comparison. A fundamental difficulty when comparing two patches from “real” data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and machine learning. By expressing patch (dis)similarity as a detection test under a given noise model, we introduce these criteria with a new one and discuss their properties. We then assess their performance for different tasks: patch discrimination, image denoising, stereo-matching and motion-tracking under gamma and Poisson noises. The proposed criterion based on the generalized likelihood ratio is shown to be both easy to derive and powerful in these diverse applications.

  • Modeling the distribution of patches with shift-invariance: Application to SAR image restoration
    2014 IEEE International Conference on Image Processing (ICIP), 2014
    Co-Authors: Sonia Tabti, Charles-alban Deledalle, Loïc Denis, Florence Tupin

    Abstract:

    Patches have proven to be very effective features to model natural images and to design image restoration methods. Given the huge diversity of patches found in images, modeling the distribution of patches is a difficult task. Rather than attempting to accurately model all patches of the image, we advocate that it is sufficient that all pixels of the image belong to at least one well-explained patch. An image is thus described as a tiling of patches that have large prior probability. In contrast to most patch-based approaches, we do not process the image in patch space, and consider instead that patches should match well everywhere where they overlap. In-order to apply this modeling to the restoration of SAR images, we define a suitable data-fitting term to account for the statistical distribution of speckle. Restoration results are competitive with state-of-the art SAR despeckling methods.