SuperResolution-aided Recognition of Cytoskeletons in Scanning Probe Microscopy Images

@inproceedings{Colantonio2014SuperResolutionaidedRO,
  title={SuperResolution-aided Recognition of Cytoskeletons in Scanning Probe Microscopy Images},
  author={Sara Colantonio and Mario D’Acunto and Marco Righi and Ovidio Salvetti},
  booktitle={ICPRAM},
  year={2014}
}
In this paper, we discuss the possibility to adopt SuperResolution (SR) methods as an important preparatory step to Pattern Recognition, so as to improve the accuracy of image content recognition and identification. Actually, SR mainly deals with the task of deriving a high-resolution image from one or multiple low resolution images of the same scene. The high-resolved image corresponds to a more precise image whose content is enriched with information hidden among the pixels of the original… 
1 Citations

Figures from this paper

Pattern Recognition Applications and Methods
TLDR
This study presents a substantially improved version of FashionDNA that boosts the accuracy of the matching model, and shows that the use of fashion-specific training data leads to superior performance of the segmentation model.

References

SHOWING 1-10 OF 24 REFERENCES
Probing Cytoskeletal Structures by Coupling Optical Superresolution and AFM Techniques for a Correlative Approach
TLDR
The application of some of the most advanced fluorescence superresolution techniques, STED AFM and STORM AFM microscopy towards imaging of cytoskeletal structures, such as microtubule filaments, is described and shown.
Super-resolution image reconstruction: a technical overview
TLDR
The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts to present the technical review of various existing SR methodologies which are often employed.
Image upsampling via imposed edge statistics
TLDR
A new method for upsampling images which is capable of generating sharp edges with reduced input-resolution grid-related artifacts, based on a statistical edge dependency relating certain edge features of two different resolutions, which is generically exhibited by real-world images.
Fundamental limits of reconstruction-based superresolution algorithms under local translation
  • Zhouchen LinH. Shum
  • Mathematics
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2004
TLDR
This paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions, based on the perturbation theory of linear systems.
Example-Based Super-Resolution
TLDR
This work built on another training-based super- resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution that requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data.
Image magnification using level-set reconstruction
  • B. MorseD. Schwartzwald
  • Computer Science
    Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
  • 2001
TLDR
Results show that this technique can produce images whose error properties are equivalent to the initial approximation used, while their contour smoothness is both visually and quantitatively improved.
Pattern recognition methods for thermal drift correction in Atomic Force Microscopy imaging
Atomic Force Microscopy (AFM) is a fundamental tool for the investigation of a wide range of mechanical properties on nanoscale due to the contact interaction between the AFM tip and the sample
Super-resolution through neighbor embedding
  • Hong ChangD. YeungYimin Xiong
  • Computer Science, Mathematics
    Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
  • 2004
TLDR
This paper proposes a novel method for solving single-image super-resolution problems, given a low-resolution image as input, and recovers its high-resolution counterpart using a set of training examples, inspired by recent manifold teaming methods.
STUDY ON THE METHODS OF SUPER-RESOLUTION IMAGE RECONSTRUCTION
With the rapid development of space technology and its related technologies, more and more remote sensing platforms are sent to outer space to survey our earth. Recognizing and positioning all these
A methodological approach for combining super-resolution and pattern-recognition to image identification
TLDR
A method derived from pattern recognition techniques for the recognition of artefacts and noise on set of images combined with super resolution algorithms is defined, opening the possibility to build a general framework for artefact recognition independently by the specific application where it is used.
...
...