Search results for "Convolution"

Retrieving leaf area index from multi-angular airborne data

2009

This work is aimed to demonstrate the feasibility of a methodology for retrieving bio-geophysical variables whilst at the same time fully accounting for additional information on directional anisotropy. A model-based approach has been developed to deconvolve the angular reflectance into single landcovers reflectances, attempting to solve the inconsistencies of 1D models and linear mixture approaches. The model combines the geometric optics of large scale canopy structure with principles of radiative transfer for volume scattering within individual crowns. The reliability of the model approach to retrieve LAI has been demonstrated using data from DAISEX- 99 campaign at Barrax, Spain. Airborn…

What about computational super-resolution in fluorescence Fourier light field microscopy?

2020

Recently, Fourier light field microscopy was proposed to overcome the limitations in conventional light field microscopy by placing a micro-lens array at the aperture stop of the microscope objective instead of the image plane. In this way, a collection of orthographic views from different perspectives are directly captured. When inspecting fluorescent samples, the sensitivity and noise of the sensors are a major concern and large sensor pixels are required to cope with low-light conditions, which implies under-sampling issues. In this context, we analyze the sampling patterns in Fourier light field microscopy to understand to what extent computational super-resolution can be triggered duri…

Fourier-domain lightfield microscopy: a new paradigm in 3D microscopy

2020

Recently, integral (also known as lightfield or plenoptic) imaging concept has been applied successfully to microscopy. The main advantage of lightfield microscopy when compared with conventional 3D imaging techniques is that it offers the possibility of capturing the 3D information of the sample after a single shot. However, integral microscopy is now facing many challenges, like improving the resolution and depth of field of the reconstructed specimens or the development and optimization of specially-adapted reconstruction algorithms. This contribution is devoted to review a new paradigm in lightfield microscopy, namely, the Fourier-domain integral microscope (FiMic), that improves the ca…

Reduction of spherical-aberration impact in microscopy by wavefront coding

2009

In modern high-NA optical scanning instruments, like scanning microscopes, the refractive-index mismatch between the sample and the immersion medium introduces a significant amount of spherical aberration when imaging deep inside the specimen, spreading out the impulse response. Since such aberration depends on the focalization depth, it is not possible to achieve a static global compensation for the whole 3D sample in scanning microscopy. Therefore a depth-variant impulse response is generated. Consequently, the design of pupil elements that increase the tolerance to this aberration is of great interest. In this paper we report a hybrid technique that provides a focal spot that remains alm…

Combinatorial approaches: A new tool to search for highly structured β-hairpin peptides

2002

Here we present a combinatorial approach to evolve a stable β-hairpin fold in a linear peptide. Starting with a de novo -designed linear peptide that shows a β-hairpin structure population of around 30%, we selected four positions to build up a combinatorial library of 20 4 sequences. Deconvolution of the library using circular dichroism reduced such a sequence complexity to 36 defined sequences. Circular dichroism and NMR of these peptides resulted in the identification of two linear 14-aa-long peptides that in plain buffered solutions showed a percentage of β-hairpin structure higher than 70%. Our results show how combinatorial approaches can be used to obtain highly structured peptide s…

Deep 3D Convolution Neural Network for Alzheimer’s Detection

2020

One of the most well-known and complex applications of artificial intelligence (AI) is Alzheimer’s detection, which lies in the field of medical imaging. The complexity in this task lies in the three-dimensional structure of the MRI scan images. In this paper, we propose to use 3D Convolutional Neural Networks (3D-CNN) for Alzheimer’s detection. 3D-CNNs have been a popular choice for this task. The novelty in our paper lies in the fact that we use a deeper 3D-CNN consisting of 10 layers. Also, with effectively training our model consisting of Batch Normalization layers that provide a regularizing effect, we don’t have to use any transfer learning. We also use the simple data augmentation te…

Influence of increasing convolution filters on plaque imaging with multislice CT using an ex-vivo model of Coronary Artery

2005

Capillary electrophoresis enhanced by automatic two-way background correction using cubic smoothing splines and multivariate data analysis applied to…

2005

Mixtures of the surfactant classes coconut diethanolamide, cocamido propyl betaine and alkylbenzene sulfonate were separated by capillary electrophoresis in several media containing organic solvents and anionic solvophobic agents. Good resolution between both the surfactant classes and the homologues within the classes was achieved in a BGE containing 80 mM borate buffer of pH 8.5, 20% n-propanol and 40 mM sodium deoxycholate. Full resolution, assistance in peak assignment to the classes (including the recognition of solutes not belonging to the classes), and improvement of the signal-to-noise ratio was achieved by multivariate data analysis of the time-wavelength electropherograms. Cubic s…

Towards unsupervised analysis of second-order chromatographic data: automated selection of number of components in multivariate curve-resolution meth…

2007

A method to apply multivariate curve-resolution unattendedly is presented. The algorithm is suitable to perform deconvolution of two-way data (e.g. retrieving the individual elution profiles and spectra of co-eluting compounds from signals obtained from a chromatograph equipped with multiple-channel detection: LC-DAD or GC-MS). The method is especially adequate to achieve the advantages of deconvolution approaches when huge amounts of data are present and manual application of multivariate techniques is too time-consuming. The philosophy of the algorithm is to mimic the reactions of an expert user when applying the orthogonal projection approach--multivariate curve-resolution techniques. Ba…

Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images

2021

Abstract Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 arc…