Search results for "multispectral image"

Multispectral and autofluorescence RGB imaging for skin cancer diagnostics

2019

This paper presents the results of statistical clinical data, combining two diagnostic methods. A combination of two skin imaging methods – diffuse reflectance and autofluorescence – has been applied for skin cancer diagnostics. Autofluorescence (AF) and multispectral diffuse reflectance images were acquired by custom made prototype with 405 nm, 526 nm, 663 nm and 964 nm LEDs and RGB CMOS camera. Parameter p’ was calculated from diffuse reflectance images under green, red and infrared illumination, AF intensity (I’) was calculated from AF images exited at 405nm wavelength. Obtained results show that criterion p` > 1 gives possibility to discriminate melanomas and different kind of keratosis…

Inflight Radiometric Calibration of New Horizons' Multispectral Visible Imaging Camera (MVIC)

2017

© 2016 Elsevier Inc. We discuss two semi-independent calibration techniques used to determine the inflight radiometric calibration for the New Horizons’ Multi-spectral Visible Imaging Camera (MVIC). The first calibration technique compares the measured number of counts (DN) observed from a number of well calibrated stars to those predicted using the component-level calibration. The ratio of these values provides a multiplicative factor that allows a conversation between the preflight calibration to the more accurate inflight one, for each detector. The second calibration technique is a channel-wise relative radiometric calibration for MVIC's blue, near-infrared and methane color channels us…

Compressive single-pixel multispectral Stokes polarimeter

2014

We present a single-pixel system that performs polarimetric multispectral imaging with the aid of compressive sensing techniques. We experimentally obtain the full Stokes spatial distribution of a scene for different spectral channels.

Feature selection with Ant Colony Optimization and its applications for pattern recognition in space imagery

2016

This paper presents a feature selection (FS) algorithm using Ant Colony Optimization (ACO). It is inspired by the particular behavior of real ants, namely by the fact that they are capable of finding the shortest path between a food source and the nest. There are considered two ACO-FS model applications for pattern recognition in remote sensing imagery: ACO Band Selection (ACO-BS) and ACO Training Label Purification (ACO-TLP). The ACO-BS reduces dimensionality of an input multispectral image data by selecting the “best” subset of bands to accomplish the classification task. The ACO-TLP selects the most informative training samples from a given set of labeled vectors in order to optimize the…

Design of a configurable multispectral imaging system based on an AOTF.

2011

In this paper, we present a configurable multispectral imaging system based on an acousto-optic tunable filter (AOTF). Typically, AOTFs are used to filter a single wavelength at a time, but thanks to the use of a versatile sweeping frequency generator implemented with a direct digital synthesizer, the imager may capture a configurable spectral range. Experimental results show a good spectral and imaging response of the system for spectral bandwidth up to a 50 nm.

Multispectral, Fluorescent and Photoplethysmographic Imaging for Remote Skin Assessment

2017

Optical tissue imaging has several advantages over the routine clinical imaging methods, including non-invasiveness (does not change the structure of tissues), remote operation (avoids infection) and ability to quantify the tissue condition by means of specific image parameters. Dermatologists and other skin experts need compact (preferably pocket-size), self-sustained and easy-to-use imaging devices. The operational principles and designs of ten portable in-vivo skin imaging prototypes developed at the Biophotonics Laboratory of Institute of Atomic Physics and Spectroscopy, University of Latvia during the recent five years are presented in this paper. Four groups of imaging devices are con…

2020

This work introduces a method to estimate reflectance, shading, and specularity from a single image. Reflectance, shading, and specularity are intrinsic images derived from the dichromatic model. Estimation of these intrinsic images has many applications in computer vision such as shape recovery, specularity removal, segmentation, or classification. The proposed method allows for recovering the dichromatic model parameters thanks to two independent quadratic programming steps. Compared to the state of the art in this domain, our approach has the advantage to address a complex inverse problem into two parallelizable optimization steps that are easy to solve and do not require learning. The p…

Melanoma-nevus differentiation by multispectral imaging

2011

A clinical trial on multi-spectral imaging of malignant and non-malignant skin pathologies comprising 22 melanomas and 59 pigmented nevi was performed in Latvian Oncology Center. Analysis of data obtained in the spectral range 450–950 nm using multispectral camera have led to a novel image processing algorithm capable to distinguish melanoma from pigmented nevi and different areas of activity of melanoma. The proposed methodology and potential clinical applications are discussed.

Kernel Anomalous Change Detection for Remote Sensing Imagery

2020

Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors. In particular, we focus on algorithms that utilize Gaussian and elliptically contoured (EC) distribution and extend them to their nonlinear counterparts based on the theory of reproducing kernels' Hilbert space. We illustrate the performance of the kernel methods introduced in both pervasive and ACD problems with real and simulated changes in multispectral and hyperspectral imagery w…

Multispectral images-based background subtraction using Codebook and deep learning approaches

2020

This dissertation aims to investigate the multispectral images in moving objects detection via background subtraction, both with classical and deep learning-based methods. As an efficient and representative classical algorithm for background subtraction, the traditional Codebook has first been extended to multispectral case. In order to make the algorithm reliable and robust, a self-adaptive mechanism to select optimal parameters has then been proposed. In this frame, new criteria in the matching process are employed and new techniques to build the background model are designed, including box-based Codebook, dynamic Codebook and fusion strategy. The last attempt is to investigate the potent…