nils-herrmann/clean_scirepeval huggingface dataset:
Natural scene-illuminant estimation using the sensor correlation

This paper describes practical algorithms and experimental results concerning illuminant classification. Specifically, we review the sensor correlation algorithm for illuminant classification and we discuss four changes that improve the algorithm's estimation accuracy and broaden its applicability. First, we space the classification illuminants evenly along the reciprocal scale of color temperature, called "mired," rather than the original color-temperature scale. This improves the perceptual uniformity of the illuminant classification set. Second, we calculate correlation values between the image color gamut and the reference illuminant gamut, rather than between the image pixels and the illuminant gamuts. This change makes the algorithm more reliable. Third, we introduce a new image scaling operation to adjust for overall intensity differences between images. Fourth, we develop the three-dimensional classification algorithms using all three-color channels and compare this with the original two algorithms from the viewpoint of accuracy and computational efficiency. The image processing algorithms incorporating these changes are evaluated using a real image database with calibrated scene illuminants.
Illumination-invariant representation for natural color images and its application

Illumination factors such as shading, shadow, and specular highlight, observed from object surfaces in a natural scene, affect seriously the appearance and analysis of the color images. This paper proposes an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. Illumination color is estimated from specular reflection component on inhomogeneous surfaces without using a reference white standard. The overall performance of the proposed representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.
An LED-Based Spectral Imaging System for Surface Reflectance and Normal Estimation

An LED-based spectral imaging system is proposed for estimating surface spectral reflectances and surface normals of art painting. The imaging system is decomposed into a multi-spectral lighting system and a digital camera. The previous lighting system has some essential limitation and inconvenience, which includes low luminance, time consumption, and expensive cost. For the purpose of solving the problems, this paper presents an LED-based spectral lighting system. This is an active illumination system in which spectral light can be controlled by a computer. We note that LED lamps with different peak wavelengths are available recently and the band width of LED light is narrow. The luminance level of emitted light is much higher and the image acquisition is much faster by changing spectral light quickly. First, we construct a flat lighting system using six LEDs with different peak wavelengths to estimate surface spectral reflectances. The surface spectral reflectances of objects are recovered from noisy spectral camera data by the Wiener estimator. Next, we construct a dome lighting system using several sets consisting of seven LEDs, mainly to estimate surface normals of art paintings. The photometric stereo technique is used for estimating the surface normal vector at every pixel point. The feasibility of the proposed method is examined in experiments using a Mac Beth Color Checker chart and an oil painting.
A color classification method for color images using a uniform color space

A color classification method that partitions color image data into a set of uniform color regions is described. The ability to classify spatial regions of the measured image into a small number of uniform regions can be useful for several problems, including image segmentation and image representation. The input image data are first mapped from device coordinates into all approximately uniform perceptual color space. Colors are classified by means of cluster detection in the uniform color space. The classification process is composed of two stages of basic classification and reclassification. The basic classification is based on histogram analysis to detect color clusters sequentially. The principal components of the color data are extracted for effective discrimination of clusters. At the reclassification stage, the extracted representative colors are reclassified on a color distance. Experimental results show that a fundamental set of colors composing an image with shades and shadows is extracted at the basic classification stage and that the objects in the original image are extracted at the reclassification stage.<<ETX>>
Estimation of bispectral matrix for fluorescent objects

A method is proposed for estimating a generalized bispectral matrix for fluorescent objects. The Donaldson matrix is constructed on the wide wavelength range over ultraviolet and visible wavelengths. We use neither an ultraviolet light (black light) nor a monochromator. We present an algorithm for estimating precisely the general shape of luminescent radiance component as a function of excitation wavelength. The method is based on use of two light sources with continuous spectral distributions and a spectrometer. The two spectral radiance factors of reflection and luminescence are estimated from spectral measurements under the known illuminant spectra. The feasibility of the proposed method is examined in experiments in detail. The estimation accuracy is confirmed in comparison with the previous method.
Spectral Image Acquisition and Analysis of Omnidirectional Scene

This paper describes a method for spectral image acquisition and analysis of omnidirectional scene. A multi-band omnidirectional imaging system with high spatial resolution is proposed in a simple system. This system consists of an RGB trichromatic digital camera, a fisheye lens, and two color filters of commercial use. First, we describe the practical spectral imaging system and analyze the spatial resolution of the captured images. Second, algorithms are presented for creating HDR omnidirectional images, estimating illuminant spectra, and chromaticity analysis in omnidirectional scene. Finally, the feasibility of the proposed method is confirmed in experiments on natural scenes.
Metal-dielectric object classification by polarization degree map

Material classification of object surfaces from captured image data is an essential problem in computer vision. The present paper proposes a method for stably classifying the material of an object surface based on the distribution of the degree of polarization (DOP) around specular highlight on the surface. We show that the characteristic of spatial distribution for DOP is remarkably different in metal and dielectric substance. Based on the characteristic, an algorithm is presented for effectively classifying any object surface into two material categories from images captured by a digital camera with rotating a polarization filter. The feasibility of the proposed method is verified on experiments using a variety of object surfaces made of different materials.
Multispectral Analysis of Oil Paintings and Data Compression

The present paper proposes a method for estimating surface reflection properties of an oil painting from the multi-band image data and compressing the data volume in image analysis/synthesis. First we show the improved multi-band imaging system. Next we estimate the surface properties, including surface normal, surface spectral reflectance, and reflection model parameters. Then we analyze statistically the whole data set of the estimated surface-spectral reflectances. The surface spectral reflectances are represented by a linear combination of a small number of basis spectral patterns. Use of the basis patterns leads to reducing the spectral data storage. The quality of this representation is examined based on both color reproduction accuracy and memory capacity. Finally the compressed data are combined for rendering images of the paintings under arbitrary conditions of illumination and viewing
Spectral image processing by a multi-channel camera

Knowledge of such spectral functions as the surface-spectral reflectances of objects in a scene and the illuminant spectral-power distribution is crucial for realizing color constancy, accurate color reproduction, and rendering realistic images. The usual camera system with three channels of RGB has difficulty in estimating the spectral functions because illuminants and surface reflectances in natural scenes are spectrally high-dimensional. A multi-channel camera with more spectral channels than three provides an effective imaging device to estimate the complicated surface and illuminant functions. The present paper proposes a camera system and algorithms for solving this estimation problem from the spectral imaging data. First we present a new multichannel camera system with six spectral channels, which is improved in the dynamic range and image resolution. Then we describe practical algorithms for recovering both the surface spectral reflectances and the illuminant spectrum with accuracy.
Statistical Electromagnetic Absorption and Shield Properties of Inhomogeneous Compound Layers with Lossy Particles and Fibers

Electromagnetic wave properties in plastic and ceramic compounds containing high resistance and high conductance parti­ cles are discussed for electromagnetic shield and absorption by stochastic field theory and experiments. In order to have non-reflection and absorption composite materials, foundamental characteristics of inhomogeneous electromagnetic shield and absorption compound layers are investigated. Quasi-static properties and high frequency properties are shown for optimum design of wide band and thin compound layers by quasi­ static potential consideration and random wave methods. Inhomogeneous lossy plastic compounds of thickness 10mm containing carbon and metal particles and fibers, which have reflection and transmission factors of -17d3 and -26dg are experimen­ tally shown.
Estimation of Two Illuminant Spectral Power Distributions from Highlights of Overlapping Illuminants

A method is proposed for estimating two illuminant spectral power distributions from the highlights of overlapping illuminants on an object. It was assumed in most previous studies that specular highlights on object surfaces are caused by a single light source, or by separate multiple light sources. In this paper, we assume that two highlights from different light sources are overlapped on object surfaces, and estimate the spectral power distributions of both. A multiband camera system is used for capturing spectral images of dielectric objects in a scene. First, we detect specular highlight areas from the spectral image. Then, the illuminant spectra of two light sources are simultaneously estimated based on cluster classification of particular pixel distribution in the highlight area. The feasibility of the proposed method is examined in experiments on real-world scenes.
Parameter estimation of a reflection model from a multi-band image

The present paper proposes a method for estimating various parameters of a reflection model from a single image of an object taken by a multi-band CCD camera. It is assumed that the object's surface is composed of an inhomogeneous dielectric material and has the shape of a cylinder. The Phong model is used as the three-dimensional reflection model, and a six-color camera is used as the multi-band CCD camera. We show some features of the reflection model and the image histogram. The model parameters to be estimated are such parameters as (1) surface-spectral reflectance, (2) illuminant spectral distribution, (3) illumination direction, (4) specular reflection exponent, and (5) ratio of body to interface intensity. The reflection parameters are estimated by analyzing the histogram on the color signal plane and making a relationship between the histogram features and the spatial points. The feasibility of the proposed method is demonstrated in an experiment using a plastic object. Computer graphics images are produced on the estimated model.
Estimation of surface properties of art paintings using a multi-band camera

This paper describes a method for precisely estimating the surface properties of art paintings, including surface normal, surface spectral reflectance, and reflection model parameters. We use a multi-band camera system with six spectral channels. The surface of a painting is observed under different illumination directions by the camera. We use the Torrance-Sparrow model as a reflection model. The surface normal is estimated on the different illumination observations by the photometric stereo. The spectral reflectance is estimated on the multi-band data by the Wiener method. The model parameters are estimated on the statistical distribution of the specular component Finally, all estimates are combined for rendering various appearances of the painting under different illumination and viewing conditions.
Surface reconstruction of oil paintings for digital archiving

This paper proposes a method for reconstructing the surface shape of an oil painting for the purpose of digital archiving of art paintings. The painting surface is regarded as a 3-D rough surface with the dichromatic reflection property. First, imaging systems are shown for image acquisition and analysis. Next, an algorithm is presented for constructing surface height from the surface normal vectors estimated using high-resolution images. Moreover, we use the low-resolution surface height information of direct measurements from a laser scanning meter. The accuracy of constructed surface is then improved by incorporating the low-resolution height information to the high-resolution gradient images. Finally, all the estimates of the surface properties are combined for realistic image rendering. The feasibility of the method is shown in experiments using real oil paintings.
On the sensor-based navigation by changing a direction to follow an encountered obstacle

In the last decade, many sensor-based navigation algorithms have been proposed. In the sensor-based navigation, a robot arrives at its goal globally while avoiding neighbor obstacles locally by sensor information. In every environment, a mobile robot arrives at its goal surely. However if an environment has complicated shape, a mobile robot sometimes joins a loop and consequently runs long until its goal. In general, a loop consists of routes which a robot follows obstacles in the same direction. Nevertheless in most previous algorithms, a mobile robot follows an encountered obstacle in a constant direction. On this observation, a robot is exempted from participation of a loop by reversing a direction to follow an obstacle. The authors discuss algorithms in which the following direction is alternatively changed. This strategy has been adopted by cockroaches living in a natural environment. They then propose algorithms in which the following direction is randomly reversed. Then they compare paths generated by constant, alternative, and random selections in Bug2 and Classl under a graphics simulator for 2D complicated environments.
Point set registration through minimization of the L2 distance between 3D-NDT models

Point set registration-the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three-Dimensional Normal Distributions Transforms. 3D-NDT models - a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3D-NDT representations of the input point sets and then formulates an objective function based on the L2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms.
An adaptive approach for linearization of temperature sensor characteristics

This paper discusses an adaptive approach, based on iterative construction of a piecewise linear approximant, for calibration of industrial sensors. Given the non-linear characteristics of the sensor, the approach consists of two steps: (i) the inverted characteristic is computed in pointwise form on the basis of an iterative Newton-Raphson scheme; (ii) an iterative algorithm is employed in order to construct a continuously improving set of piecewise linear approximations of the computationally inverted sensor characteristic. The main feature of the presented approach is the irregular partitioning of the measurement range, induced from the error minimization procedure performed in (ii). On this basis, the lookup table necessary for calibration of the sensor is constructed in the "cheapest" possible way, allowing on the one hand optimal linear approximation in every particular measurement sub-range and on the other an effective reduction of the necessary storage capacity.
Improved local shape feature stability through dense model tracking

In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability.
Robot assisted gas tomography — Localizing methane leaks in outdoor environments

In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.
Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge: mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in the context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at the same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in a highly dynamic 3D mapping and tracking scenario with centimeter accuracy over a 1.5km trajectory.
Path planning in 3D environments using the Normal Distributions Transform

Planning feasible paths in fully three-dimensional environments is a challenging problem. Application of existing algorithms typically requires the use of limited 3D representations that discard potentially useful information. This article proposes a novel approach to path planning that utilizes a full 3D representation directly: the Three-Dimensional Normal Distributions Transform (3D-NDT). The well known wavefront planner is modified to use 3D-NDT as a basis for map representation and evaluated using both indoor and outdoor data sets. The use of 3D-NDT for path planning is thus demonstrated to be a viable choice with good expressive capabilities.
Sun tracker with sensor — photovoltaic cell

The paper presents a technical solution for one-dimensional (east-west) solar panels managing as two photovoltaic cells are used for the sensor. The photovoltaic cells are placed at an angle of 45 ° to each other on the surface of the panel. If the difference between the measured radiations by the cells is greater than 5%, the corresponding relay is activated and the panel rotates. The proposed solution is simple, its parameters can be easily changed and can be implemented with the Arduino microcontroller. It provides a rapid reaction to increase the effectiveness of the solar panels under fast changing environmental conditions. The implementation and experimental results are also presented.
Beyond points: Evaluating recent 3D scan-matching algorithms

Given that 3D scan matching is such a central part of the perception pipeline for robots, thorough and large-scale investigations of scan matching performance are still surprisingly few. A crucial part of the scientific method is to perform experiments that can be replicated by other researchers in order to compare different results. In light of this fact, this paper presents a thorough comparison of 3D scan registration algorithms using a recently published benchmark protocol which makes use of a publicly available challenging data set that covers a wide range of environments. In particular, we evaluate two types of recent 3D registration algorithms - one local and one global. Both approaches take local surface structure into account, rather than matching individual points. After well over 100 000 individual tests, we conclude that algorithms using the normal distributions transform (NDT) provides accurate results compared to a modern implementation of the iterative closest point (ICP) method, when faced with scan data that has little overlap and weak geometric structure. We also demonstrate that the minimally uncertain maximum consensus (MUMC) algorithm provides accurate results in structured environments without needing an initial guess, and that it provides useful measures to detect whether it has succeeded or not. We also propose two amendments to the experimental protocol, in order to provide more valuable results in future implementations.