Search Results (5)

Imaging techniques have revolutionized cultural heritage analysis, particularly for objects that cannot be sampled. This study investigated the utilization of spectral imaging for the identification of pigments in artifacts from the Arxiu Valencià del Disseny, in conjunction with other portable spectroscopy techniques such as XRF, Raman, FT-NIR, and FT-MIR. Four early 1930s watercolors were examined using point-wise elemental and molecular spectroscopic data for pigment classification. Initially, the data cubes obtained with the spectral camera were processed using various methods. The spectral behavior was analyzed pixel-point, and the reflectance curves were qualitatively compared with a set of standards. Subsequently, a computational approach was applied to the data cube to produce RGB, false-color infrared (IRFC), and principal component (PC) images. Algorithms, such as the Vector Angle (VA) mapper, were also employed to map the pigment spectra. Consequently, 19th-century pigments such as Prussian blue, chrome yellow, and alizarin red were distinguished according to their composition, combining the spatial and spectral dimensions of the data. Elemental analysis and infrared spectroscopy supported these findings. In this context, the use of reflectance imaging spectroscopy (RIS), despite its technical limitations, emerged as an essential tool for the documentation and conservation of design heritage. Full article

Mercury, as one type of toxic heavy metal, represents a great threat to environmental and biological metabolic systems. Thus, reliable and sensitive quantitative detection of mercury levels is particularly meaningful for environmental protection and human health. We proposed a high-throughput single-particle color imaging strategy under dark-field microscopy (DFM) for mercury ions (Hg²⁺) detection by using individual concave cube Au nanoparticles as optical probes. In the presence of ascorbic acid (AA), Hg²⁺ was reduced to Hg which forms Au–Hg amalgamate with Au nanoparticles, altering their localized surface plasmon resonance (LSPR). Transmission electron microscopy (TEM) images demonstrated that the concave cube Au nanoparticles were approaching to sphere upon increasing the concentration of Hg²⁺. The nanoparticles underwent an obvious color change from red to yellow, green, and finally blue under DFM due to the shape-evolution and LSPR changes. In addition, we demonstrated for the first time that the LSPR of Au–Hg amalgamated below 400 nm. Inspired by the above-mentioned results, single-particle color variations were digitalized by converting the color image into RGB channels to obtain (green+blue)/red intensity ratios [(G+B)/R]. The concentration-dependence change was quantified by statistically analyzing the (G+B)/R ratios of a large number of particles. A linear range from 10 to 2000 nM (R² = 0.972) and a limit of detection (LOD) of 1.857 nM were acquired. Furthermore, many other metal ions, like Cu²⁺, Cr³⁺, etc., did not interfere with Hg²⁺ detection. More importantly, Hg²⁺ content in industrial wastewater samples and in the inner regions of human HepG2 cells was determined, showing great potential for developing a single-particle color imaging sensor in complex biological samples using concave cube Au nanoparticles as optical probes. Full article

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement. Full article

Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification. Full article

A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute of grayscale values, the naive application of the methods for grayscale images to color images often results in unsatisfactory consequences. Conventional hue-preserving color image enhancement methods utilize histogram equalization (HE) for enhancing the contrast. However, they cannot always enhance the saturation simultaneously. In this paper, we propose a histogram specification (HS) method for enhancing the saturation in hue-preserving color image enhancement. The proposed method computes the target histogram for HS on the basis of the geometry of RGB (rad, green and blue) color space, whose shape is a cube with a unit side length. Therefore, the proposed method includes no parameters to be set by users. Experimental results show that the proposed method achieves higher color saturation than recent parameter-free methods for hue-preserving color image enhancement. As a result, the proposed method can be used for an alternative method of HE in hue-preserving color image enhancement. Full article