Local Color Differences Research Articles

Tungsten mineralization in the Huangjindong gold deposit, southern China: Insights from scheelite texture, in-situ trace elements and Sr isotope compositions

Scheelite and wolframite are two primary tungsten minerals in nature. The occurrence of tungsten minerals varies in different types of deposits, with scheelite rather than wolframite typically forming in orogenic gold deposits. However, the explanation for this phenomenon remains unclear. Huangjindong deposit, located in the center of the Jiangnan Orogenic Belt in southern China, is a typical orogenic gold deposit with scheelite mineralization. Texture, in-situ trace elements and Sr isotope compositions of scheelite from this deposit were investigated to explore the formation process of scheelite and the source of tungsten of similar orogenic gold deposits.Three hydrothermal stages have been identified at this deposit: milky scheelite + quartz (pre-gold Stage I); gold + sericite + sulfide + quartz (syn-gold Stage II); and dolomite + quartz (post-gold Stage III). Scheelite primarily occurs during the initial pre-gold stage. Local color differences in CL image have developed along rims and in domains adjacent to fractures of scheelite due to slight late-stage hydrothermal alteration of scheelite along their fractures. The intense development of sericite and chlorite alteration in the host rocks indicates that the water–rock reaction consumed a large amount of H+. Within the scheelite-quartz veins, the presence of brecciated slate enclaves and localized quartz geodes, indicating that the fluid experienced significant pressure drop. This indicates that the water–rock reaction and the decrease in pressure resulted in the precipitation of scheelite. The dominance of tungsten mineral precipitation due to pressure reduction serves as the main factor driving the development of scheelite over wolframite within the deposit, which also elucidates the reason that tungsten mineralization associated with orogenic gold deposits predominantly features scheelite. The characteristics of scheelite without oscillatory zoning, high Sr (1180–8600 ppm) and low Mo (bdl-0.18 ppm) concentrations, and flat REE pattern are similar to those of scheelite in orogenic gold deposits worldwide. The high 87Sr/86Sr ratio (0.7539–0.7663) indicates that the tungsten-bearing ore-forming fluids derived from deep-seated metamorphic fluids.

Measuring Perceptual Color Differences of Smartphone Photographs

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this paper, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop®, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.

Regional importance detection of 3D mesh via fusion of local color difference and curvature entropy

Predicting relative visual salient regions on three-dimensional (3D) meshes benefits many computer graphics applications. Most computation models for mesh saliency focus on geometrical information alone. Nevertheless, the ignored texture, lighting and material also provide more detailed appearance information, especially in the context of static scene rendering. In this paper, we propose a mesh saliency detection algorithm considering both geometrical and colorimetric information to address this challenge. Our model first computes the local curvature entropy at multi-scale to capture the geometrical details. Second, a set of images are projected onto the screen at several viewpoints with specified material and lighting model. Potentially salient regions on the rendered images are detected by fusion of multiple color difference maps measured with an approximated multi-scale Laplacian of Gaussian filter. A Gaussian distribution-based central bias model is applied to the image saliency map to emphasize the global rarity of salient regions. Third, the saliency maps of rendered images are projected back to the 3D mesh via the ray casting method. In the end, both saliency maps are combined linearly as the saliency map of 3D mesh. Experiment on the human fixation database demonstrates the performance of our method compared to the classic methods in terms of linear correlation coefficient and AUC.

A novel point cloud simplification method with integration of multiple-feature fusion and density uniformity

In recent years, 3D point cloud technology has played an increasingly important role in the field of casting parts detection. Due to the huge amount of high-precision point cloud data, simplification is often necessary before 3D reconstruction to improve computational efficiency. However, most of the existing point cloud simplification algorithms often blur edge details and produce holes in the final 3D reconstruction. To avoid these problems, a novel point cloud simplification method is proposed here. To accurately describe the geometric and texture features, a multi-featured fusion method was first developed to integrate the curvature, local projection distance, normal vector and local color differences. As this approach uses multiple types of geometric features, it accurately distinguishes sharp and smooth edges. Moreover, due to the use of local color difference, the texture features of the point cloud are recognizable. Then, to evaluate the density of the non-uniform point cloud, a globular neighborhood search strategy was used to estimate the density of the point cloud itself. Since the number of points in the spherical neighborhood reflected the sparsity of the point cloud, this method accurately represented the density of a point cloud with an uneven distribution. Finally, the feature fusion and density uniformity were integrated, allowing the use of these preserved points to retain more edge details of the point cloud in the final 3D reconstruction. This approach also avoided holes in the final 3D reconstruction. On the point cloud data of Anchor, Fantisk and Humana, the root mean square error of the proposed method reduces 40%, 32% and 19% than the best algorithm among the K-means clustering, Grid average, and Graph based method, respectively.

Multiple channels local binary pattern for color texture representation and classification

Image texture description and analysis technology are the basis of many practical applications in pattern recognition. This paper presents a novel and simple, yet powerful method, namely multiple channels local binary pattern (MCLBP), which is the natural extension and development of local binary pattern (LBP) algorithm for color texture representation and classification. MCLBP combines single-channel texture characteristics with multi-channel color information, which reflects the correlations and dependency among different channels. Furthermore, we decompose local color differences into color-difference signs and color-difference magnitudes and MCLBP is extended to MCLBP+M. Then, the resulted image descriptor is a histogram representation, which fuses rich features including color difference sign and color difference magnitude. Comprehensive experiments conducted on five benchmark databases, including Outex, KTH-TIPS, CUReT, STex and KTH-TIPS2-b clearly demonstrate that our proposed method outperforms most of the existed color texture features in terms of classification accuracy. Particularly, our method achieves the best classification performance in CUReT and STex databases.

Specular Reflection Image Enhancement Based on a Dark Channel Prior

In this paper, we propose a specular highlight image enhancement algorithm based on a dark channel prior to solve the problem of information loss in specular highlight images in real scenes. First, the algorithm is based on the dark channel prior algorithm, and a moving window minimum filter is used to estimate the global illumination component. Then, a weighted function based on the local pixel color difference is introduced to solve the halo artifacts in the image. Then, the improved guided filter algorithm is used to optimize the transmittance, which improves the computational efficiency of the algorithm. Finally, the brightness of the image is adjusted by the CLAHE algorithm to enhance the local details of the image. Experimental results show that this method can effectively enhance the information in specular highlight images, and its processing results are obviously better than those of other algorithms.

WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding.

Wireless capsule endoscopy (WCE) has become an effective facility to detect digestive tract diseases. To further improve the accuracy and efficiency of computer-aided diagnosis system in the detection of intestine polyps, a novel algorithm is proposed for WCE polyp detection in this paper. First, by considering the rich color information of endoscopic images, a novel local color texture feature called histogram of local color difference (LCDH) is proposed for describing endoscopic images. A codebook acquisition method which is based upon positive samples is also proposed, generating more balanced visual words with the LCDH features. Furthermore, based on locality-constrained linear coding (LLC) algorithm, a normalized variance regular term is introduced as NVLLC algorithm, which considers the dispersion degree between k nearest visual words and features in the approximate coding phase. The final image representations are obtained from using the spatial matching pyramid model. Finally, the support vector machine is employed to classify the polyp images. The WCE dataset including 500 polyp and 500 normal images is adopted for evaluating the proposed method. Experimental results indicate that the classification accuracy, sensitivity and specificity have reached 96.00%, 95.80% and 96.20%, which performances better than traditional ways. A novel method for WCE polyp detection is developed using LCDH feature descriptor and NVLLC coding scheme, which achieves a promising performance and can be implemented in clinical-assisted diagnosis of intestinal diseases.

Spatially weighted order binary pattern for color texture classification

In this paper, we propose a novel descriptor called spatially weighted order binary pattern (SWOBP) for color texture classification. The SWOBP descriptor not only encodes color order information in different channels but also encodes color order relationships in the spatial domain. To achieve these goals, we introduce a color gradient channel to complement the traditional color channels and explore a multi-channel color order pattern to jointly encode inter-channel features. Furthermore, we decompose local color differences into spatially weighted binary templates and use them to encode color order information in a local neighborhood. Finally, we aggregate all the encoded features into image histograms as texture descriptor. Experiments on five benchmark databases demonstrate that the proposed SWOBP descriptor achieves the state-of-the-art performance for color texture classification.

Matting-Based Residual Optimization for Structurally Consistent Image Color Correction

Image color correction aims to eliminate color differences between images, especially for color consistency in panoramic or stereoscopic images. Nowadays, the global color correction methods cannot correct local color differences, while the local color correction methods usually lead to structural inconsistency between local regions and image clarity reduction. To address these problems, we propose a matting-based residual optimization for structurally consistent image color correction (MROC). Inspired by the residual image, we formulate the image color correction problem as the optimization of a residual image between the input target image and the resulting image. The residual image is initialized and improved by the soft matting method with a closed-form solution. Besides, a data term is introduced to identify those pixels with higher color and structural consistencies and preserve these pixels during optimization. The whole computational infrastructure operates at the pixel level to correct local color differences while maintaining image clarity. Experimental results demonstrate that the performance of the proposed MROC method is superior to the state-of-the-art image color correction methods. Furthermore, the proposed matting-based residual optimization can also be incorporated in a variety of color correction methods, with enhanced outcomes justified by a group of image quality assessment metrics.

Image descriptor based on local color directional quaternionic pattern

Most of the existing local binary pattern methods discard local color differences by holding their binary information. And local spatial information is also neglected. To address these problems, a robust color image descriptor local color directional quaternionic pattern (LCDQP) is proposed. In the descriptor, the color distance map (CDM) is generated in the RGB color space to capture the color distribution among three channels. According to the distribution of CDM elements and their neighbors, four edge models are defined to describe the change trend of the original image. Then, based on this, the LCDQP strings are gained according to the distribution of four models in the four directions of 0 deg, 45 deg, 90 deg, and 135 deg. Finally, an effective quaternionic code method is adopted to construct the LCDQP descriptor. The proposed descriptor not only captures the local color features but also reflects the spatial structure information. Experiments on four representative databases demonstrate that the proposed descriptor is superior to other state-of-the-art approaches.

Saliency Detection Based on Multiscale Extrema of Local Perceptual Color Differences.

Visual saliency detection is a useful technique for predicting, which regions humans will tend to gaze upon in any given image. Over the last several decades, numerous algorithms for automatic saliency detection have been proposed and shown to work well on both synthetic and natural images. However, two key challenges remain largely unaddressed: 1) How to improve the relatively low predictive performance for images that contain large objects and 2) how to perform saliency detection on a wider variety of images from various categories without training. In this paper, we propose a new saliency detection algorithm that addresses these challenges. Our model first detects potentially salient regions based on multiscale extrema of local perceived color differences measured in the CIELAB color space. These extrema are highly effective for estimating the locations, sizes, and saliency levels of candidate regions. The local saliency candidates are further refined via two global extrema-based features, and then a Gaussian mixture is used to generate the final saliency map. Experimental validation on the extensive CAT2000 data set demonstrates that our proposed method either outperforms or is highly competitive with prior approaches, and can perform well across different categories and object sizes, while remaining training-free.

The perception of three-dimensional contours and the effect of luminance polarity and color change on their detection.

In the present study we investigated the detectability of three-dimensional (3D) cocircular contours defined by binocular disparity and established the influence of a number of stimulus factors to their perception. In Experiment 1 we examined the depth range over which local elements are grouped in depth, and whether contour detectability systematically changed with the degree to which they are oriented in depth. We found that increasing the orientation of curved contours in depth improved detection performance. In Experiment 2, we examined the degree to which contour detection was disrupted by varying their continuity in depth by jittering the local depth position of contour elements. Detection performance declined with the increasing displacement of local contour elements in depth away from the depth orientation of the contour. Experiments 3 and 4 ascertained whether a detection advantage is afforded to 3D contours defined by local variations in luminance polarity and color. Local color and polarity differences can disrupt the two-dimensional grouping of local contour elements on the basis of similarity, but we tested whether continuity in depth facilitates grouping of contour elements differing in polarity and color. We found no detection advantage for 3D contours defined by local color and polarity variations, suggesting binocular disparity does not facilitate grouping in depth when local elements differ in color and polarity. These findings further suggest the visual system uses binocular disparity to detect contours, but is likely to involve systems tuned to luminance polarity and color.

지역 색차 기반의 히스토그램 정교화에 의한 영상 검색

Since digital images and videos are rapidly increasing in the internet with the spread of mobile computers and smartphones, research on image retrieval has gained tremendous momentum. Color, shape, and texture are major features used in image retrieval. Especially, color information has been widely used in image retrieval, because it is robust in translation, rotation, and a small change of camera view. This paper proposes a new method for histogram refinement based on local color difference. Firstly, the proposed method converts a RGB color image into a HSV color image. Secondly, it reduces the size of color space from 256 to 32. It classifies pixels in the 32-color image into three groups according to the color difference between a central pixel and its neighbors in a 3x3 local region. Finally, it makes a color difference vector(CDV) representing three refined color histograms, then image retrieval is performed by the CDV matching. The experimental results using public image database show that the proposed method has higher retrieval accuracy than other conventional ones. They also show that the proposed method can be effectively applied to search low resolution images such as thumbnail images.

Variational Approach for the Fusion of Exposure Bracketed Pairs

When taking pictures of a dark scene with artificial lighting, ambient light is not sufficient for most cameras to obtain both accurate color and detail information. The exposure bracketing feature usually available in many camera models enables the user to obtain a series of pictures taken in rapid succession with different exposure times; the implicit idea is that the user picks the best image from this set. But in many cases, none of these images is good enough; in general, good brightness and color information are retained from longer-exposure settings, whereas sharp details are obtained from shorter ones. In this paper, we propose a variational method for automatically combining an exposure-bracketed pair of images within a single picture that reflects the desired properties of each one. We introduce an energy functional consisting of two terms, one measuring the difference in edge information with the short-exposure image and the other measuring the local color difference with a warped version of the long-exposure image. This method is able to handle camera and subject motion as well as noise, and the results compare favorably with the state of the art.