Image Color Research Articles

No browsing, no donating: impact of image emotion and colour on browsing intention in online charitable fundraising

ABSTRACT Browsing behaviour, as a pivotal step before donation behaviour, plays a crucial role in online charitable fundraising, with little attention focused on attracting potential donors’ attention and improving their browsing intention. This research focuses on the colour elements of tone and saturation, as well as the victim’s emotion, to investigate the impact of their interaction on donors’ browsing and donation behaviour. The results of two studies indicate that presenting a victim image with happy emotion in low saturation, blue (vs. yellow) tones can enhance the individual’s attention to the victim’s image and further improve their browsing intention. However, when presenting images in high saturation, the colour tone and the emotion of the image have no significant influence on browsing intention. Besides, in low saturation, blue (vs. yellow) tones can enhance the donor’s donation rate and amount. These findings could help scholars and non-profit organisations understand how to present colour elements and victim emotions in the fundraising materials to stimulate the browsing and donation behaviours of donors.

An in-situ image enhancement method for the detection of marine organisms by remotely operated vehicles

Abstract With the assistance of the visual system, remote operated vehicles (ROVs) can replace frogmen to achieve safer and more efficient capturing of marine organisms. However, the selective absorption and scattering of light lead to a decrease in the visual quality of underwater images, which hinders ROV operators from observing the operating environment. Unfortunately, most image enhancement methods only focus on image color correction rather than perceptual enhancement, which in turn prevents the object detector from quickly locating the target. Therefore, a visual-enhanced and detection-friendly underwater image enhancement method is needed. In this paper, an underwater image enhancement method called in-situ enhancement is proposed to improve the semantic information of the visual hierarchy based on current scene information in multiple stages. Mapping the underwater image to its dual space allows the enhancement equation to be applied to severely degraded underwater scenes. Moreover, it is also a detection-friendly method and has good generalization in both visual quality improvement and object detection. The experimental results show that in different underwater datasets, the in-situ enhancement effectively improves the visual quality of underwater images, and its enhanced results train different object detectors with high detection accuracy.

Genesis of the Nuri Cu‐W‐Mo Deposit, Tibet, China: Constraints from in situ Trace Elements and Sr Isotopic Analysis of Scheelite

AbstractThe Nuri deposit is the only Cu‐W‐Mo polymetallic deposit with large‐scale WO3 resources in the eastern section of the Gangdese metallogenic belt, Tibet, China. However, the genetic type of this deposit has been controversial since its discovery. Based on a study of the geological characteristics of the deposit, this study presents mineralization stages, focusing on the oxide stage and the quartz‐sulfide stage where scheelite is mainly formed, referred to as Sch‐A and Sch‐B, respectively. Through LA‐ICP‐MS trace element and Sr isotope analyses, the origin, evolutionary process of the ore‐forming fluid and genesis of the ore deposit are investigated. Scanning Electron Microscope‐Cathodoluminescence (SEM‐CL) observations reveal that Sch‐A consists of three generations, with dark gray homogenous Sch‐A1 being replaced by relatively lighter and homogeneous Sch‐A2 and Sch‐A3, with Sch‐A2 displaying a gray CL image color with vague and uneven growth bands and Sch‐A3 has a light gray CL image color with hardly any growth band. In contrast, Sch‐B exhibits a ‘core‐rim’ structure, with the core part (Sch‐B1) being dark gray and displaying a uniform growth band, while the rim part (Sch‐B2) is light gray and homogeneous. The normalized distribution pattern of rare earth elements in scheelite and Sr isotope data suggest that the early ore‐forming fluid in the Nuri deposit originated from granodiorite porphyry and, later on, some country rock material was mixed in, due to strong water‐rock interaction. Combining the O‐H isotope data further indicates that the ore‐forming fluid in the Nuri deposit originated from magmatic‐hydrothermal sources, with contributions from metamorphic water caused by water‐rock interaction during the mineralization process, as well as later meteoric water. The intense water‐rock interaction likely played a crucial role in the precipitation of scheelite, leading to varying Eu anomalies in different generations of scheelite from the oxide stage to the quartz‐sulfide stage, while also causing a gradual decrease in oxygen fugacity (fO2) and a slow rise in pH value. Additionally, the high Mo and low Sr contents in the scheelite are consistent with typical characteristics of magmatic‐hydrothermal scheelite. Therefore, considering the geological features of the deposit, the geochemical characteristics of scheelite and the O‐H isotope data published previously, it can be concluded that the genesis of the Nuri deposit belongs to porphyry‐skarn deposit.

DRC: Chromatic aberration intensity priors for underwater image enhancement

Underwater imaging technology is a crucial tool for monitoring marine flora and fauna. However, selective light absorption and scattering properties of water make underwater imagery frequently appear blurred and exhibit color biases, hindering the extraction of vital aquacultural insights. To address this challenge, we propose a method, namely DRC, which is a holistic approach to enhancing underwater image clarity and color fidelity. This method comprises three integral components: D-procedure, R-procedure, and C-procedure. The D-procedure intricately accounts for the trichromatic underwater attenuation dynamics, formulating a chromatic aberration intensity prior. This prior counters the disparities in degradation levels seen in conventional single-channel prior depth estimations, achieving a dynamic depth representation approaching binocular image precision. The R-procedure, utilizing an adaptive dark-pixel prior, pinpoints corresponding points across varied depth zones to counteract backscattering, thereby mitigating the image’s hazy appearance. The C-procedure bolsters image luminance and color fidelity through opponent channel rectification and amalgamates pronounced image contrasts and intricate details via Gaussian pyramid fusion. The method was tested on several publicly available datasets and compared with nine popular underwater image enhancement techniques. Both subjective and objective assessments underscore the superiority of our DRC method over existing underwater image enhancement techniques.

Image color rendering based on frequency channel attention GAN

Image color rendering based on frequency channel attention GAN

Durian Species Classification Using Deep Learning Method

Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy.

A Mobile Image Aesthetics Processing System with Intelligent Scene Perception

Image aesthetics processing (IAP) is used primarily to enhance the aesthetic quality of images. However, IAP faces several issues, including its failure to analyze the influence of visual scene information and the difficulty of deploying IAP capabilities to mobile devices. This study proposes an automatic IAP system (IAPS) for mobile devices that integrates machine learning and traditional image-processing methods. First, we employ an extremely computation-efficient deep learning model, ShuffleNet, designed for mobile devices as our scene recognition model. Then, to enable computational inferencing on resource-constrained edge devices, we use a modern mobile machine-learning library, TensorFlow Lite, to convert the model type to TFLite format. Subsequently, we adjust the image contrast and color saturation using group filtering, respectively. These methods enable us to achieve maximal aesthetic enhancement of images with minimal parameter adjustments. Finally, we use the InceptionResNet-v2 aesthetic evaluation model to rate the images. Even when employing the benchmark model with an accuracy of 70%, the score of the IAPS processing image is verified to be higher and more effective compared with a state-of-the-art smartphone’s beautification function. Additionally, an anonymous questionnaire survey with 100 participants is conducted, and the result shows that IAPS enhances the aesthetic appeal of images based on the public’s preferences.

Power equipment image enhancement processing based on YOLO-v8 target detection model under MSRCR algorithm

Abstract With the rapid development of the power industry, higher requirements have been put forward for real-time monitoring and fault identification of power equipment. However, images of power equipment in actual scenes are often affected by problems such as uneven illumination and color distortion, leading to a decrease in the performance of the target detection model. Hence, this paper suggests merging the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm with the YOLO-v8 target detection model to enhance the visual quality of power equipment images and boost the accuracy and efficiency of target detection. Initially, the MSRCR algorithm enhances image brightness, contrast, and color restoration and preserves edge and detail features. Subsequently, the paper explores the architecture of YOLO-v8, incorporating the SE (Squeeze-and-Excitation) attention mechanism. This mechanism dynamically adjusts channel weights to optimize feature processing in input data. The final experimental results show that using the MSRCR algorithm to enhance the data and combining it with the SE attention mechanism have improved by about 3.2% compared to the original YOLO-v8 model. In comparative experiments with other algorithms, the method proposed in this article achieved an accuracy of 94.3% and a recall rate of 92.6%, which are both higher than other models. By enhancing power equipment images with the MSRCR algorithm, the YOLO-v8 model has significantly improved both target detection accuracy and recall rate. In summary, the MSRCR power equipment image enhancement processing method proposed in this article based on the YOLO-v8 target detection model can effectively improve the visual quality of power equipment images and improve the accuracy and efficiency of target detection.

Generation of Mandelbrot and Julia sets for generalized rational maps using SP-iteration process equipped with [formula omitted]-convexity

In this paper, we introduce a generalized rational map to develop a theory of escape criterion via the SP-iteration process equipped with s-convexity. Furthermore, we develop algorithms for the exploration of unique kinds of Mandelbrot as well as Julia sets. We demonstrate graphically the change in colour, size, and shape of images with the change in values of the considered iteration’s parameters. The new fractals thus obtained are visually very pleasing and attractive. Most of these newly generated fractals resemble natural objects around us. Moreover, we numerically study the dependence between the iteration’s parameters and the set size. The experiments show that this dependency is non-linear. We believe that the obtained conclusions will motivate researchers who are interested in fractal geometry.

Application of virtual reality technology in second language classroom teaching in colleges and universities

The design and implementation of teachers’ classroom teaching strategies is the key to the success of second language classroom teaching. In order to improve the quality of second-language classroom teaching in universities and enhance the interactivity in the teaching process, the application of virtual reality technology in second-language classroom teaching in universities is studied. Firstly, an integrated ware platform is designed for second language classroom teaching in universities, which consists of an integrated ware library and a database. Then virtual reality technology is used to design an integrated ware library within the platform and collect information such as various teaching media resources designed in the classroom teaching content into the integrated ware library; Utilize 3DS Max software in virtual reality technology to construct three-dimensional models of teaching scenes and entities; Choose to use linear difference method to render 3D model colors; From a visual perspective, enhance the realism of the rendered model through image enhancement technology and color contrast enhancement technology. According to the functions of the physical object, various interactive events are added to the created teaching scene and the three-dimensional model of the entity and stored in the integrable ware library to achieve panoramic roaming of the second language classroom teaching scene in universities. The experimental results show that the teaching platform designed by this method can accurately construct three-dimensional models of teaching scenes and objects with good visual effects, providing users with a more realistic sensory experience and effectively improving students’ mastery of teaching content.

Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels

Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson’s phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R2 of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R2, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species.

A arte da fotografia na China: algumas observações sobre sua conotação estética

Abstract: Photography, as a tool, mainly presents the relationship between people and objects through the use of artistic elements, such as light, shadow and color in images. The creation of photography is an activity that requires inspiration and skills. It requires photographers to carefully and deeply observe and think about the overall structure of the work, in order to take good photos that are both artistic and thoughtful. The development and popularization of photography in today’s society cannot be underestimated for their impact on public aesthetics and humanistic thinking. This paper aims to analyze the aesthetic connotation of Chinese photography and explore its aesthetic standards and values. The philosophical thoughts in Chinese photography are explored, and their reflections and perspectives on life and society are analyzed. The relationship and mutual influence between aesthetic connotations and philosophical thoughts in Chinese photography are explored, and their significance and influence on photography works are analyzed, so as to better understand and appreciate the uniqueness of Chinese photography. Chinese photography is a unique and diverse form of artistic expression that reflects China’s rich cultural heritage and social development. By emphasizing harmony, the integration of painting elements, storytelling and appreciation of nature, Chinese photography provides a profound aesthetic experience and conveys deeper meanings of unity, tranquility, and the relationship between people and the environment.

Development of a smartphone enabled, paper-based quantitative diagnostic assay using the HueDx color correction system.

Color correction is an important methodology where a digital image's colors undergo a transformation to more accurately represent their appearance using a predefined set of illumination conditions. Colorimetric measurements in diagnostics are sensitive to very small changes in colors and therefore require consistent, reproducible illumination conditions to produce accurate results, making color correction a necessity. This paper presents an image color correction pipeline developed by HueDx, Inc., using transfer algorithms that improve upon existing methodologies and demonstrates real-world applications of this pipeline in colorimetric clinical chemistry using a smartphone enabled, paper-based total protein diagnostic assay. Our pipeline is able to compensate for a variety of illumination conditions to provide consistent imaging for quantitative colorimetric measurements using white-balancing, multivariate gaussian distributions and histogram regression via dynamic, non-linear interpolating lookup tables. We empirically demonstrate that each point in the color correction pipeline provides a theoretical basis for achieving consistent and precise color correction. To show this, we measure color difference with deltaE (ΔE00), alongside quantifying performance of the HueDx color correction system, including the phone hardware, color sticker manufacturing quality and software correction capabilities. The results show that the HueDx color correction system is capable of restoring images to near-imperceptible levels of difference independent of their original illumination conditions including brightness and color temperature. Comparisons drawn from the paper-based total protein assay calibrated and quantified with and without using the HueDx color correction pipeline show that the coefficient of variation in precision testing is almost twice as high without color-correcting. Limits of blank, detection and quantitation were also higher without color-correction. Overall, we were able to demonstrate the HueDx platform improves reading and outcome of the total protein diagnostic assay and is useful for the development of smartphone-based quantitative colorimetric diagnostic assays for point-of-care testing.

A Study of Folk Culture in Francophone Central Africa Incorporating Deep Learning

Abstract Through the theoretical analysis of folk culture in French-speaking countries in Central Africa, six-dimensional features of folk culture in non-French-speaking countries are summarized, and the folk culture database of French-speaking countries in Central Africa is constructed based on a web crawler. Fusing scene region features with image color and texture features, an Extreme Learning Machine algorithm carries out the classification to realize the construction of a folk culture classification model based on deep learning, and the cross-entropy loss function and Adam optimizer are used respectively to optimize the above-constructed model. The relevant parameters, training set, and test set are determined, and an example analysis of non-Chinese folk culture from the perspective of deep learning is carried out. It is found that the accuracy of non-folk culture classification of the model after Adam and cross-entropy optimization is significantly higher than that of the CNN model, with an average of 0.03~0.04, indicating that after optimization, the model can more comprehensively reflect non-French-speaking countries’ folk culture information and features in images. In addition, there are fewer symbols of non-French-speaking folk culture in the image modality, such as religious beliefs (17, 4.57%) and music and dance (32, 8.60%), which is because it is difficult to represent this type of folk culture with images. This paper fully illustrates the prospects for the application of deep learning models in the study of folk culture in French-speaking Central Africa, which can contribute to the development of digitization and informatization of folk culture in French-speaking Central Africa.

AnlightenDiff: Anchoring Diffusion Probabilistic Model on Low Light Image Enhancement.

Low-light image enhancement aims to improve the visual quality of images captured under poor illumination. However, enhancing low-light images often introduces image artifacts, color bias, and low SNR. In this work, we propose AnlightenDiff, an anchoring diffusion model for low light image enhancement. Diffusion models can enhance the low light image to well-exposed image by iterative refinement, but require anchoring to ensure that enhanced results remain faithful to the input. We propose a Dynamical Regulated Diffusion Anchoring mechanism and Sampler to anchor the enhancement process. We also propose a Diffusion Feature Perceptual Loss tailored for diffusion based model to utilize different loss functions in image domain. AnlightenDiff demonstrates the effect of diffusion models for low-light enhancement and achieving high perceptual quality results. Our techniques show a promising future direction for applying diffusion models to image enhancement.

Multi-Modal Fusion Based on Depth Adaptive Mechanism for 3D Object Detection

Lidars and cameras are critical sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, accurate and robust fusion methods are still under exploration due to non-homogenous representations. In this paper, we find that the complementary roles of point clouds and images vary with depth. An important reason is that the point cloud appearance changes significantly with increasing distance from the Lidar, while the image's edge, color, and texture information are not sensitive to depth. To address this, we propose a fusion module based on the Depth Attention Mechanism (DAM), which mainly consists of two operations: gated feature generation and point cloud division. The former adaptively learns the importance of bimodal features without additional annotations, while the latter divides point clouds to achieve differential fusion of multi-modal features at different depths. This fusion module can enhance the representation ability of original features for different point sets and provide more comprehensive features by using the dual splicing strategy of concatenation and index connection. Additionally, considering point density as a feature and its negative correlation with depth, we build an Adaptive Threshold Generation Network (ATGN) to generate the depth threshold by extracting density information, which can divide point clouds more reasonably. Experiments on the KITTI dataset demonstrate the effectiveness and competitiveness of our proposed models.

Multimodal Discourse Analysis of Green Advertising of Electric Vehicles from the Perspective of Visual Grammar

Based on the visual grammar, this paper analysed the multi-modal discourse of green advertisement by taking the video of Electric vehicle as an example. Through the analysis of different modes such as composition, colour, text and background, this paper deeply studied the expression forms of vocabulary, grammar, vision, and symbol in the EV ads and the mutual relationship between different modes, revealed the correlation between the mode and meaning, and explored the language features of green advertisement application and the effect of discourse transmission. This paper finds that green advertising uses image resources and colour resources to construct conceptual meaning, using font, background, and depth of field to construct emotional meaning, and using composition resources to construct social significance.

К вопросу о дихотомии образа змеи в южнославянской традиции

An appeal to figurative representations preserved by folk tradition is an attempt to study mentality, understanding the world of a particular culture. Immersion in the world of mythological and fairy-tale formations makes it possible to reveal social representations, the specifics of intercultural interaction at the level of folk tradition. The relevance of this article lies in the possibility of identifying supra-ethnic processes of intercultural interaction in the South Slavic realm, as well as their social, cultural and ideological aspects. The symbolism of the snake in the cultures of various samples is known from Paleolithic times. According to a biological point of view, this is one of the most common groups of animals, neighboring in the past and today to humans all around the globe. Emotional perception of the quality of a snake (danger, secrecy, unusual appearance) and its mythological derivatives, make it possible to identify alternative, but common types of the snake image reflection that take place in Western and especially Eastern Slavic, Romanesque, Albanian, Germanic, Indo-Iranian, Greco-Roman and other mythological traditions. Their correct understanding of the ethical and aesthetic cultural paths of development of modern Slavic peoples, who have entered into a regulated dynamic cultural convergence with the whole world, is an urgent scientific and social task. The main sources of the article are songs, fairy tales, descriptions of the rituals of the Serbs, Croats, Slovenes, Bulgarians, Macedonians. Perception of the image of a snake and giving it fabulous and mythological features in historical times revealing the inconsistency of Slavic ideas about the objects present, their natural, emotional coloring of images and detection with a person.

A Multicenter Pilot Study of Biliary Atresia Screening Using Digital Stool Color Imaging.

The presence of alcoholic stool in biliary atresia (BA) patients is the basis of a stool color card (SCC), a screening tool that has led to more patients receiving Kasai portoenterostomy earlier. This study aimed to evaluate the color image processing of stool images captured using smartphones. We propose that measuring digital color parameters is a more objective method for identifying BA stools and may improve the sensitivity of BA screening. A prospective study was conducted in five hospitals in Thailand between October 1, 2020, and December 31, 2021. Stools from infants presenting with jaundice, acholic stool, or dark-colored urine were photographed. Digital image color analysis was performed, and software was developed based on the color on the original SCC. Sensitivity and specificity for predicting BA stools were compared between the SCC and the software. Of 33 infants eligible for data collection, 19 were diagnosed with BA. Saturation and blue were two potential digital color parameters used to differentiate BA stools. The receiver operating characteristic curve was used to determine the optimum cutoff point of both values, and when saturation ≤56 or blue ≥61 was set as a threshold for detecting BA stool, high accuracy was achieved at 81.8% and 78.8%, respectively. Digital image processing is a promising technology. With appropriate cutoff values of saturation in hue, saturation, value and blue in red, green, blue color models, BA stools can be identified, and equivocal-colored stools of non-BA patients can be differentiated with acceptable accuracy in infants presenting with jaundice.

Information communication and aesthetic value of data visualization in the field of art and design

Abstract Data visualization has become a popular discipline in today’s society, and understanding this trend has become a hot topic in the field of design. This paper introduces the visual perception system of the human eye, combining graphic features and dynamic features to reveal the visualization mechanism of information dynamic conveying in the field of art design. Then, the quantitative model of visual communication in color and graphic texture in art design, respectively, and the art design image containing rich plants are taken as an example to realize the visual analysis of information communication in the field of art design. There is a significant negative correlation (p > 0.05) between the purity of the foreground image, the foreground image color gamut area, and the proportion of foreground image color matching. This means that the higher the purity of the foreground image, the smaller the color matching area, leading to more coordinated color matching and a higher aesthetic degree. Simultaneously, the alteration in the gray value of the image texture typically exhibits characteristics such as high frequency, small amplitude, and randomness. The use of visualization technology in art design, reasonable collocation of colors, and graphic aesthetics fully demonstrate its aesthetic value.