Color Modeling Research Articles

Study of chromatic adaptation using memory color matches, Part I: neutral illuminants.

Twelve corresponding color data sets have been obtained using the long-term memory colors of familiar objects as target stimuli. Data were collected for familiar objects with neutral, red, yellow, green and blue hues under 4 approximately neutral illumination conditions on or near the blackbody locus. The advantages of the memory color matching method are discussed in light of other more traditional asymmetric matching techniques. Results were compared to eight corresponding color data sets available in literature. The corresponding color data was used to test several linear (von Kries, RLAB, etc.) and nonlinear (Hunt & Nayatani) chromatic adaptation transforms (CAT). It was found that a simple two-step von Kries, whereby the degree of adaptation D is optimized to minimize the DEu'v' prediction errors, outperformed all other tested models for both memory color and literature corresponding color sets, whereby prediction errors were lower for the memory color sets. The predictive errors were substantially smaller than the standard uncertainty on the average observer and were comparable to what are considered just-noticeable-differences in the CIE u'v' chromaticity diagram, supporting the use of memory color based internal references to study chromatic adaptation mechanisms.

A Hybrid Approach for Real-Time Object Detection and Tracking to Cover Background Turbulence Problem

Objectives: A number of techniques available in literature do not discuss the problem of incorrect object detection in the scenes having unstable or moving background. A novel hybrid technique is proposed in the paper to cover this problem. Methods/Statistical Analysis: The new approach proposed is the hybrid of two well known techniques for object detection. One is frame-differencing approach and other is skin colour modelling. This newer technique exploits the fact that the demerit of one technique works as merit for other and hence hybrid technique resolves the problem of moving background or turbulence in background. A real time video having turbulence in background is used for testing the efficiency of the approach. Findings: With accuracy of 0.97, the proposed approach outperforms the individual approaches i.e. frame-differencing and skin colour modelling. A very low value of False Positive Rate (FPR) for proposed approach compared to other approaches confirms the least incorrect detections. High value of True Positive Rate (TPR) conveys that fall out of correct object is least in the video by proposed approach. Results show that the proposed approach better applicable for background with turbulence. Application/Improvements: Automatic object detection and tracking in applications as surveillance are tractable with the proposed hybrid approach.

Diagnosis of Technological Systems Based On the Structural Decomposition of Their Coloured Petri Net Model

Abstract Diagnosing faults during the operation of a system is an essential task when investigating technological systems. In this paper, a new online fault identification method is proposed which is based on the occurrence graph of the coloured Petri net model of the system. The model is able to simulate the normal and faulty operations of the system given in the form of event lists, so called traces. The diagnosis is based on the search for deviations between the traces of the normal and the actual operations. In the case of complex technological systems, the occurrence graph can contain hundreds of nodes; therefore, the computational effort and searching-time increase significantly. Our proposed structural decomposition method can manage these demands so it has a crucial impact on the practical application of diagnostic processes. The main idea of our method is that the complex systems can be decomposed into technological units. Therefore, the diagnosis can be done by components separately and the diagnostic result of a unit can be used for the diagnosis of the other units connected to it. Because of the structural decomposition, the diagnosis has to be performed on much smaller occurrence graphs but the effect of faults in previous units is taken into account. The proposed method is illustrated by a simple case study.

Integrated expression method for technical mapping of traffic parameters using RGB color model

SummaryIn the field of traffic flow, speed, density, time, and distance are fundamental variables analyzed to predict traffic conditions. Reliable sources of information are gauged using tested mathematical approaches that have been developed. However, a fundamental diagram that could serve as a basis for expression techniques has not been devised. Red–green–blue (RGB) color modeling was used to overcome this limitation in traffic flow. The purpose of this study is to provide a way to understand traffic flow conditions based on features of three traffic flow elements simultaneously. The limitation of three‐dimensional expressions in two‐dimensional paper was extended to multi‐dimensional information. Information on speed, density, and flow were combined into a single RGB color and given the name RGB flow‐density space time‐distance space. This cancels out the effect of each individual's vehicular trajectories and contains five major components of a specific road section. The new gizmo aims to provide information on traffic flow conditions in transition and to stimulate further approaches related to the predictions and understanding of traffic flow. Copyright © 2016 John Wiley & Sons, Ltd.

Mining Suspicious Tax Evasion Groups in Big Data

There is evidence that an increasing number of enterprises plot together to evade tax in an unperceived way. At the same time, the taxation information related data is a classic kind of big data. These issues challenge the effectiveness of traditional data mining-based tax evasion detection methods. To address this problem, we first investigate the classic tax evasion cases, and employ a graph-based method to characterize their property that describes two suspicious relationship trails with a same antecedent node behind an Interest-Affiliated Transaction (IAT). Next, we propose a Colored Network-Based Model (CNBM) for characterizing economic behaviors, social relationships, and the IATs between taxpayers, and generating a Taxpayer Interest Interacted Network (TPIIN). To accomplish the tax evasion detection task by discovering suspicious groups in a TPIIN, methods for building a patterns tree and matching component patterns are introduced and the completeness of the methods based on graph theory is presented. Then, we describe an experiment based on real data and a simulated network. The experimental results show that our proposed method greatly improves the efficiency of tax evasion detection, as well as provides a clear explanation of the tax evasion behaviors of taxpayer groups.

A Novel Petri Nets-Based Modeling Method for the Interaction between the Sensor and the Geographic Environment in Emerging Sensor Networks.

The service of sensor device in Emerging Sensor Networks (ESNs) is the extension of traditional Web services. Through the sensor network, the service of sensor device can communicate directly with the entity in the geographic environment, and even impact the geographic entity directly. The interaction between the sensor device in ESNs and geographic environment is very complex, and the interaction modeling is a challenging problem. This paper proposed a novel Petri Nets-based modeling method for the interaction between the sensor device and the geographic environment. The feature of the sensor device service in ESNs is more easily affected by the geographic environment than the traditional Web service. Therefore, the response time, the fault-tolerant ability and the resource consumption become important factors in the performance of the whole sensor application system. Thus, this paper classified IoT services as Sensing services and Controlling services according to the interaction between IoT service and geographic entity, and classified GIS services as data services and processing services. Then, this paper designed and analyzed service algebra and Colored Petri Nets model to modeling the geo-feature, IoT service, GIS service and the interaction process between the sensor and the geographic enviroment. At last, the modeling process is discussed by examples.

Creating a Colorful Model of Change

This article describes how a meta-theory of change, now referred to as the “color model,” was developed over a period of two decades. We look back to better understand how one creative idea took on many manifestations and is now a widely used theory. We identify three distinct periods of development: inception, storming and norming, and maturity. In each of these periods, we discern a similar pattern of activities, in line with Smith and Hitt’s four-stage model of theory development: tension, searching, elaboration, and proclamation. The case illustrates the journey was spurred on by breakdowns in meaning, influenced by context and serendipity, and shaped by incremental elaboration. As academic practitioners, we discuss how our position in the field affected the way we approach theory development. We conclude the article with a discussion on the downside of originality.

ILUT based Skin Colour Modelling for Human Detection

Objectives: Numerous techniques have been proposed in past for skin colour modelling and some out of these are utilized in human detection problem. This paper proposes a novel technique which uses Indexed Look-up-table (ILUT) for skin colour modelling. Methods: In the proposed technique, skin colour cluster on 2-dimensional Cb-Cr plane in YCbCr colour space is modelled with an ILUT. ILUT contains the lower and upper bounds of Cb values corresponding to each value of Cr in the skin colour cluster. Outliers in the cluster are removed by applying median filter, because they contribute to the wrong classification of skin colour and hence wrong detection. Findings: The proposed technique delivers reasonably good performance on True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy parameters. Classification accuracy of 89.89% for proposed technique is almost comparable to that of other techniques in literature. Comparative results are shown in Table 2 of the paper. Classification complexity is one parameter on which the proposed technique outperforms the rest of the other skin colour modelling techniques and it is of O(MxN). MxN is the image resolution. Skin colour modelling using ILUT is also efficient in terms of space requirements as compared to other non-parametric methods of skin colour modelling. Applications: Least classification complexity makes this technique most appropriate for real time systems/ applications for detecting presence of human being.

Let there be color!

We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image. The entire framework, including the global and local priors as well as the colorization model, is trained in an end-to-end fashion. Furthermore, our architecture can process images of any resolution, unlike most existing approaches based on CNN. We leverage an existing large-scale scene classification database to train our model, exploiting the class labels of the dataset to more efficiently and discriminatively learn the global priors. We validate our approach with a user study and compare against the state of the art, where we show significant improvements. Furthermore, we demonstrate our method extensively on many different types of images, including black-and-white photography from over a hundred years ago, and show realistic colorizations.

Colored Petri net model for significant reduction of invariants in flexible manufacturing systems

This study focuses on simplification of the Petri net model of a manufacturing system for reduction in the total number of elementary circuits to decrease the analysis time of systems during investigations. A flexible manufacturing system with a tool sharing environment has been developed and investigated using colored Petri nets. The proposed model is extended and implemented on large flexible manufacturing systems (FMSs) having multiple machines and tools. The main objective of this study is to reduce the resultant invariants, as the addition of parts, machines, and tools in black token Petri net models exponentially increases the resultant invariants. Various scenarios have been investigated and compared using the developed model. The structure of the developed colored Petri net (CPN) model allows grouping of the tools that reduces the total number of places in the model. Therefore, it is evident that the developed CPN model reduces the resultant invariant that results in reduced complexity of the system.

Prediction of Photocatalytic Degradation and Mineralization Efficiencies of Basic Blue 3 Using $${{\rm TiO}_{2}}$$ TiO 2 by Nonlinear Modeling Based on Box–Behnken Design

In the present study, nonlinear modeling of color and chemical oxygen demand (COD) removal of Basic Blue 3 as a fairly toxic textile dye by photocatalytic degradation using \({{\rm TiO}_{2}}\) was investigated. A three replicates and one duplicate of Box–Behnken design with 45 runs, with three factors and three levels of catalyst concentration of 0.5–1.5 g/L, initial dye concentration of 20–100 mg/L, and initial pH of 3–11 were set to quantify the explanatory variables. ANOVA results showed that the best-fit multi-nonlinear regression models were cross-validated \({(R^{2}_{\rm pred})}\) accounting for 99.98–99.79 % and were expressed \({(R^{2}_{\rm adj})}\) accounting for 99.88–99.85 % of variation in decolorization (color removal) and mineralization (COD removal), respectively. In total, 0.51 g/L of \({{\rm TiO}_{2}}\), 100 mg/L of initial dye concentration, and initial pH of 7 were established as optimum operating parameters by leave-one-out optimization that resulted in 88.61 and 79.03 % for color and COD removal, respectively. 88.78 % of color removal and 76.95 % of COD removal were determined by cross-validation experiments at optimum conditions. Results showed that optimum point determination was successful.

Region-based colour modelling for joint crop and maize tassel segmentation

Crop segmentation is a frequently occurring problem for computer vision applications in agriculture. Meanwhile, the fine-grained shape extraction of maize tassels is also an essential step in the detasselling and field-based phenotyping research. However, existing methods are usually dependent on category, which is hard to transfer to other cultivars with different colours. To address this, the goal of this study is to develop a general method that can process different colours simultaneously and that has good flexibility and expandability. Targeting maize, we proposed to segment jointly the crop and maize tassel. In particular, a novel joint segmentation dataset regarding crop and maize tassel (323 images with corresponding manually-annotated ground-truth images) is constructed, hoping that it can serve as a benchmark to facilitate related studies. Technically, a region-based approach that leverages the efficient graph-based segmentation algorithm and simple linear iterative clustering (SLIC) is developed to generate region proposals. Also, we proposed to model colours with ensemble neural networks specific to each intensity, aiming to achieve robustness to illumination. In addition, two simple but effective strategies are devised to accelerate the colour statistics extraction and ensemble model prediction. The effectiveness and efficiency of our method are demonstrated on the two segmentation tasks, respectively. Results show that our method significantly outperforms other state-of-the-art approaches on tassel segmentation, with average precision of 74.3%, and achieves comparable performance of 77.8% on the traditional crop segmentation even with the naivest colour feature. The dataset and source code are made available online.

Fuzzy Support Vector Machine Based on Color Modeling for Facial Complexion Recognition in Traditional Chinese Medicine

Face diagnosis is one of the four diagnostic methods in Traditional chinese medicine (TCM). The morbidity of the organs can be revealed from the facial complexion. Due to the ambiguity of face diagnosis in TCM, Fuzzy support vector machine (FSVM) is utilized to remove the influence of outliers. The facial complexion recognition in TCM based on FSVM is proposed in this paper, which includes the following steps: 1) The facial cheek region are segmented as skin blocks; 2) The color feature in Lab color space is extracted from skin block to represent facial complexion characteristic; 3) The fuzzy membership is calculated to obtain the membership of the training samples, in which the final membership of training samples is determined by combining membership calculation based on distance with the facial complexion recognition based on color modeling; 4) The FSVM is built to classify facial complexion characteristic. Experimental results show that proposed facial complexion recognition has better anti-interference performance as well as a higher recognition rate up to 82%.

Matter fields in triangle–hinge models

The worldvolume theory of membrane is mathematically equivalent to three-dimensional quantum gravity coupled to matter fields corresponding to the target space coordinates of embedded membrane. In a recent paper [arXiv:1503.08812] a new class of models are introduced that generate three-dimensional random volumes, where the Boltzmann weight of each configuration is given by the product of values assigned to the triangles and the hinges. These triangle-hinge models describe three-dimensional pure gravity and are characterized by semisimple associative algebras. In this paper, we introduce matter degrees of freedom to the models by coloring simplices in a way that they have local interactions. This is achieved simply by extending the associative algebras of the original triangle-hinge models, and the profile of matter field is specified by the set of colors and the form of interactions. The dynamics of a membrane in $D$-dimensional spacetime can then be described by taking the set of colors to be $\mathbb{R}^D$. By taking another set of colors, we can also realize three-dimensional quantum gravity coupled to the Ising model, the $q$-state Potts models or the RSOS models. One can actually assign colors to simplices of any dimensions (tetrahedra, triangles, edges and vertices), and three-dimensional colored tensor models can be realized as triangle-hinge models by coloring tetrahedra, triangles and edges at a time.

Training for Skull Base Surgery with a Colored Temporal Bone Model Created by Three-Dimensional Printing Technology

A 3-dimensional temporal bone model for skull base surgical training was reconstructed via the use of a selective laser sintering technique, which is one of the 3-dimensional printing technologies. The temporal bone model was created in 2 pieces to remove powder material in the mastoid air cells and to place dye into the semicircular canal and the Fallopian canal. The powder material was minimal, and the decisive structures were identified in color. This artificial model will pave the way to a "new era" in surgical training and medical education.

Image Segmentation Using Fuzzy Spatial-Taxon Cut: Comparison of Two Different Stage One Perception Based Input Models of Color (Bayesian Classifier and Fuzzy Constraint)

Image Segmentation Using Fuzzy Spatial-Taxon Cut: Comparison of Two Different Stage One Perception Based Input Models of Color (Bayesian Classifier and Fuzzy Constraint)

Pengembangan Model Pembelajaran Permainan Colorful Balls Run Untuk Reaksi Gerak Pada Anak Tunagarhita

The purpose of this study was to determine the products of The Development of Learning Colorful Balls Run for Motion Reaction of mentally disabled children of SLB Negeri Semarang grade V in the academic year of 2015.. This research is the development (research and development / R & D), which consists of 10 steps of research, namely the potential and problems, data collection, product design, design validation, design revisions, test products, product revision, trial use, testing products, mass production Because the average obtained from the experts of physical education teacher 80% (good) and from learning experts gained 92% (very good). The results of trial I product of small group on cognitive aspects was 83.53% (good), affective aspects was 82.10% (good), psychomotor aspects was 81.39% (good), the average of trial I was 82.34% (good). The results of trial II, the large group in the cognitive aspects was 85.14% (good) affective aspects was 83.76% (good) psychomotor aspects was 83.07% (good), the average of trial II was 83.99% (good). It was concluded that the development of colorful balls run game model can be used as an alternative to learn sport especially small ball game for V graders of SLB Negeri Semarang.

Variational Image Colorization Models Using Higher-Order Mumford–Shah Regularizers

This article introduces variational models for restoring a color image from a grayscale image with color given in only small regions. The models involve the chromaticity color component as in Kang ...

Parameter Estimation of Non-linear Dynamic Channel Based on UKF

The current research results of cognitive radio are too many, people have made a lot of efforts to estimate the parameters of cognitive channel. But the channel noise model in these articles is Gauss noise model, this model is different from the reality of the channel. There is a certain deviation between the research result and the actual under this condition. For this problem, in this paper, a channel model based on Colored noise model is proposed, which is based on the steady state distribution as the noise model. Realization of channel parameter estimation by UKF algorithm, After the study, it is found that UKF has better effect on the estimation of the parameters of the nonlinear dynamic channel than the other algorithms.

Inquiry-driven Teaching & Learning Using the Archaeal Microorganism Halobacterium NRC-1

Access to microorganisms in the classroom is essential for teaching fundamental concepts of life sciences, yet most are neither safe nor accessible for classroom instruction. Halobacterium NRC-1, a member of the Domain Archaea, is ideal for engaging students through inquiry-based teaching and learning in the classroom. We introduce this colorful model microbe and provide many ways in which it can be used for experiential learning, including life in extreme environments, basic microbiology skills, action of antibiotics, genotype–phenotype connection, genomics and bioinformatics, and applications in biotechnology.