Visualization of uncertainty associated with spatial prediction of continuous variables using HSI color model:a case study of prediction of pH for topsoil in peri-urban Beijing,China

Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of topsoil in the peri-urban Beijing. A two-dimensional legend was designed to accompany the visualization-vertical axis （hues） for visualizing the predicted values and horizontal axis （whiteness） for visualizing the prediction error. Moreover, different ways of visualizing uncertainty were briefly reviewed in this paper. This case study indicated that visualization of both predictions and prediction uncertainty offered a possibility to enhance visual exploration of the data uncertainty and to compare different prediction methods or predictions of totally different variables. The whitish region of the visualization map can be simply interpreted as unsatisfactory prediction results, where may need additional samples or more suitable prediction models for a better prediction results.

A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

Artificial Neural Network Performing the Forward Operation of Color Appearance Model

A method of the forward operation of color appearance (from colorimetric attributes to color appearance attributes) using an artificial neural network (ANN) is presented The neural network model developed is a multilayer feedforward neural network model for predicting color appearance model (CAM). This method greatly decreased the mathematical computation in color appearance prediction. The error backed-propagation (BP) algorithm was applied in the training of the neural networks, and it was trained and tested by the LUTCHI color appearance datasets which are the most comprehensive one in testing color appearance model. CRT was selected as a typical example in experiment because it is usually used as self-luminous object in fact, and several ways for choosing training samples were included and compared each other. The testing results show that the color appearance prediction using artificial neural network is well consistent with visual evaluation.

Simulation of CIECAM02 color appearance model based on Chinese color system

Color appearance model （CAM） can be used to determine the required colors for repro- duction across changes in cross-media. CIECAM02 color appearance model prediction is implemen- ted by artificial neural networks in this paper, which includes forward and reversed prediction. 1333 color samples as training samples and other 1332 color samples as test samples are selected in the Chinese color system. In order to test the prediction accuracy of neural networks after simulation of CIECAM02 color appearance model, the color-difference formula can be used for the evaluation of forward and reversed models. Results have shown that BP neural-network has acceptable accuracy in simulation of CIECAM02 color appearance model for colors of Chinese color system.

Color and Texture Segmentation Using an Unified MRF Model

The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I1, I2, I3) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.

Improving CAD Hemorrhage Detection in Capsule Endoscopy

This study explores an automated framework to assist the recognition of hemorrhage traces and bleeding lesions in video streams of small bowel capsule endoscopy (SBCE). The proposed methodology aims to achieve fast image control (<10 minutes), save valuable time of the physicians, and enable high performance diagnosis. A specialized elimination algorithm excludes all identical consecutive frames by utilizing the difference of gray levels in pixel luminance. An image filtering algorithm is proposed based on an experimentally calculated bleeding index and blood-color chart, which inspects all remaining frames of the footage and identifies pixels that reflect active or potential hemorrhage in color. The bleeding index and blood-color chart are estimated of the chromatic thresholds in RGB and HSV color spaces, and have been extracted after experimenting with more than 3200 training images, derived from 99 videos of a pool of 138 patients. The dataset has been provided by a team of expert gastroenterologist surgeons, who have also evaluated the results. The proposed algorithms are tested on a set of more than 1000 selected frame samples from the entire 39 testing videos, to a prevalence of 50% pathologic frames (balanced dataset). The frame elimination of identical and consecutive frames achieved a reduction of 36% of total frames. The best statistical performance for diagnosis of positive pathological frames from a video stream is achieved by utilizing masks in the HSV color model, with sensitivity up to 99%, precision 94.41% to a prevalence of 50%, accuracy up to 96.1%, FNR 1%, FPR 6.8%. The estimated blood-color chart will be clinically validated and used in feature extraction schemes supporting machine learning ML algorithms to improve the localization potential.

Inhomogeneous Shadowing Effect in High-Energy p-A Drell-Yan Process:

Having studied the initial state energy loss versus nuclear shadowing for the Drell-Yan dimuon pairproduction in the color string model,the inhomogeneous shadowing effect is considered in this paper.We find thatthe inhomogeneous shadowing effect does amend the rate of energy loss per unit path length,-dE/dz.Finally,thetheoretical results for the Drell-Yan differential cross-section ratios are compared with the E772 and E866 data.It isfound that the theoretical results are in good agreement with the experimental data.

Non-perturbative Aspects of QCD and Parameterized Quark Propagator

Based on the Global Color Symmetry Model, the non-perturbative Q, CD vacuum is investigated in the parameterized fully dressed quark propagator. Our theoretical predictions for various quantities characterized the QCD vacuum are in agreement with those predicted by many other phenomenologieal QCD inspired models. The successful predictions clearly indicate the extensive validity of our parameterized quark propagator used here. A detailed discussion on the arbitrariness in determining the integration cut-off parameter ofμ in calculating QCD vacuum condensates and a good method, which avoided the dependence of calculating results on the cut-off parameter is also strongly recommended to readers.

Mesons in the Constituent Quark Model

The quark-antiquark （q^-q） spectrum is studied by solving the Schrōdinger equation in the framework of nonrelativistic constituent quark model. An overall good fit to the experimental data of meson is obtained. The interactions between quark and antiquark consist of quadratic colour confinement-exchange, one-gluon-exchange, and Goldstone-boson-exchange potentials.

Probabilistic Tracking of Objects with Adaptive Cue Fusion Mechanism

A tracking method based on adaptive multiple cue fusion mechanism was presented,where particle filter is used to integrate color and edge cues.The fusion mechanism assigns different weights to two cues according to their importance,thus improving the robustness and reliability of the tracking algorithm.Moreover,a multi-part color model is also invoked to deal with the confliction among similar objects.The experimental results on two real image sequences show the tracking algorithm with adaptive fusion mechanism performs well in the presence of complex scenarios such as head rotation,scale change and multiple person occlusions.

Modeling of cognitive framework in time-stressed decision making

An approach for modeling a human cognitive framework in time-stressed decision making is presented. The recognitive and metacognitive processes that represent the cognitive framework are modeled by the colored Petri nets （CPNs）. A structural and behavioral analysis method is adopted to obtain the static and dynamic property used to verify the CPNs model of the cognitive framework. Finally, an example from the command and control radar recognition system is used to evaluate the feasibility and availability of the CPNs model adopted in practical systems.

Application of artificial intelligence in tongue diagnosis of traditional Chinese medicine:A review

Tongue diagnosis is an important process to non-invasively assess the condition of a patient’s internal organs in traditional Chinese medicine(TCM)and each part of the tongue is related to corresponding internal organs.Due to continuing computer technological advances,especially the artificial intelligence(AI)methods have achieved significant success in tackling tongue image acquisition,processing,and classification,novel AI methods are being introduced in traditional Chinese medicine tongue diagnosis medical practices.Traditional tongue diagnose depends on observations of tongue characteristics,such as color,shape,texture,moisture,etc.by traditional Chinese medicine physicians.The appearance of the tongue color,texture and coating reflects the improvement or deterioration of patient’s conditions.Moreover,AI can now distinguish patient’s condition through tongue images,texture or coating,which is all possible increasingly with help from traditional Chinese medicine physicians under the traditional Chinese medicine tongue theory.AI has enabled humans to do what was previously unimagined:traditional Chinese medicine tongue diagnosis with feeding a large amount of tongue image and tongue texture/coating data to train the AI modes.This review focuses on the research advances of AI in TCM tongue diagnosis thus far to identify the major scientific methods and prospects.In this article,we tried to review the AI application in resolving the tongue diagnosis of traditional Chinese medicine on color correction,tongue image extraction,tongue texture/coating segmentation.

Study of Strange Quark Mass in CFL Phase

In this paper we introduce bilocal fields in the global color symmetry model and consider color and electrical neutrality conditions simultaneously to study the effect of strange quark mass Ms for the momentum-dependent condensate of color-flavor locked phase. Consequently we find that there will be a quantum phase transition occurring.

Applying a semantic information Petri Net modeling method to AUV systems design

This paper informally introduces colored object-oriented Petri Nets(COOPN) with the application of the AUV system.According to the characteristic of the AUV system's running environment,the object-oriented method is used in this paper not only to dispart system modules but also construct the refined running model of AUV system,then the colored Petri Net method is used to establish hierarchically detailed model in order to get the performance analyzing information of the system.After analyzing the model implementation,the errors of architecture designing and function realization can be found.If the errors can be modified on time,the experiment time in the pool can be reduced and the cost can be saved.

Face Detection Technology Based on Robot Vision

One being developed automatic sweep robot, need to estimate if anyone is on a certain range of road ahead then automatically adjust running speed, in order to ensure work efficiency and operation safety. This paper proposed a method using face detection to predict the data of image sensor. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction robot.

Tracking multiple people under occlusion and across cameras using probabilistic models

Tracking multiple people under occlusion and across cameras is a challenging question for discussion. Furthermore, the cameras in this study are used to extend the field of view, which are distinguished from the same field of view. Such corre- spondence between multiple cameras is a burgeoning research subject in the area of computer vision. This paper effectively solves the problems of tracking multiple people who pass from one camera to another and segmenting people under occlusion using probabilistic models. The probabilistic models are composed of blob model, motion model and color model, which make the most of the space, motion and color information. First, we present a color model that uses maximum likelihood estimation based on non-parametric kernel density estimation. Second, we introduce a blob model based on mean shift, which segments the body into many regions according to the color of each person in order to spatially localize the color features corresponding to the way people are dressed. Clothes can be any mixture of colors. Third, we bring forward a motion model based on statistical probability which indicates the movement position of the same person between two successive frames in a single camera. Finally, we effectively unify the three models into a general probabilistic model and attain a maximization likelihood probability image, which is used to segment the foreground region under occlusion and to match people across multiple cameras.

Studying pressure sores through illuminant invariant assessment of digital color images

Methods for pressure sore monitoring remain both a clinical and research challenge.Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions.In this paper a technique is introduced for the assessment of pressure sores in guinea pigs,using captured color images.Sores were artificially induced,utilizing a system particularly developed for this purpose.Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation.Different segments of the color images were divided and labeled into three classes,based on their severity status.For quantitative analysis,a color based texture model,which is invariant against monotonic changes in illumination,is proposed.The texture model has been developed based on the local binary pattern operator.Tissue segments were classified,using the texture model and its features as inputs to a combination of neural networks.Our method is capable of discriminating tissue segments in different stages of pressure sore generation,and therefore can be a feasible tool for the early assessment of pressure sores.

Indoor and outdoor people detection and shadow suppression by exploiting HSV color information

An adaptive background model based on max-imum statistical probability and a shadow suppression scheme for indoor and outdoor people detection by exploiting hue saturation value(HSV)color information is proposed.To obtain the initial background scene,the frequency of R,G,and B component values for each pixel at the same position in the learning sequence are respec-tively calculated;the R,G,and B component values with the biggest ratios are incorporated to model the initial background.The background maintenance,or the so-called background re-initiation,is also proposed to adapt to scene changes such as illumination changes and scene geometry changes.Moving cast shadows generally exhibit a challenge for accurate moving target detection.Based on the observation that a shadow cast on a background region lowers its brightness but does not change its chro-maticity significantly,we address this problem in the ar-ticle by exploiting HSV color information.In addition,quantitative metrics is introduced to evaluate the algo-rithm on a benchmark suite of indoor and outdoor video sequences.The experimental results are given to show the performance of the algorithm.

Universal function of the diffractive process in color dipole picture

In this study,we obtain the universal function corresponding to the diffractive process and show that the cross section exhibits geometrical scaling.It is observed that diffractive theory according to the color dipole approach at small-x is a convenient framework that reveals the color transparency and saturation phenomena.We also calculate the contribution of heavy quark production in the diffractive cross section at high energy that is determined by the small size dipole configuration.The ratio of the diffractive cross section to the total cross section in electron-proton collision is the other important quantity that is computed in this work.

Real-time Face Detection using Skin Color Model

This paper presents a new face detection approach to real-time applications, which is based on the skin color model and the morphological filtering. First the non-skin color pixels of the input image are removed based on the skin color model in the YC rC b chrominance space, from which we extract candidate human face regions. Then a mathematical morphological filter is used to remove noisy regions and fill the holes in the candidate skin color regions. We adopt the similarity between the human face features and the candidate face regions to locate the face regions in the original image. We have implemented the algorithm in our smart media system. The experiment results show that this system is effective in real-time applications.