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.

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.

Variability of phytoplankton absorption in the northern South China Sea:influence of the size structure and pigment composition of algal populations

Data from three cruises conducted in the Zhujiang River （ZR）, coastal waters of Guangdong （CWGD） and the northern South China Sea （NSCS） during 2003 and 2004 were examined for assessing the relative importance of pigment composition and packaging effect in modifying the specific absorption coefficients of phytoplankton. The three survey regions differ widely in their phytoplankton community with large cells dominating the ZR and CWGD waters and small cells dominating the NSCS region. Variations in the size structure and the accessory pigments have much effect on the chlorophyll a-specific absorption coefficient of phytoplankton. The size index accounted for about 42% and 33% of the variation of the specific absorption coefficient at 440 and 675 nm, respectively. Using the multiple regression analysis approach, pigment concentrations for each sample were calculated. The accessory pigments other than chlorophyll a contribute to absorption mainly in the blue - to - green region of the spectrum and their absorptions account for about 44%, 43% and 53% on the average of the total phytoplankton absorption at 440 nm for the ZR, CWGD and NSCS regions. Among the accessory pigments, the photosynthetic carotenoids （noted PSC） play a dominant role in the ZR and CWGD waters, while in the NSCS the nonphotosynthetic carotenoids （noted PPG） as well as PSC have important contributions. Because the variations of both the size structure and accessory pigments in algal populations contributed to the variability of the specific absorption coefficient in the study regions, these factors may be considered explicitly in future bio - optical algorithms to derive chlorophyll a concentration more accurately.

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.

Resonant production of color octet muons at Future Circular Collider-based muon-proton colliders

We investigate the resonant production of color octet muons in order to explore the discovery potential of Future Circular Collider（FCC）-based μp colliders.It is shown that the search potentials of μp colliders essentially surpass the potential of the LHC and would exceed that of the FCC pp collider.

Image processing algorithms for in-field cotton boll detection in natural lighting conditions

In developing countries,the cotton harvesting operation is currently being performed manually.Due to the monotonous nature of this task and the involvement of a considerable amount of labor,this operation becomes very tedious and costly.The harvesting robots can be a good alternative for the selective picking of cotton bolls from the field.In this study,an attempt has been made to develop the image processing algorithms for in-field cotton boll detection in natural lighting conditions for the cotton harvesting robot.Four image processing algorithms namely color difference,band ratio,YCbCr method,and chromatic aberration were proposed for the real-time segmentation of cotton bolls under natural outdoor light conditions.The performance of developed image processing algorithms was evaluated and the experimental results revealed that the chromatic aberration method outperforms as compared to other developed algorithms.The chromatic aberration method showed the highest identification rate of 91.05%with false positive and false negative rates of 6.99%and 4.88%respectively,among all the proposed algorithms.The highest sensitivity and specificitywere found to be 81.31%and 97.53%,respectively,using the chromatic aberration method.Overall,the chromatic aberration approach demonstrated a very promising performance for in-field cotton bolls detection under natural lighting conditions which confirms its applicability for the robotic cotton harvesters.

Automatic Identification of Cracks from Borehole Image Under Complicated Geological Conditions

Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.

Intelligent Video Surveillance for Checking Attendance of Traffic Controllers in Level Crossing

This paper proposes a detecting and tracking scheme for automatic checking attendance of traffic controllers in level crossing by recognizing their warning waistcoats. Considering of the actual requirement of rapidity and validity, this paper employs techniques of motion detection, color segmentation and feature matching to deal with the challenging problems of illumination varying, light reflection and disturbance. Therefore, the task of distinguishing the target from candidates can be fulfilled accurately. Once a target being detected, the established color models are modified through learning color of the detected target, and then Cam-shift algorithm is employed to track this target smoothly. The experiments in real scenes demonstrate that this method has a great capability to detect and track traffic controllers in complex level crossing environment accurately, and the comparisons further demonstrate the validity of the proposed method.

Comparing AdS/CFT dipole model to HERA F_2 data

We apply an AdS/CFT-inspired color-dipole model which contains only three free parameters to describe the HERA data for the inclusive structure function F 2 at small Bjorken-x and virtuality.We found that the saturation scale in our AdS/CFT-based parameterization varies in the range of 1 ÷ 3 GeV becoming independent of energy/Bjorken-x at very small x.This leads to the prediction of x-independence of the structure functions at very small x.With the fitted parameters in our model,the predictions for F 2,longitudinal structure function,charm structure function and total photo-production cross-sections in the kinematic regions of future experiments can be given.