Detecting Financial Bubbles Using Gap between Common Stocks and Preferred Stocks
How to detecting financial bubble? Addressing this simple question has been the focus of a vast amount of empirical research spanning almost half a century. However, financial bubble is hard to observe and varying over the time; there needs to be more research on this area. In this paper, we used abnormal difference between common stocks price and those preferred stocks price to explain financial bubble. First, we proposed the ‘W-index’ which indicates spread between common stocks and those preferred stocks in stock market. Second, to prove that this ‘W-index’ is valid for measuring financial bubble, we showed that there is an inverse relationship between this ‘W-index’ and S&P500 rate of return. Specifically, our hypothesis is that when ‘W-index’ is comparably higher than other periods, financial bubbles are added up in stock market and vice versa; according to our hypothesis, if investors made long term investments when ‘W-index’ is high, they would have negative rate of return; however, if investors made long term investments when ‘W-index’ is low, they would have positive rate of return. By comparing correlation values and adjusted R-squared values of between W-index and S&P500 return, VIX index and S&P500 return, and TED index and S&P500 return, we showed only W-index has significant relationship between S&P500 rate of return. In addition, we figured out how long investors should hold their investment position regard the effect of financial bubble. Using this W-index, investors could measure financial bubble in the market and invest with low risk.
Challenges in Video Based Object Detection in Maritime Scenario Using Computer Vision
This paper discusses the technical challenges in
maritime image processing and machine vision problems for video
streams generated by cameras. Even well documented problems
of horizon detection and registration of frames in a video are
very challenging in maritime scenarios. More advanced problems
of background subtraction and object detection in video streams
are very challenging. Challenges arising from the dynamic nature
of the background, unavailability of static cues, presence of small
objects at distant backgrounds, illumination effects, all contribute to
the challenges as discussed here.
Thermal Effect on Wave Interaction in Composite Structures
There exist a wide range of failure modes in composite
structures due to the increased usage of the structures especially in
aerospace industry. Moreover, temperature dependent wave response
of composite and layered structures have been continuously studied,
though still limited, in the last decade mainly due to the broad
operating temperature range of aerospace structures. A wave finite
element (WFE) and finite element (FE) based computational method
is presented by which the temperature dependent wave dispersion
characteristics and interaction phenomenon in composite structures
can be predicted. Initially, the temperature dependent mechanical
properties of the panel in the range of -100 ◦C to 150 ◦C are
measured experimentally using the Thermal Mechanical Analysis
(TMA). Temperature dependent wave dispersion characteristics of
each waveguide of the structural system, which is discretized as a
system of a number of waveguides coupled by a coupling element, is
calculated using the WFE approach. The wave scattering properties,
as a function of temperature, is determined by coupling the WFE
wave characteristics models of the waveguides with the full FE
modelling of the coupling element on which defect is included.
Numerical case studies are exhibited for two waveguides coupled
through a coupling element.
Detecting Geographically Dispersed Overlay Communities Using Community Networks
Community detection is an extremely useful technique
in understanding the structure and function of a social network.
Louvain algorithm, which is based on Newman-Girman modularity
optimization technique, is extensively used as a computationally
efficient method extract the communities in social networks. It
has been suggested that the nodes that are in close geographical
proximity have a higher tendency of forming communities. Variants
of the Newman-Girman modularity measure such as dist-modularity
try to normalize the effect of geographical proximity to extract
geographically dispersed communities, at the expense of losing
the information about the geographically proximate communities.
In this work, we propose a method to extract geographically
dispersed communities while preserving the information about the
geographically proximate communities, by analyzing the ‘community
network’, where the centroids of communities would be considered as
network nodes. We suggest that the inter-community link strengths,
which are normalized over the community sizes, may be used
to identify and extract the ‘overlay communities’. The overlay
communities would have relatively higher link strengths, despite
being relatively apart in their spatial distribution. We apply this
method to the Gowalla online social network, which contains
the geographical signatures of its users, and identify the overlay
communities within it.
Reduction of False Positives in Head-Shoulder Detection Based on Multi-Part Color Segmentation
The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.
Ship Detection Requirements Analysis for Different Sea States: Validation on Real SAR Data
Ship detection is nowadays quite an important issue
in tasks related to sea traffic control, fishery management and ship
search and rescue. Although it has traditionally been carried out
by patrol ships or aircrafts, coverage and weather conditions and
sea state can become a problem. Synthetic aperture radars can
surpass these coverage limitations and work under any climatological
condition. A fast CFAR ship detector based on a robust statistical
modeling of sea clutter with respect to sea states in SAR images
is used. In this paper, the minimum SNR required to obtain a
given detection probability with a given false alarm rate for any
sea state is determined. A Gaussian target model using real SAR
data is considered. Results show that SNR does not depend heavily
on the class considered. Provided there is some variation in the
backscattering of targets in SAR imagery, the detection probability
is limited and a post-processing stage based on morphology would
Multimedia Firearms Training System
The goal of the article is to present a novel Multimedia Firearms Training System. The system was developed in order to compensate for major problems of existing shooting training systems. The designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, and implementation of ballistics equations. The combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. The introduced algorithms of geometric and photometric recalibration allow the use of economically viable commercially available projectors for systems that require long and intensive use without most of the negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.
Design and Implementation of a Counting and Differentiation System for Vehicles through Video Processing
This paper presents a self-sustaining mobile system for
counting and classification of vehicles through processing video. It
proposes a counting and classification algorithm divided in four steps
that can be executed multiple times in parallel in a SBC (Single
Board Computer), like the Raspberry Pi 2, in such a way that it
can be implemented in real time. The first step of the proposed
algorithm limits the zone of the image that it will be processed.
The second step performs the detection of the mobile objects using
a BGS (Background Subtraction) algorithm based on the GMM
(Gaussian Mixture Model), as well as a shadow removal algorithm
using physical-based features, followed by morphological operations.
In the first step the vehicle detection will be performed by using
edge detection algorithms and the vehicle following through Kalman
filters. The last step of the proposed algorithm registers the vehicle
passing and performs their classification according to their areas.
An auto-sustainable system is proposed, powered by batteries and
photovoltaic solar panels, and the data transmission is done through
GPRS (General Packet Radio Service)eliminating the need of using
external cable, which will facilitate it deployment and translation to
any location where it could operate. The self-sustaining trailer will
allow the counting and classification of vehicles in specific zones
with difficult access.
Topology-Based Character Recognition Method for Coin Date Detection
For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.
Video Based Ambient Smoke Detection By Detecting Directional Contrast Decrease
Fire-related incidents account for extensive loss of life and
material damage. Quick and reliable detection of occurring fires has high
real world implications. Whereas a major research focus lies on the detection
of outdoor fires, indoor camera-based fire detection is still an open issue.
Cameras in combination with computer vision helps to detect flames and
smoke more quickly than conventional fire detectors. In this work, we present
a computer vision-based smoke detection algorithm based on contrast changes
and a multi-step classification. This work accelerates computer vision-based
fire detection considerably in comparison with classical indoor-fire detection.
Real Time Video Based Smoke Detection Using Double Optical Flow Estimation
In this paper, we present a video based smoke detection
algorithm based on TVL1 optical flow estimation. The main part
of the algorithm is an accumulating system for motion angles and
upward motion speed of the flow field. We optimized the usage of
TVL1 flow estimation for the detection of smoke with very low smoke
density. Therefore, we use adapted flow parameters and estimate the
flow field on difference images. We show in theory and in evaluation
that this improves the performance of smoke detection significantly.
We evaluate the smoke algorithm using videos with different smoke
densities and different backgrounds. We show that smoke detection
is very reliable in varying scenarios. Further we verify that our
algorithm is very robust towards crowded scenes disturbance videos.
An Energy Detection-Based Algorithm for Cooperative Spectrum Sensing in Rayleigh Fading Channel
Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.
Model Updating-Based Approach for Damage Prognosis in Frames via Modal Residual Force
This paper presents an effective model updating strategy for damage localization and quantification in frames by defining damage detection problem as an optimization issue. A generalized version of the Modal Residual Force (MRF) is employed for presenting a new damage-sensitive cost function. Then, Grey Wolf Optimization (GWO) algorithm is utilized for solving suggested inverse problem and the global extremums are reported as damage detection results. The applicability of the presented method is investigated by studying different damage patterns on the benchmark problem of the IASC-ASCE, as well as a planar shear frame structure. The obtained results emphasize good performance of the method not only in free-noise cases, but also when the input data are contaminated with different levels of noises.
Comparison of Central Light Reflex Width-to-Retinal Vessel Diameter Ratio between Glaucoma and Normal Eyes by Using Edge Detection Technique
Glaucoma is a disease that causes visual loss in adults. Glaucoma causes damage to the optic nerve and its overall pathophysiology is still not fully understood. Vasculopathy may be one of the possible causes of nerve damage. Photographic imaging of retinal vessels by fundus camera during eye examination may complement clinical management. This paper presents an innovation for measuring central light reflex width-to-retinal vessel diameter ratio (CRR) from digital retinal photographs. Using our edge detection technique, CRRs from glaucoma and normal eyes were compared to examine differences and associations. CRRs were evaluated on fundus photographs of participants from Mettapracharak (Wat Raikhing) Hospital in Nakhon Pathom, Thailand. Fifty-five photographs from normal eyes and twenty-one photographs from glaucoma eyes were included. Participants with hypertension were excluded. In each photograph, CRRs from four retinal vessels, including arteries and veins in the inferotemporal and superotemporal regions, were quantified using edge detection technique. From our finding, mean CRRs of all four retinal arteries and veins were significantly higher in persons with glaucoma than in those without glaucoma (0.34 vs. 0.32, p < 0.05 for inferotemporal vein, 0.33 vs. 0.30, p < 0.01 for inferotemporal artery, 0.34 vs. 0.31, p < 0.01 for superotemporal vein, and 0.33 vs. 0.30, p < 0.05 for superotemporal artery). From these results, an increase in CRRs of retinal vessels, as quantitatively measured from fundus photographs, could be associated with glaucoma.
Application of Computer Aided Engineering Tools in Performance Prediction and Fault Detection of Mechanical Equipment of Mining Process Line
Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.
Shot Boundary Detection Using Octagon Square Search Pattern
In this paper, a shot boundary detection method is presented using octagon square search pattern. The color, edge, motion and texture features of each frame are extracted and used in shot boundary detection. The motion feature is extracted using octagon square search pattern. Then, the transition detection method is capable of detecting the shot or non-shot boundaries in the video using the feature weight values. Experimental results are evaluated in TRECVID video test set containing various types of shot transition with lighting effects, object and camera movement within the shots. Further, this paper compares the experimental results of the proposed method with existing methods. It shows that the proposed method outperforms the state-of-art methods for shot boundary detection.
Energy Detection Based Sensing and Primary User Traffic Classification for Cognitive Radio
As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.
Fabrication of Immune-Affinity Monolithic Array for Detection of α-Fetoprotein and Carcinoembryonic Antigen
In this paper, we presented a highly sensitive immune-affinity monolithic array for detection of α-fetoprotein (AFP) and carcinoembryonic antigen (CEA). Firstly, the epoxy functionalized monolith arrays were fabricated using UV initiated copolymerization method. Scanning electron microscopy (SEM) image showed that the poly(BABEA-co-GMA) monolith exhibited a well-controlled skeletal and well-distributed porous structure. Then, AFP and CEA immune-affinity monolithic arrays were prepared by immobilization of AFP and CEA antibodies on epoxy functionalized monolith arrays. With a non-competitive immune response format, the presented AFP and CEA immune-affinity arrays were demonstrated as an inexpensive, flexible, homogeneous and stable array for detection of AFP and CEA.
Leukocyte Detection Using Image Stitching and Color Overlapping Windows
Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.
Automatic Detection of Defects in Ornamental Limestone Using Wavelets
A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.
Edge Detection Using Multi-Agent System: Evaluation on Synthetic and Medical MR Images
Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.
Active Islanding Detection Method Using Intelligent Controller
An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.
Malware Beaconing Detection by Mining Large-scale DNS Logs for Targeted Attack Identification
One of the leading problems in Cyber Security today
is the emergence of targeted attacks conducted by adversaries with
access to sophisticated tools. These attacks usually steal senior level
employee system privileges, in order to gain unauthorized access to
confidential knowledge and valuable intellectual property. Malware
used for initial compromise of the systems are sophisticated and
may target zero-day vulnerabilities. In this work we utilize common
behaviour of malware called ”beacon”, which implies that infected
hosts communicate to Command and Control servers at regular
intervals that have relatively small time variations. By analysing
such beacon activity through passive network monitoring, it is
possible to detect potential malware infections. So, we focus on
time gaps as indicators of possible C2 activity in targeted enterprise
networks. We represent DNS log files as a graph, whose vertices
are destination domains and edges are timestamps. Then by using
four periodicity detection algorithms for each pair of internal-external
communications, we check timestamp sequences to identify the
beacon activities. Finally, based on the graph structure, we infer the
existence of other infected hosts and malicious domains enrolled in
the attack activities.
A Fast Silhouette Detection Algorithm for Shadow Volumes in Augmented Reality
Real-time shadow generation in virtual environments
and Augmented Reality (AR) was always a hot topic in the last
three decades. Lots of calculation for shadow generation among AR
needs a fast algorithm to overcome this issue and to be capable of
implementing in any real-time rendering. In this paper, a silhouette
detection algorithm is presented to generate shadows for AR systems.
Δ+ algorithm is presented based on extending edges of occluders
to recognize which edges are silhouettes in the case of real-time
rendering. An accurate comparison between the proposed algorithm
and current algorithms in silhouette detection is done to show the
reduction calculation by presented algorithm. The algorithm is tested
in both virtual environments and AR systems. We think that this
algorithm has the potential to be a fundamental algorithm for shadow
generation in all complex environments.
Land Use Change Detection Using Remote Sensing and GIS
In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.
Financial Statement Fraud: The Need for a Paradigm Shift to Forensic Accounting
The unrelenting series of embarrassing audit failures should stimulate a paradigm shift in accounting. And in this age of information revolution, there is need for a constant improvement on the products or services one offers to the market in order to be relevant. This study explores the perceptions of external auditors, forensic accountants and accounting academics on whether a paradigm shift to forensic accounting can reduce financial statement frauds. Through Neo-empiricism/inductive analytical approach, findings reveal that a paradigm shift to forensic accounting might be the right step in the right direction in order to increase the chances of fraud prevention and detection in the financial statement. This research has implication on accounting education on the need to incorporate forensic accounting into present day accounting curriculum. Accounting professional bodies, accounting standard setters and accounting firms all have roles to play in incorporating forensic accounting education into accounting curriculum. Particularly, there is need to alter the ISA 240 to make the prevention and detection of frauds the responsibilities of bot those charged with the management and governance of companies and statutory auditors.
Elman Neural Network for Diagnosis of Unbalance in a Rotor-Bearing System
The operational life of rotating machines has to be extended using a predictive condition maintenance tool. Among various condition monitoring techniques, vibration analysis is most widely used technique in industry. Signals are extracted for evaluating the condition of machine; further diagnostics is carried out with detected signals to extend the life of machine. With help of detected signals, further interpretations are done to predict the occurrence of defects. To study the problem of defects, a test rig with various possibilities of defects is constructed and experiments are performed considering the unbalanced condition. Further, this paper presents an approach for fault diagnosis of unbalance condition using Elman neural network and frequency-domain vibration analysis. Amplitudes with variation in acceleration are fed to Elman neural network to classify fault or no-fault condition. The Elman network is trained, validated and tested with experimental readings. Results illustrate the effectiveness of Elman network in rotor-bearing system.
Actuator Fault Detection and Fault Tolerant Control of a Nonlinear System Using Sliding Mode Observer
In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.
HRV Analysis Based Arrhythmic Beat Detection Using kNN Classifier
Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.
Towards Integrating Statistical Color Features for Human Skin Detection
Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.