Spectral dimensionality reduction based on intergrated bispectrum phase for hyperspectral image analysis

In this paper, we propose a method to reduce spectral dimension based on the phase of integrated bispectrum. Because of the excellent and robust information extracted from the bispectrum, the proposed method can achieve high spectral classification accuracy even with low dimensional feature. The classification accuracy of bispectrum with one dimensional feature is 98.8%, whereas those of principle component analysis (PCA) and independent component analysis (ICA) are 41.2% and 63.9%, respectively. The unsupervised segmentation accuracy of bispectrum is also 20% and 40% greater than those of PCA and ICA, respectively

OpenCV Based Real-Time Video Processing Using Android Smartphone

as the smarphone industry grows rapidly, the smartphone application needs to be faster and consumes lower power because the smartphone is only powered by a battery. In this paper, two Android applications based on video processing method are introduced; one by using OpenCV library, the other one is using Android library with self-implemented algorithm called CamTest. Eight image processing methods are applied to each frame of the video captured from the Android smartphone. The smartphone used in this study is the Samsung Galaxy S, with Android 2.3 Gingerbread Operating System. The efficiencies and power consumptions of the two applications are compared by observing their frame processing rate and power consumption. The experimental results show that out of the eight image processing methods, six methods that executed using OpenCV library are faster than that of CamTest with a total average ratio of 0.41. For the power consumption per frame test, six methods that executed using OpenCV library consume less power than that of CamTest with a total average ratio of 0.39

ANALYSIS OF REAL-TIME OBJECT DETECTION METHODS FOR ANDROID SMARTPHONE

This paper presents the analysis of real-time object detection method for embedded system, especially the Android smartphone. As we all know, object detection algorithm is a complicated algorithm that consumes high performance hardware to execute the algorithm in real time. However due to the development of embedded hardware and object detection algorithm, current embedded device may be able to execute the object detection algorithm in real-time. In this study, we analyze the best object detection algorithm with respect to efficiency, quality and robustness of the object detection. A lot of object detection algorithms have been compared such as Scale Invariant Feature Transform (SIFT), Speeded-Up Feature Transform (SuRF), Center Surrounded Extrema (CenSurE), Good Features To Track (GFTT), Maximally- Stable Extremal Region Extractor (MSER), Oriented Binary Robust Independent Elementary Features (ORB), and Features from Accelerated Segment Test (FAST) on the GalaxyS Android smartphone. The results show that FAST algorithm has the best combination of speed and object detection performance

Measuring Power Consumption for Image Processing on Android Smartphone

The energy consumption of smartphones can be undertaken in multiple levels of hardware and software. Generally, there are two approaches in measuring power consumption of a smartphone application which are the measurement-based and estimation-based methods. The goal of this study is to compare the two power consumption measuring approaches in quantifying the power consumed by image processing applications in Android smartphone. For measurement-based approach, a simple wattmeter is designed whereas for the estimation-based approach, an Android application called the PowerTutor will be utilized. The wattmeter and PowerTutor will measure the power consumption of eight image processing methods running on modified Android library with self implemented algorithm called the CamTest. According to t-test analysis that has been conducted, the p values of all of the image processing methods show that there are no significant differences between the wattmeter and the PowerTutor application (p>0.01). Even though measurement based method is more accurate than estimation-based method in term of measuring power consumption, PowerTutor application proved it provides accurate, real-time power consumption estimation for Android platform smartphones. Application developers still can use PowerTutor as an option to determine the impact of software design on power consumption

Target Detection of Hyperspectral Images Based on Their Fourier Spectral Features

Original spectral features contain information pertinent to certain target spectral features. Without an efficient spectral feature extraction method, the target detection performance might be degraded. We present spectral feature extraction techniques based on the Fourier domain for use in target detection. These feature extraction methods are the Fourier magnitude (FM), Fourier phase (FP), and Fourier coefficient selection (FCS)methods. In our target detection experiments, we compared the proposed methods to the principle component analysis (PCA) and independent component analysis (ICA) methods and the original spectral features. The experiment results show that the FCS target detection accuracy is 95.75%, whereas the accuracies of the FM, FP, PCA, ICA methods,and the original spectral features are 86.76%, 36.28%, 84.51%, 74.49%,and 78.92%, respectively. The average feature extraction times of the proposed methods are 223% faster than that found for the PCA and 304% faster than the ICA methods

Comparison of Feature Extractors for Real-Time Object Detection on Android Smartphone

This paper presents the analysis of real-time object detection method for embedded system particularly the Android smartphone. As we all know, object detection algorithm is a complicated algorithm that consumes high performance hardware to execute the algorithm in real time. However due to the development of embedded hardware and object detection algorithm, current embedded device may be able to execute the object detection algorithm in real-time. In this study, we analyze the best object detection algorithm with respect to efficiency, quality and robustness of the algorithm. Several object detection algorithms have been compared such as Scale Invariant Feature Transform (SIFT), Speeded-Up Feature Transform (SuRF), Center Surrounded External (CenSurE), Good Features To Track (GFTT), Maximally-Stable External Region Extractor (MSER), Oriented Binary Robust Independent Elementary Features (ORB), and Features from Accelerated Segment Test (FAST) on the GalaxyS Android smartphone. The results show that FAST algorithm has the best combination of speed and object detection performance

Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans

Texture feature is an important feature analysis method in computer-aided diagnosis systems for disease diagnosis. However, texture feature itself could not provide an overall description of the diseases. In this paper, we propose Continuous Local Feature (CLH) to diagnose the Bronchiolitis Obliterans (BO) lung diseases in the chest computer tomography images. CLH is based on the continuous combination of histograms of local texture feature, local shape feature, and the brightness feature. Because CLH extracts more information, it has high discriminating power and is able to classify between the BO lung disease and normal lung region effectively. The experimental results in classifying between BO and normal lung region show that CLH achieves 98.15% of average sensitivity whereas Local Binary Patterns and Gray Level Run Length Matrix achieve 73% and 75.8% of average sensitivities, respectively. In the receiver operating curve analysis, CLH archives 0.9 of area under curve (AUC) whereas LBP and GLRLM achieve 0.78 and 0.86 of AUCs

Real-Time Video Processing Using Native Programming on Android Platform

As the smartphone industry grows rapidly, smartphone applications need to be faster and real-time. For this purpose, most of the smartphone platforms run the program on the native language or compiler that can produce native code for hardware. However for the Android platform that based on the JAVA language, most of the software algorithm is running on JAVA that consumes more time to be compiled. In this paper the performance of native programming and high level programming using JAVA are compared with respect to video processing speed. Eight image processing methods are applied to each frame of the video captured from a smartphone that is running on an Android platform. The efficiencies of the two applications with difference programming language are compared by observing their frame processing rate. The experimental results show that out of the eight images processing methods, six methods that are executed using the native programming are faster than that of the JAVA programming with a total average ratio of 0.41. An application of the native programming for real-time object detection is also presented in this paper. The result shows that with native programming on Android platform, even a complicated object detection algorithm can be done in real-time

The Pruning of Combined Neighborhood Differences Texture Descriptor for Multispectral Image Segmentation

This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation by pruning the two dimensional texture feature named combine neighborhood differences. In contrast with the original CND, which is applied with traditional image, the pruned CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to the codeword to form another unique value. These values are then grouped to construct the multiband CND feature image where is used in the unsupervised segmentation. Experimental results, with respect to segmentation and classification accuracy using two LANDSAT multispectral images test suite have been performed. The result shows that the pruned CND feature outperforms spectral feature, with average classification accuracies of 87.55% whereas that of spectral feature is 55.81%

Feasibility of N1-P2 Habituation to Differentiate Loudness Levels

In the present study, the feasibility of habituation correlates of N1-P2 component of late auditory evoked potential to differentiate loudness levels was investigated. In ten normal hearing subjects, it was shown that habituation correlates of N1- P2 is able to differentiate between acceptable loudness levels (comfortable loudness and comfortable but loud) and strong and high loudness levels (loud, upper level and uncomfortable loudness level (UCL)). Therefore, the proposed approach is promising for the development of objective setting method for hearing devices, especially to estimate the level of UCL