The sea background often fluctuates violently and has a low contrast with the target, which brings difficulties in detecting the infrared maritime targets. To solve this problem, the mixture Gaussian background modeling for sea background in the Fourier domain (FGMM) was proposed. First, the mixture Gaussian background model was constructed for the amplitude spectrum sequence at each frequency point. Second, the amplitude spectrum of the test frame was compared with the mixture Gaussian background model to separate the background and foreground frequency points. And the parameters of each Gaussian distribution were updated to adapt to the change of seawater. Also, the two features of the neighborhood amplitude spectrum contrast and the information entropy of local amplitude spectrum were fused into the mixture Gaussian background model to get the final detection results. Experimental results showed that the proposed method has good effects in suppressing the seawater and detecting the targets. Moreover, compared with the traditional spatial mixture Gaussian background modeling algorithm, its performance has been significantly improved.
Occasionally, stripe noise infiltrates infrared images during acquisition, causing the image quality to severely deteriorate and affecting subsequent image analysis. To remove stripe noise, an adaptive comb-type notch filtering method is proposed, and a spatial mathematical stripe noise model and the comb-like impulse spectrum characteristics of stripe noise are theoretically and experimentally analyzed. The analysis results demonstrate that when periodic stripe noise consists of only intact stripes, the amplitude spectrum of the periodic stripe noise is an ideal impulse comb spectrum, and the noise frequency energy is concentrated; by contrast, when the periodic stripe noise contains incomplete stripes, spectral leakage occurs, and the noise frequency energy spreads to all frequencies. On this basis, optimal methods of estimating the stripe width and the size of the subimages to be processed are also presented. First, we crop the original image into two subimages of the optimal size to focus the comb-like impulse spectrum characteristics of the stripe noise, thereby enabling easier removal of the stripe noise patterns in the subimages. Then we detect the peaks in the amplitude spectra of the subimages based on the characteristics of the comb-like impulse spectrum and eliminate those peaks using an adaptive comb-type notch filter. Finally, we fuse the two filtered subimages into a final image. Experimental results show that the proposed method achieves a good effect in removing stripe noise from infrared images.
Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.
Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.
For ship targets detection in cluttered infrared image sequences, a robust detection method, based on the probabilistic single Gaussian model of sea background in Fourier domain, is put forward. The amplitude spectrum sequences at each frequency point of the pure seawater images in Fourier domain, being more stable than the gray value sequences of each background pixel in the spatial domain, are regarded as a Gaussian model. Next, a probability weighted matrix is built based on the stability of the pure seawater’s total energy spectrum in the row direction, to make the Gaussian model more accurate. Then, the foreground frequency points are separated from the background frequency points by the model. Finally, the false-alarm points are removed utilizing ships’ shape features. The performance of the proposed method is tested by visual and quantitative comparisons with others.
Background modeling is the critical technology to detect the moving target for video surveillance. Most background modeling techniques are aimed at land monitoring and operated in the spatial domain. A background establishment becomes difficult when the scene is a complex fluctuating sea surface. In this paper, the background stability and separability between target are analyzed deeply in the discrete cosine transform (DCT) domain, on this basis, we propose a background modeling method. The proposed method models each frequency point as a single Gaussian model to represent background, and the target is extracted by suppressing the background coefficients. Experimental results show that our approach can establish an accurate background model for seawater, and the detection results outperform other background modeling methods in the spatial domain.
In the linear discriminative analysis, especially in the high dimension case, it is insufficient to project the data onto a
one-dimensional subspace for the two-category classification problem. Therefor a weak component approach (WCA)
was proposed to project patterns to a low dimensional subspace with rich number of classification features. The role of
the weak component in pattern classification was discussed. And the abundance of discriminative information contained
in weak components was explored. Firstly, a definition of the weak component was given. Secondly, an improved
regularization method was proposed. The regularization is a biased estimate of the variance in the corresponding
dimension of the training data and the population data. Then a construction method of the feature subspace based on
weak component was given, which extracts the eigenvector of the scatter matrixes according to their discriminative
information. Finally, the proposed approach was validated in the experiments by comparing it with LDA. A better
classification accuracy of the presented method was achieved. As WCA extracts the dims on which the data distributes
closer, it is applicable to the high-dimensional data which distributes elliptically.
KEYWORDS: Wavelets, Bismuth, Terahertz radiation, Signal processing, Wavelet transforms, Statistical analysis, Signal analyzers, Data storage, Spectroscopy, Refractive index
A THz time-domain spectroscopy (THz-TDS) pulse signal is a temporal response of THz reference pulse. Although the
field of THz-TDS signal processing and analysis techniques is relatively unexplored, work has been reported in this
field. One of those is wavelet analysis approach of terahertz signals. It has been shown that the wavelet transform is an
efficient representation of THz pulses due to their pulse-like nature. Unlike Fourier analysis, which only uses infinite
sinusoids as the basis functions, in wavelet analysis, there are a large number of wavelet bases for different applications,
and each of these wavelet bases exhibits different properties. In this paper, the problem that how to select an appropriate
wavelet basis for representation and analysis of THz-TDS signals is discussed by lots of comparing experiments. Three
criterions, which are wavelet basis efficiency index (WBEI), pulse spectral relative entropy (PSRE) and pulse spectral
cumulative error (PSCE), are presented to determine a preferable mother wavelet for a given THz-TDS reference pulse.
A novel visualization method of terahertz time-domain spectroscopy (THz-TDS) image is presented, which is based on
principal component analysis (PCA) technique. The proposed method include three processing steps: firstly, the THz-
TDS image is preprocessed using a spatial vector filtering technique to denoise. Secondly, the THz-TDS image is
transformed from spatio-temporal domain to spatio-spectral domain, and the transformed image can be viewed as a
multispectral image whose spectral dimensionality D is equal to the sampled number of THz-TDS pulse at each pixel.
Thirdly, each of spectrum vector at a pixel is viewed as a point in D dimensional space, the covariance matrix of pixels
can be computed, and then three eigenvectors corresponding to the first 3 largest eigenvalues are found by PCA
technique. the THz-TDS image is projected along these three eigenvectors. By normalizing these 3 principal component
images and mapping them into the RGB space, we can get a synthetic color image as a visualization result of the THz-
TDS image. Due to vector-based dimensionality reduction, the proposed method can provide more visual information of
the THz-TDS image than scalar-based visualization techniques. Finally, experimental results are provided to demonstrate
the performance of the proposed method.
This paper proposes a new data structure for GIS image data (wavelet pyramid structure) based on integer wavelet transform. With this structure, an original image is divided into an image of the lowest resolution and two groups of image detail coefficients. These coefficients are organized by a pyramid structure-wavelet pyramid structure. In comparison with R-trees, this structure can reduce data redundance and the original image can be restored perfectly and losslessly with this method.
Standard Mutual Information function contains local maxima, which make against to convergence of registration transformation parameters for automated multimodality image registration problems. We proposed Feature Potential Mutual Information (FPMI) to increases the smoothness of the registration measure function and use Particle Swarm Optimization to search the optimal registration transformation parameter in this paper. At first, Edges of images are detected. Next, edge feature potential is defined by expanding edges to the neighborhood region using potential function. Each edge point influences the whole potential field, just like the particle of physics in the gravitation field space. FPMI is computed on the edge feature potential of two images. It substitutes the edge feature potential values for gray values in images. It can avoid great change of joint probability distribution and has less local maxima. The registration accuracy of FPMI is analyzed under different edge detection cases. It is shown that the registration accuracy of FPMI is more accurate and more robust than that of MI. Maximization of FPMI is done by PSO. PSO combines local search methods with global search methods, attempting to balance exploration and exploitation. Its complex behavior follows from a few simple rules and has less computational complexity. Multimodal medical images are used to compare the response of FPMI and MI to translation and rotation. Experiments show that FPMI is smoother and has less local fluctuations than that of MI. Registration results show that PSO do it better than Powell’s method to search the optimal registration parameters.
A new technique is developed for the data fusion of multispectral image and panchromatic image. The intensity component of fusion image is modified by combine multispectral information and high-resolution information, which is determined by image edge intensity. The fusion image is reconstructed by means of the inverse IHS transform. Experiment comparison shows that our method performing better in preserving spatial resolutions and color content than that of traditional IHS transform technique and wavelet transform fusion method.
The weighting exponent m is an important parameter in fuzzy c-means (FCM) algorithm. In this paper, three basic problems about m in FCM algorithm: clustering validity method based on optimal m (or whether does optimal m exist), how does m effect on the performance of fuzzy clustering, and which is the proper range of m in general applications, are studied with the knee of objective function Jm, and fuzzy decision-making methods. Numerical experimental results show that the optimal m* for specific data set does exist. Moreover, a group of numerical experimental results indicate that, within the range of m (epsilon) (1.5, 3.5), the optimal m* monotone increase linearly against the separability (rho) of data set. So in practical applications, one can choose the value of m within the range of [1.5, 3.5].
In this paper, a fuzzy matching algorithm for recognizing primitives in hand-drawn graphical symbols is presented. By primitives we mean the frequently used sub-graphic units in the certain graphic symbols set. The recognition process is performed through local and global relation calculation, fuzzy rules are adopted in the process to describe the relation between basic geometric lines. As a middle layer of a hand-drawn graphic symbol recognition system, primitives recognition can greatly reduce the searching space of graphic symbols matching and improve the performance of whole system.
This paper first describes the effect of the fixed pattern noise and limits of some important IR Image systems to it. The source of the fixed pattern noise is explored and the output function of the infrared detection element is derived. Finally, the method of correction for the fixed pattern noise according to this function is developed and some photos of corrected result are given.
Key words IRCCD, opto—electronic imaging, noise correction
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