A digital watermarking technique is a specific branch of steganography, which can be used in various applications,
provides a novel way to solve security problems for multimedia information. In this paper, we proposed a kind of
wavelet domain adaptive image digital watermarking method using chaotic stream encrypt and human eye visual
property. The secret information that can be seen as a watermarking is hidden into a host image, which can be publicly
accessed, so the transportation of the secret information will not attract the attention of illegal receiver. The experimental
results show that the method is invisible and robust against some image processing.
Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support
Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect targets. Image
intensity surfaces for the neighborhood of every pixel of the original long-wave infrared image and mid-wave infrared
are well-fitted by mapped LS-SVM respectively. And long-wave and mid-wave infrared image gradient images are
obtained by LS-SVM based on radial basis kernels function. Fusion rule is set up according to the features of gradient
images. At last, segment fused image and targets can be detected with contrast threshold. Compared with wavelet fusion
detection algorithm and morphological fusion detection algorithm, when a target is affected by baits, the experimental
results demonstrate that the proposed approach in the paper based on LS-SVM to fuse and detect weak and small target
is reliable and efficient.
There are two ways for transmitting data in a secure manner: Cryptography and steganography. Digital watermarking is a
specific branch of steganography, which can be used in various applications, including covert communication, owner
identification, authentication and copy control. In this paper, we proposed a blind adaptive watermarking algorithm
based on HVS is proper for covert communication. The secret information that can be seen as a watermarking is hidden
into a host image, which can be publicly accessed, so the transportation of the secret information will not attract the
attention of illegal receiver. With our approach, the secret information is embedded in the wavelet domain. By the
background luminance and the texture mask characters of HVS, we divide the wavelet coefficients of carrier image into
different classes. According to the classes of the wavelet coefficients the watermark image is embedded. The result of
our experimental shows that this approach is imperceptible and robust some image processing such as JPEG lossy
compression, cropping, median filtering, grads sharpening, Gaussian white noise attacks and so on.
The paper puts forward a fractal dimension algorithm that fuses mid-wave and long-wave infrared images and detects targets. Usually, the targets in infrared images are man-made, and their fractal dimensions are different from that of their natural background. In the fractal dimension algorithm, the source images are first decomposed by wavelet transformation. Then in the wavelet transformation domain, fractal dimensions are calculated and fusion rules that merge corresponding sub images of two matching source images are set up, and new sub images are created. Finally, the image is reconstructed by inverse wavelet transformation and a fused image is obtained. The fusion results have shown that the contrast between the targets and their background has changed significantly, and the targets can be easily detected by applying a contrast threshold. The experimental results have shown that the method using fractal dimension to fuse dualband infrared images and detect targets is superior to the one using mid-wave or longwave infrared images to detect targets alone.
Airborne light detection and ranging (LIDAR) data filtering is the most time-consuming and expensive part in applications related to laser scanning. This paper proposed a fast facet-based LIDAR data filtering method. LIDAR point clouds are interpolated onto a regular grid, and the filtering of nonground points is implemented on the grid-based data. The simple, quadratic, and cubic facet models, which are respectively, based on the zero, second, and third orders of orthogonal polynomials, are used to estimate the underlying elevation surface trend, which is considered as the approximation of ground surface. As the ground measurements are generally below the objects, the nonground points are filtered by removing the points that are higher than the estimated elevation surface trend. The resulting holes are filled with the nearest remaining measurements. Iteratively filtering in this way, the estimated elevation surface trend converges at the real ground surface. The nonground points that are higher than the finally approximated ground surface are filtered and the ground points are extracted from the LIDAR data. Experimental results on the test data released from the International Society for Photogrammetry and Remote Sensing (ISPRS) demonstrate that the proposed approach is efficient and provides at least comparable performance with the accuracy reports published by ISPRS.
Automatic feature extraction for road information plays a central role in applications related to terrains. In this paper, we propose a new road extraction method using the one-class support vector machine (SVM). For a manually segmented seed road region, only a part of pixels are really road, some pixels locating on the sideway, shadows of the building, and the cars etc., are not really road pixels. The one-class SVM is used to estimate a decision function that takes the value +1 in a small feature region capturing most of the data points in the seed road area, and -1 elsewhere. Since the road pixels in the satellite image have the similar properties, such as the spectral feature in multi-spectral image, the novelty pixel is discriminated by the estimated decision function for road segmentation. Many computation experiments are undertaken on the IKONOS high resolution image. The results demonstrate that the proposed method is effective and has much higher computation efficiency than the standard pixel-based SVM classification method.
A new noninvasive measurement of oxygen contents in hepatic tissues using near-infrared technique according to
physiological characteristics is proposed. The procedure can be divided into three categories. First a quantitative formula
is introduced to measure oxygen contents in hepatic tissues based on the relationship between absorption coefficient and
typical wavelengths, where 760nm and 850nm infrared wavebands are utilized in this paper. Second, many
characteristics such as waveforms of oxygen contents in hepatic tissues, cross correlation of blood-oxygen and power
spectrum of oxygen contents, are analyzed detailedly with regard to different symptoms in hepatic tissues. Finally, a
conclusion can be drawn that waveforms of oxygen contents, cross correlation and power spectrum are three main
features, which can well depict the symptoms of hepatic tissues. The proposed method is applied to examine 143 people,
including 40 normal people and 103 patients with different symptoms in hepatic tissues. The false probability is 8.3%
and the missing probability is 13.7% under specified criterion. The clinical experiments show that our proposed method
is simple but effective and can be used to routine examinations or intensive care units for liverish patients.
A novel method for fusion detection of small infrared targets based on support vector machines (SVM) in the wavelet domain is presented. Target detection task plays an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. SVM is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. Least-squares support vector machines (LS-SVMs) are reformulations to standard SVMs. The proposed algorithm can be divided into four steps. First, each frame of the image sequence is decomposed by the discrete wavelet frame (DWF). Second, the components with low frequency are performed by regression based on LS-SVM. The one-order partial derivatives in row and column directions are derived. Therefore, feature images of the gradient strength can be obtained. Third, feature images of five consecutive frames are fused to accumulate the energy of target of interest and greatly reduce false alarms. Finally, the segmentation method based on contrast between target and background is utilized to extract the target. In terms of connectivity of moving targets, the majority of residual clutter and false alarms that survive are removed based on 3-D morphological dilation across three consecutive frames along the motion direction of the moving targets. Actual infrared image sequences in backgrounds of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.
Efficient line-based very large scale integration architectures for the 2-D discrete wavelet transform (DWT) based on a lifting scheme, using the 9/7 wavelet filters adopted in the JPEG 2000 proposal, are proposed. The embedded decimation technique based on folding and time multiplexing was exploited to optimize the architecture, which reduces the size of buffer memory required and the amount of RAM access, and hence the occupied area and power consumption of the devices. Using this technique, a single-input, single-output architecture (SISOA) and a two-input, two-output architecture (TITOA) are proposed. The presented SISOA is designed to generate one output per clock cycle; the TITOA is designed to generate two outputs per clock cycle with the same memory requirement as that for SISOA, where the four subband coefficients of the transformed signal are available interleaved. Because only one line of data is required at a time, a single-port memory can be used. Performance analysis and comparison results demonstrate that the proposed method is economical of hardware cost and computation time. The advantages of the design also include short output latency, simple data flow, regularity, and scalability, as well as suitability for VLSI implementation.
Detection of dim moving small targets at low signal noise ratio is a very important issue and difficult problem in infrared searching and tracking system. Based on analysis of the character of infrared images, a new double energy accumulating method is proposed. Firstly, images are denoised by wavelet transformation with soft threshold. Then, object motion area is detected according to difference images and the target intensity is well enhanced by accumulating energy two times with addition and product operation. Finally, target candidates are separated from background by thresholding process with the selected threshold. Computer experiments are carried out with an infrared image sequence and the experimental results illustrate that the proposed method is effective and efficient.
Walsh-Haar function system that was first intruoduced by us is a new kind of function systems, and has a good global/local property. This function system is called Walsh ordering function system since its generation kernel functions belong to Walsh ordering Walsh function system. We worked out a recursive property of the matrix corresponding to the first Walsh-Haar functions in Walsh-Haar function system, and we also proved that Walsh-Haar function system is perfect and orthogonal similar to Walsh function system and Haar function system. Thus, discrete Walsh-Haar transformation (DW-HT) is an orthogonal transformation that can be widely used in signal processing. In this paper, using the recursive property of the matrix and the fast algorithm of discrete Walsh transformation (DWT) in Walsh ordering, we have designed a fast algorithm of Walsh ordering DW-HT based on the bisection technique. The idea and method used in this paper can be used for designing fast algorithms of other ordering DW-HTs and other discrete orthogonal transformations.
Speckle noise in synthetic aperture radar (SAR) images is characterized as multiplicative random noise. To address SAR image speckle denoising, this paper proposes a new method which is based on the combination of statistical model of wavelet coefficients and modification to the coefficients according to module-maximum-based (significant coefficient) rule. In our method, wavelet coefficients of image are firstly modeled as mixture density of two Gaussian (MG) distributions with zero mean. In order to incorporate the spatial dependencies into the denoising procedure, hidden markov tree (HMT) model is explored and expectation maximization (EM) algorithm is proposed to estimate model parameters. Bayes minimum mean square error (Bayes MMSE) method is used to estimate the wavelet coefficients free of noise. The wavelet coefficients are updated according to a rule whether the coefficient is a significant one or not. 2D inverse DWT is performed on the updated coefficients to get denoised SAR image. Experimental Results using real SAR image demonstrate that the method can not only reduce the speckle but also preserve edges and radiometric scatter points. Equivalent Number of Look Enl shows that the proposed method yields very satisfactory results compared with other methods.
Target detection techniques play an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. In this paper, a novel method for fusion detection of infrared weak targets based on multifeature distance map (MFDM) in image sequences is proposed. As for small weak targets, there are many features, such as local entropy, average gradient strength. These features depict the characteristics of small infrared targets and can be extracted. Multifeature-based fusion techniques are applied to detect such weak targets. The problem of detecting small targets is converted to search peak values in specified feature space where multifeature vectors space (MFVS) is considered. Distance map (DM) can be derived according to feature vectors and target detection is performed in DM. In order to accumulate energy of targets deeply and suppress background and clutters to a great extent, five distance maps obtained by corresponding five consecutive frames are utilized to fuse with average weight, which results in the fact that the contrast between targets and background including clutters are enlarged and that the feature peaks of targets are obvious different from background and clutters. After these steps, a contrast segmentation method is used to extract targets from complicated background on the fused DM. Actual infrared image sequences in background of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.
Based on statistical learning theory, support vector machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. The mapped least squares support vector machine (MLS-SVM) is a special least square SVM (LS-SVM), which extends the application of the SVM to the image processing. Based on the MLS-SVM, a family of filters for the approximation of partial derivatives of the digital image surface is designed. Prior information (e.g., local dominant orientation) are incorporated in a two dimension weighted function. The weighted MLS-SVM with the radial basis function kernel is applied to design the proposed filters. Exemplary application of the proposed filters to fingerprint image segmentation is also presented.
Among the variety of approaches proposed in literature, we can clearly distinguish the Wiener filter and the wavelet transform based ones for their effectiveness and, in many cases, simplicity. By exploiting the characteristics of both wavelet thresholding denoising and spatial Wiener filtering, the paper presents a combined scheme for the noise removal in images. We first perform thresholding denoising in wavelet domain to obtain a pre-denoised image, then spatial adaptive Wiener filter, i.e. Lee filtering, is used to increase the quality of the image restored. The crux of our method lies in the simple yet effective estimation of the optimal noise variance for Lee filter. By numerical computation, this optimal noise variance of Lee filter is presented which can nearly minimize the mean square error (MSE) of the pre-denoised image. Experiment results show that mean square error and signal-to-noise ratio (SNR) of our combined denoising approach have been improved, compared with the denoising solely in wavelet or spatial domain.
A new wavelet fusion method based on local energy and local entropy is presented. First, multiresolution decomposition
of source images are obtained by discrete wavelet transform. Second, the corresponding sub-band images by using
different rules are fused, that is, the method based on local energy is used in low frequency components while high
frequency components including horizontal, vertical and diagonal details are fused by local entropy. Finally, the fused
image is obtained by inverse discrete wavelet transform. This method is tested by fusing two groups of images from the
same scene. One group is the images with different parts focused by Gaussian blur, while the other is visible light image
and far-infrared image. The experimental results prove the novel method to be applicable, efficient and promising.
The goal of image fusion is to create new images that are more suitable for the purposes of human visual perception, object detection and target recognition. For Automatic Target Recognition (ATR), we can use multi-sensor data including visible and infrared images to increase the recognition rate. In this paper, we propose a new multiresolution data fusion scheme based on Daubechies Wavelet Basis (DWB) and pixel-level weights including thermal weights and visual weights. We use multiresolution decompositions to represent the input images at different scales, present a multiresolution/multimodal segmentation to partition the image domain at these scales. The crucial idea is to use this segmentation to guide the fusion process. Physical thermal weights and perceptive visual weights are used as segmentation multimodals. Daubechies Wavelet (at different levels) is choosen as the Wavelet Basis. Experimental results confirm that the proposed algorithm is the best image sharpening method and can best maintain the spectral information of the original infrared image. Also, the proposed technique performs better than the other ones in the literature, more robust and effective, from both subjective visual effects and objective statistical analysis results.
Forward Looking Infra-Red (FLIR) image segmentation is crucial for Automatic Target Recognition (ATR). This paper presents a thresholding method for image segmentation by performing fuzzy partition on a two-dimensional (2-D) histogram based on maximum entropy principle. We combine the original image with its smooth image to form a binary set, called a "generalized image", and the histogram of the generalized image is a 2-D histogram. In order to adequately utilize the intrinsic information of the FLIR image, we adopt a newly defined fuzzy partition of two fuzzy sets, dark and bright, basing on 2-D histogram. Also we define the corresponding 2-D membership function, which represents the membership of darkness and brightness for each element in the binary set, respectively. The entropy is used as a measure of fuzziness. Based on the Shannon function, we define a 2-D fuzzy entropy. The total fuzzy entropy is the sum of the entropy of each block. Therefore, the fuzzy region can be determined by maximizing the total fuzzy entropy. A genetic algorithm is employed to find the optimal combination of all the fuzzy parameters. Experiment results show that the proposed method gives good performance.
Star acquisition is one of the most time-consuming routines in star-tracker operation. In the star image, a star point spread function (PSF) represents a near-Gaussian distribution. The star extraction consists in finding the highest-intensity pixel among the PSFs, collecting the adjacent pixels, and then calculating the star centroids in the star image plane. The candidate highest-intensity pixels are the maximum extremum points of the underlying intensity function of a digital star image. To extract star from the star image, the cubic facet model is applied to fit the underlying intensity surface in star acquisition procedure. A new extraction approach, using surface-fitting methods to approximate locally the image intensity function, and then using the partial derivatives of the fitted surface to make decisions regarding the maximum extremum points, is proposed. A number of experiments are carried out on simulated star images. The experimental results demonstrate that the proposed method is efficient and robust.
In this paper, we firstly present turbo product codes (TPCs) for forward error correction (FEC) coding, including TPCs encoding process and decoding principle, and then compare TPCs with turbo convolutional codes (TCCs) error coding solution. The performance of TPCs is shown to be closer to the Shannon limit than TCCs. Secondly, we introduce TPCs’ application in the 4th generation (4G) mobile communication system which is being developed in our country at present. The concept of TPC-OFDM system which promises higher code rate than conventional OFDM is first modified. Finally, simulation results show that the simplified 4G uplink systems offer Bit Error Rate of nearly 0 over IMT-2000 channel at Eb/N0 > 15dB.
We describe a novel interpolation algorithm to find the optimal image intensity function generating an optimal gray-level estimation of interpolated pixels of digital images. The new approach is based on the proposed image block mapping method and least-square support vector machines (LSSVM) with Gaussian radial basis function (RBF) kernels. With the mapping technique, the interpolation procedure of the LSSVM is actually accomplished in the same input vector space. A number of different scale interpolation experiments are carried out. The experimental results demonstrate that the performance of the proposed algorithm is competitive with many other existing methods, such as cubic, spline, and linear methods. The peak signal-to-noise ratio of the image reconstructed by the proposed algorithm is higher than those obtained by the spline. And the estimated accuracy of the proposed algorithm is similar to that of the cubic algorithm, while the computational requirement is lower than the latter.
In this paper, an efficient VLSI architecture for biorthogonal 9/7 wavelet transform by lifting scheme is presented. The proposed architecture has many advantages including, symmetrical forward and inverse wavelet transform as a result of adopting pipeline parallel technique, as well as area and power efficient because of the decrease in the amount of memory required together with the reduction in the number of read/write accesses on account of using embedded boundary data-extension technique. We have developed a behavioral Verilog HDL model of the proposed architecture, which simulation results match exactly that of the Matlab code simulations. The design has been synthesized into XILINX xcv50e-cs144-8, and the estimated frequency is 100MHz.
In this paper a FLIR image segmentation algorithm based on genetic algorithm and fuzzy set theory was presented. Image processing has to deal with many ambigious situations. Fuzzy set theory is a useful mathematical tool for handling the ambiguity or uncertainty. A fuzzy entropy is a functional on fuzzy sets that becomes smaller when the sharpness of its argument fuzzy set is improved. The paper defined different member function for the object and background of the image to transform the image into fuzzy domain and chose Z-function and S-function as the membership functions for the object and background of the image respectively and threshold the image into the object and background by maximizing the fuzzy entropy. The procedure for finding combination of a, b and c is implemented by genetic algorithm with appropriate coding method to avoid useless chromosomes. The experiment results show that our proposed method gives better performance than other general methods with good real-time by using genetic algorithm.
A novel contour-based 3D terrain matching method is presented in this paper. In the method, Iso-Elevation Contour Map (IECM), a compact feature-based representation, is proposed to represent the reference DEM and recovered DEM(REM) from real-time data to convert 3D terrain matching to contour-based matching. In the contour-based matching, a normalized wavelet descriptor, which is invariant to 2D rigit transformation, is employed to describe contours. A very fast contour-matching algorithm based on normalized wavelet descriptor is presented. The proposed matching method is robust and effective computation, and can achieve high location accuracy.
A new general method of the automatic selection of guide star, which based on a new dynamic Visual Magnitude Threshold (VMT) hyper-plane and the Support Vector Machines (SVM), is introduced. The high dimensional nonlinear VMT plane can be easily obtained by using the SVM, then the guide star sets are generated by the SVM classifier. The experiment results demonstrate that the catalog obtained by the proposed algorithm has a lot of advantages including, fewer total numbers, smaller catalog size and better distribution uniformity.
This paper presents a new method to simulate virtual ocean wave surface. One of the widely used methods for simulating ocean wave is making use of wind-wave spectrums, which is mostly based on linear wave theories. The ocean waves produced in this way can reflect the statistical characteristics of the real ocean well, on the other hand the waves does not look like actual ocean surface, they just look like superposition of sine/cosine curves. In order to overcome this shortcoming of traditional method, the new method proposed in this paper take account of the effect of the random wind velocity field over ocean surface. In practice, this method can simulate the natural environment of ocean more accurately than traditional method; in theory, the method increases the nonlinear factors of ocean waves. The virtual ocean wave simulated by this way is not only accord with statistical characteristics, but also looks like real ocean wave, it can be widely used in VR applications.
According to characteristic of image wavelet transform and interpolation, this paper proposes a remote sensing image interpolation method combining wavelet transform and interpolation algorithm, which can improve the remote sensing image resolution. Experiments show that the algorithm can properly retain abundant high frequency information in original remote sensing image. After interpolation processing and wavelet reconstruction, we can obtain a remote sensing image with higher resolution, better visual effect, higher Signal Noise Ratio (SNR), more detail information and no apparent warp. Therefore, this algorithm is an effective method of super-resolution remote sensing image processing.
Based on lifting scheme and the construction theorem of the integer Haar wavelet and biorthogonal wavelet, we propose a new integer wavelet transform construct method on the basis of lift scheme after introduciton of constructing specific-demand biorthogonal wavelet transform using Harr wavelet and Lazy wavelet. In this paper, we represent the method and algorithm of the lifting scheme, and we also give mathematical formulation on this method and experimental results as well.
Terrain visualization plays an important role in the fast growing domain of Geographic Information System (GIS) and Virtual Reality (VR). How to render terrain in real-time is a hot issue in recent years, and many algorithms of terrain refinement and simplification have appeared. In this paper, an improved terrain refinement method is represented. This algorithm is based on quadtree triangulation. We enhance the sorting and triangulating process. The algorithm adopts view dependent error metric and view frustum culling to acquire considerable better performance. By implementing the algorithm and analyzing the results from experiment, we conclude that this algorithm is practical and applicative in terrain visualization system.
3D surface reconstruction is one of the hotspots in Computer Vision and Remote Sensing. Relative orientation is a important part of 3D surface reconstruction. The traditional method that finding the parameters of relative oriented model is Newton Method, but it is highly sensitive to the initial values of parameters. To overcome this shortage, we give a method of using penalty function to find the relative oriented model in this paper.
The paper presents a novel technique for stereo matching. According to the anisotropic character of human visual system, the original stereo images are decomposed into several directional images. The primitive extraction, description and matching are performed parallel in each pair of directional images. Since the information in each directional image belongs to only a limited interval of direction, the matching in each directional image is rather precise and robust. The process is more consistent with the physiology and it directly leads to extensions of Marr-Poggio theory in several aspects. The experimental results have shown the efficiency of the stereo technique.
In this paper, wp cast stereo matching as a problem in merit maximization. This is achieved by the formulation of a merit function which influence the similarity between primitives in the right and left images and the mutual dependency between primitives. Stereo matching are done by finding the "best" paths that maximize the merit function. This is handled by using dynamic programming technique. With this algorithm, a global optimum matching can be obtained. We give a mathematical description for the merit function and the algorithm has been implemented. The experimental results are presented to show the efficacy of the proposed stereo matching method.
Passively sensing three-dimensional structure by means of computational stereo has received a great deal of attention in the computer vision community as well as in the traditional photogrammetric and remote sensing communities. The first and most difficult step in recovering 3-D information from a pair of stereo images is that of matching points from one image of the pair to the corresponding points in the second image. In this paper we develop an edge-based, fast and effective stereo matching technique characterized by two matching stages: initial matching and consistency check. Several constraints (Epipolar, Uniqueness, Disparity continuity, Stochastic constraint and Disparity range constraint) are used to reduce the combinatorial search and the ambiguity of the false targets. With this approach, we can obtain the global optimum matches. The algorithm has been experimentally evaluated using a set of real images. The implementation and results have shown the efficacy of the proposed stereo matching technique.
A geometric correction method using the photographic model with the orbit parameters and attitude parameters of the satellite is presented in this paper. The piecewise majorization method is proposed to optimize and identify these parameters with a group of GCPs (Ground Controlled Points) to give the model high accuracy. This method is proved to be especially effective to those images of wide coverage and serious geometric distortion.