基于SIFT的脱机签名鉴别算法研究

提出一种基于SIFT的算法,对签名鉴别算法进行研究。首先对采集样本图像进行预处理。然后对处理后的图像分别构建高斯差分尺度空间（DoG）,通过DoG来寻求极值点即特征点。最后通过BBF搜索算法来匹配目标样本。该方法继承了SIFT算法的稳定性,能取得较好的签名鉴别精度。     

Near optimal detection of signals in impulsive noise modeled with asymmetric α-stable distribution

There has been great interest in symmetric α-stable distributions which have proved to be very good models for impulsive noise. However, most of the classical non-Gaussian receiver design techniques cannot be extended to the symmetric α-stable noise case since these techniques require an explicit compact analytical form for the probability density function (PDF) of the noise distribution which α-stable distributions do not possess. A new analytical representation has been suggested for the symmetric α-stable PDF which is based on scale mixtures of Gaussians. Based on this new analytical representation, this paper introduces a novel near-optimal receiver for the detection of signals in symmetric α-stable noise. The performance of the new receiver is very close to the locally optimum receiver and is significantly better than the performance of previously suggested sub-optimum receivers. The new technique has important potential in radar, sonar, and other applications     

Computer-aided breast cancer detection and diagnosis of massesusing difference of Gaussians and derivative-based feature saliency

A new model-based vision (MBV) algorithm is developed to find regions of interest (ROI's) corresponding to masses in digitized mammograms and to classify the masses as malignant/benign. The MBV algorithm is comprised of 5 modules to structurally identify suspicious ROI's, eliminate false positives, and classify the remaining as malignant or benign. The focus of attention module uses a difference of Gaussians (DoG) filter to highlight suspicious regions in the mammogram. The index module uses tests to reduce the number of nonmalignant regions from 8.39 to 2.36 per full breast image. Size, shape, contrast, and Laws texture features are used to develop the prediction module's mass models. Derivative-based feature saliency techniques are used to determine the best features for classification. Nine features are chosen to define the malignant/benign models. The feature extraction module obtains these features from all suspicious ROI's. The matching module classifies the regions using a multilayer perceptron neural network architecture to obtain an overall classification accuracy of 100% for the segmented malignant masses with a false-positive rate of 1.8 per full breast image. This system has a sensitivity of 92% for locating malignant ROI's. The database contains 272 images (12 b, 100 μm) with 36 malignant and 53 benign mass images. The results demonstrate that the MBV approach provides a structured order of integrating complex stages into a system for radiologists     

Representation of image content based on RoI-BoW

Representation of image content is an important part of image annotation and retrieval, and it has become a hot issue in computer vision. As an efficient and accurate image content representation model, bag-of-words (BoW) has attracted more attention in recent years. After segmentation, BoW treats all of the image regions equally. In fact, some regions of image are more important than others in image retrieval, such as salient object or region of interest. In this paper, a novel region of interest based bag-of-words model (RoI-BoW) for image representation is proposed. At first, the difference of Gaussian (DoG) is adopted to find key points in an image and generates different size grid as RoI to construct visual words by the BoW model. Furthermore, we analyze the influence of different size segmentation on image content representation by content based image retrieval. Experiments on Corel 5K verify the effectiveness of RoI-BoW on image content representation, and prove that RoI-BoW outperforms the BoW model significantly. Moreover, amounts of experiments illustrate the influence of different size segmentation on image representation based on the Bow model and RoI-BoW model respectively. This work is helpful to choose appropriate grid size in different situations when representing image content, and meaningful to image classification and retrieval.     

Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio

Neural spike sorting is an indispensable step in the analysis of multiunit extracellular neural signal recording. The applicability of spike sorting systems has been limited, mainly to the recording of sufficiently high signal-to-noise ratios, or to the cases where supervised classification can be utilized. We present a novel unsupervised method that shows satisfactory performance even under high background noise. The system consists of an efficient spike detector, a feature extractor that utilizes projection pursuit based on negentropy maximization (Huber, 1985 and Hyvarinen et al, 1999), and an unsupervised classifier based on probability density modeling using mixture of Gaussians (Jain et al., 2000). Our classifier is based on the mixture model with a roughly approximated number of Gaussians and subsequent mode-seeking. It does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. The feature extraction stage leads to better performance than those utilizing principal component analysis and two nonlinear mappings for the recordings from the somatosensory cortex of rat and the abdominal ganglion of Aplysia. The classification method yielded correct classification ratio as high as 95%, for data where it was only 66% when a kappa-means-type algorithm was used for the classification stage.     

Chromatographic pattern classification

In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population.     

Alpha稳定噪声PDF的近似估计

Alpha稳定分布的概率密度函数除少数特例外不存在闭式表达,造成应用的困难.无穷级数逼近的渐进表达存在级数的阶数难以确定,且近似性能不稳定的缺陷,而无穷高斯混合的表达方法同样存在阶数难以的确定问题,而且逼近的效果较差.本文提出了仅采用两高斯混合的逼近方法,给出了两种高斯随机变量参数的确定方法以及所占比例的估计方法.实验结果表明该方法逼近效果稳定.该方法仅使用两种高斯的混合形式,计算简便.     

基于稀疏表示的被动毫米波L-R成像算法

在被动毫米波的图像恢复中,L-R算法是一种简单而有效的非线性方法。但当噪声不可忽略时,L-R算法难以获得较好的复原结果。自适应稀疏表示,作为一种新的信号处理方法,具有表达信号灵活的特点,能够在保持目标特征的同时有效地去除噪声。该文提出一种基于自适应稀疏表示的L-R算法。首先采用稀疏信号表示的方法进行去噪,然后使用L-R算法进行图像恢复。这种改进算法通过使用基于自适应稀疏表示的去噪算法有效地减少了噪声对L-R算法的影响。实验数据的成像结果表明:该文的改进算法提高了L-R算法的性能,可用于低信噪比的图像复原。     

A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction

This paper presents a novel fuzzy-segmentation method for diffusion tensor (DT) and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g., those based on fuzzy C means (FCM), incorporate Gaussian class models that are inherently biased towards ellipsoidal clusters characterized by a mean element and a covariance matrix. Tensors in fiber bundles, however, inherently lie on specific manifolds in Riemannian spaces. Unlike FCM-based schemes, the proposed method represents these manifolds using nonparametric data-driven statistical models. The paper describes a statistically-sound (consistent) technique for nonparametric modeling in Riemannian DT spaces. The proposed method produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-random-field framework. Results on synthetic and real, DT and MR images, show that the proposed method provides information about the uncertainties in the segmentation decisions, which stem from imaging artifacts including noise, partial voluming, and inhomogeneity. By enhancing the nonparametric model to capture the spatial continuity and structure of the fiber bundle, we exploit the framework to extract the cingulum fiber bundle. Typical tractography methods for tract delineation, incorporating thresholds on fractional anisotropy and fiber curvature to terminate tracking, can face serious problems arising from partial voluming and noise. For these reasons, tractography often fails to extract thin tracts with sharp changes in orientation, such as the cingulum. The results demonstrate that the proposed method extracts this structure significantly more accurately as compared to tractography.     

基于二维最大熵阈值分割的SIFT图像匹配算法

提出了一种基于二维灰度直方图最大熵阈值分割的SIFT图像特征匹配算法。与传统SIFT算法相比,该算法首先综合利用图像像素的灰度信息和邻域空间信息,生成图像二维灰度直方图,并基于此直方图的最大熵对图像进行阈值分割,然后检测分割后图像的DoG尺度空间局部极值,并以此作为特征点进行图像匹配。实验结果表明,基于所提出的匹配算法,可以有效降低图像背景噪声和边缘像素点对目标匹配的干扰,进而提高图像目标的匹配性能。     

Impulse generation with appropriate amplitude, length,inter-arrival, and spectral characteristics

This paper proposes a suitable method for simulating impulses with appropriate amplitude, spectral, and inter-arrival characteristics. The statistics used to develop the parameters of this model are based on statistics derived from observations of impulse noise on the telephone networks of British Telecom (BT) and Deutsche Telekom (DT). This paper initially reviews the former DT approach to impulse noise generation for testing digital subscriber line systems, so called xDSL systems. Some problems are highlighted and an alternative technique is suggested that is capable of generating impulses with both appropriate amplitude an spectral characteristics     

EDGE LOCALIZATION BY MULTIPLE OF GAUSSIANs THEORY

Zero-crossing of a derivative of Gaussian filter is a well-known edge location criterion. Examples are the Laplacian, the second derivative in the gradient direction (SDGD) and the sum of the Laplacian and SDGD (PLUS). Derivative operators can easily be implemented by convoluting the primitive image with a derivative of a Gaussian. Gaussian filter displaces the equipotential of half height inwards for convex edge and outwards for concave edges. A Difference-of-Gaussian (DoG) filter is similar to the Laplacian-of-Gaussian but with opposite sign and causes a convex edge shift inwards. This paper introduces the Multiple-of-Gaussian niters to reduce curvature-based location error. Using a linear combination of N Gaussians(N>2) with proper weights, the edge shifts can be reduced to 1/(2N-3) of the ones produced by a similar Laplacian-of-Gaussian filter.     

Isolated word language identification system with hybrid features from a deep belief network

The representation of good audio features is the first and foremost requirement for improving the identification performance of any system. Most of the representation learning approaches are based on connectionist systems to learn and extract latent features from the speech data. This research work presents a hybrid feature extraction approach to integrate Mel-Frequency Cepstral Coefficients (MFCC) features with Shifted Delta Cepstral (SDC) coefficients features, which are further stacked to Deep Belief Network (DBN), for yielding new feature representations of the speech signals. DBN is utilized for unsupervised feature learning on the extracted MFCC-SDC acoustic features. A 3-layer Back Propagation Neural Network (BPNN) classifier is initialized in terms of the learning outcomes of hidden layers of DBN for identifying language from the uttered speech. The efficiency of the proposed approach is evaluated by simulating several experimental algorithms on the user-defined database of isolated words in four languages, namely, Tamil, Malayalam, Hindi, and English, in the working platform of MATLAB. The obtained results for the proposed hybrid approach MFCC-SDC-DBN are promising. The proposed approach is also compared with the baseline feature extraction approach MFCC-SDC by utilizing traditional acoustic features and BPNN classifier. The accuracy obtained with our proposed approach is 98.1% whereas that of the baseline approach is 82%, thereby providing an overall improvement of 16.1%.     

脉冲噪声环境中基于clipping方法的鲁棒LPFT及其重排算法

为了更好地处理脉冲噪声环境中的时变信号,本文提出了基于clipping方法的鲁棒局部多项式傅里叶变换(LPFT)及其重排算法。首先利用clipping方法对信号中掺杂的脉冲噪声进行抑制,得到较好的信号时频分布表示,然后将重排算法与该鲁棒LPFT相结合,以提高信号的时频聚集性。通过实验仿真可以看出,与基于中值滤波器的鲁棒LPFT相比,基于clipping方法的鲁棒LPFT同样能对被脉冲噪声干扰的信号给出较好的时频表示,而且其瞬时频率估计的最小均方误差(MSE)较低,计算量较小。并且,本文在基于clipping方法的鲁棒LPFT对掺杂脉冲噪声的信号进行处理的基础上,利用重排算法与其结合,有效增强了信号的时频聚集性。因此基于clipping方法的鲁棒LPFT及其重排算法是一种高效的处理脉冲噪声干扰信号及提高信号时频聚集性的方法。     

复杂场景下结合SIFT与核稀疏表示的交通目标分类识别

针对复杂场景下的交通目标分类识别难点,提出一种基于尺度不变特征转换(SIFT)与核稀疏表示的分类识别算法.该算法首先利用SIFT分别提取训练样本和待测目标局部特征信息,通过核方法将特征样本映射到核空间,构建过完备字典,最后通过待测目标在字典中的稀疏度与重构误差对交通目标类别进行判定.同时,分析了随机投影下的核稀疏表示分类与特征维数之间的关系.实验结果表明,与SVM、稀疏表示分类(SRC)相比,该方法增强了交通目标特征层的类判别能力,具有较好的识别率和鲁棒性.     

Sparse Representation with Optimized Learned Dictionary for Robust Voice Activity Detection

Traditionally, most of voice activity detection (VAD) methods are based on speech features such as spectrum, temporal energy, and periodicity. The robustness of these features plays a critical role on the performance of VAD. However, since these features are always directly generated from observed signal, the robustness of these features would be significantly degraded in non-stationary noise environments, especially at low level signal-to-noise ratio (SNR) condition. This paper proposes a kind of robust feature for VAD based on sparse representation with an optimized learned dictionary. To do so, a speech dictionary and a noise dictionary are first learned from speech corpus and noise corpus, respectively. Then an optimization algorithm is designed to reduce the mutual coherence between the two learned dictionaries. After that the proposed feature is generated from the optimized dictionary-based sparse representation, and a VAD method is derived from the proposed feature. The proposed method is evaluated over seven types of noise and four types of SNR level, experimental results show that the optimized dictionary is important for enhancing the robustness of the proposed method, and the proposed method performs well under non-stationary noise, especially at low level SNR condition.     

Geometric Features-Based Filtering for Suppression of Impulse Noise in Color Images

A geometric features-based filtering technique, named as the adaptive geometric features based filtering technique (AGFF), is presented for removal of impulse noise in corrupted color images. In contrast with the traditional noise detection techniques where only 1-D statistical information is used for noise detection and estimation, a novel noise detection method is proposed based on geometric characteristics and features (i.e., the 2-D information) of the corrupted pixel or the pixel region, leading to effective and efficient noise detection and estimation outcomes. A progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise. Extensive experiments conducted using a wide range of test color images have shown that the AGFF is superior to a number of existing well-known benchmark techniques, in terms of standard image restoration performance criteria, including objective measurements, the visual image quality, and the computational complexity.