Application of multiresolution spatial filters to long-axis tracking

A method was developed for automatically tracking the long axis of thin objects which have nonuniform width and arbitrary orientation in a two-dimensional image space. This method is used to determine the length of isolated contractile smooth muscle cells, but has applications in other medical areas such as angiographic imaging. Pattern recognition techniques that determine object size and orientation are used to identify the long axis of an imaged object from its responses to difference of Gaussian and orientation filters. This method needs no a priori knowledge of object location, adapts to varying image magnification, requires little human interaction, and yields reproducible results.     

Conditional run-length coding for multilevel graphic image

The concept of conditional run-length coding mainly used for bilevel image is extended for multilevel graphic image. It is shown that the extended coding algorithm is also optimum for the Markov model. Its performance is compared with bit-plane coding and cosine transform coding.     

Iterative Wiener filters for image restoration

The iterative Wiener filter, which successively uses the Wiener-filtered signal as an improved prototype to update the covariance estimates, is investigated. The convergence properties of this iterative filter are analyzed. It has been shown that this iterative process converges to a signal which does not correspond to the minimum mean-squared-error solution. Based on the analysis, an alternate iterative filter is proposed to correct for the convergence error. The theoretical performance of the filter has been shown to give minimum mean-squared error. In practical implementation when there is unavoidable error in the covariance computation, the filter may still result in undesirable restoration. Its performance has been investigated and a number of experiments in a practical setting were conducted to demonstrate its effectiveness     

Complex spatial filters for image deconvolution

The history and principles of image distortion correction by means of holographic spatial filtering are reviewed. Both predetection and postdetection filtering are considered. These and other principle applications of complex spatial filtering are assessed. The major problem areas are discussed, along with possible solutions.     

基于空间正则化约束的支持向量相关滤波器目标跟踪方法

基于支持向量相关滤波器(Support Correlation Filters,SCF)的目标跟踪方法存在严重的样本边界不连续问题,因此模型判别能力受到严重限制。本文将空间正则化项引入到SCF中,提出了基于空间正则化约束的支持向量相关滤波器(Spatially Regularized SCF,SRSCF)模型。相比于SCF,SRSCF不仅可以借助更大的图像区域进行模型学习,同时也能缓解样本的边界不连续问题对模型学习的负面影响,由此得到判别能力更强的模型。此外,本文提出了一种ADMM(Alternating Direction Method of Multiplier)算法求解SRSCF模型,其中每个子问题具有解析解。实验结果表明,相较于SCF,SRSCF能够有效地提升跟踪精度,同时仅增加较少的计算开销。     

序列图像运动点目标快速检测与跟踪方法

为解决低信噪比运动点目标在实时检测中的系统抖动和信噪比低等问题,根据运动点目标的特点,提出了基于数学形态学滤波、目标光强度连续性和假设检验的点目标检测方法。基于数学形态学的高通滤波器能较强地抑制背景噪声并增强目标;利用光强度时空连续性识别目标;假设检验对解决目标丢失和新目标出现都具有较好的效果。用TMS320C6201高速信号处理器的处理结果表明：算法对信噪比约为2的点目标检测性能较为满意。     

Interpolated Mth-band filters for image size conversion

Image/video size conversion at variable rates requires that a large set of interpolation filters should be stored in a table. We present the interpolated Mth-band filters as the interpolation filters, which are obtained from the cubic spline interpolation of a prototype M/sub p/th-band eigenfilter. The proposed filter can be calculated in real time, eliminating the need for a large on-chip memory. Scaled images using the proposed filters show superb image quality.     

Prescanned minmax centre-weighted filters for image restoration

The prescanned minmax centre-weighted (PMCW) filter, which is capable of restoring images severely corrupted by impulsive noise, is presented. Before filtering, the input image is scanned by a running window; the maximum and minimum of each ranked set in the running window are grouped as the first subset, and the rest as the second subset. Then, the two subsets are filtered in sequence by a centered weight filter. It is shown that filtering results in the first subset provide the extension property by which the effective smoothing region for the subsequent filtering in the second subset is extended. The detail-control property of the PMCW is characterised and some relationships between PMCW and ranked-order based filters are derived. Quantitative comparisons demonstrate that the PMCW filter offers a more desirable combination of noise suppression and detail preservation properties than can other median-type filters     

Partition-based weighted sum filters for image restoration

We develop the concept of partitioning the observation space to build a general class of filters referred to as partition-based weighted sum (PWS) filters. In the general framework, each observation vector is mapped to one of M partitions comprising the observation space, and each partition has an associated filtering function. We focus on partitioning the observation space utilizing vector quantization and restrict the filtering function within each partition to be linear. In this formulation, a weighted sum of the observation samples forms the estimate, where the weights are allowed to be unique within each partition. The partitions are selected and weights tuned by training on a representative set of data. It is shown that the proposed data adaptive processing allows for greater detail preservation when encountering nonstationarities in the data and yields superior results compared to several previously defined filters. Optimization of the PWS filters is addressed and experimental results are provided illustrating the performance of PWS filters in the restoration of images corrupted by Gaussian noise.     

Adaptive multichannel filters for colour image processing

This paper addresses the problem of noise suppression for multichannel data, such as colour images. The proposed filters utilize adaptive data dependent nonparametric techniques. Simulation results indicate that the new filters suppress impulsive as well as Gaussian noise and preserve edges and details.     

Technique for designing biorthogonal wavelet filters with anapplication to image compression

A technique for designing new symmetric biorthogonal wavelets from a given symmetric regular filter is presented. The main idea is to find a symmetric complementary filter of a given regular filter such that it has the least mean square (LMS) amplitude deviation from the ideal halfband lowpass filter. New biorthogonal wavelet filter pairs can be obtained via factoring the product of the complementary polynomial and the given binomial. By applying these new symmetric biorthogonal wavelet filters to the compression of some complicated images an improved result in reducing artefacts may be achieved     

Particle filters for positioning, navigation, and tracking

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter-based algorithms. Here, the use of nonlinear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable with satellite navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars     

Particle filters for tracking an unknown number of sources

This paper addresses the application of sequential importance sampling (SIS) schemes to tracking directions of arrival (DOAs) of an unknown number of sources, using a passive array of sensors. This proposed technique has significant advantages in this application, including the ability to detect a changing number of signals at arbitrary times throughout the observation period and that the requirement for quasistationarity over a limited interval may be relaxed. We propose the use of a reversible jump Monte Carlo Markov chain (RJMCMC) step to enhance the statistical diversity of the particles. This step also enables us to introduce two novel moves that significantly enhance the performance of the algorithm when the DOA tracks cross. The superior performance of the method is demonstrated by examples of application of the particle filter to sequential tracking of the DOAs of an unknown and nonstationary number of sources and to a scenario where the targets cross. Our results are compared with the PASTd method.     

采用混合t分布粒子滤波器的视觉跟踪

由于目标数量的变化,观测数据的岐义性和目标间的遮挡,多目标视觉跟踪问题面临多种困难.基于目标分布的有限t分布混合模型提出了一种混合t分布粒子滤波器以实现多目标跟踪.在算法中,每个被跟踪目标指派一个独立的粒子滤波器,显式处理当新目标出现在场景中时对应粒子滤波器的初始化,当被跟踪目标消失时,对应粒子滤波器的删除.混合t分布...     

Conditional entropy coding of VQ indexes for image compression

Block sizes of practical vector quantization (VQ) image coders are not large enough to exploit all high-order statistical dependencies among pixels. Therefore, adaptive entropy coding of VQ indexes via statistical context modeling can significantly reduce the bit rate of VQ coders for given distortion. Address VQ was a pioneer work in this direction. In this paper we develop a framework of conditional entropy coding of VQ indexes (CECOVI) based on a simple Bayesian-type method of estimating probabilities conditioned on causal contexts, CECOVI is conceptually cleaner and algorithmically more efficient than address VQ, with address-VQ technique being its special case. It reduces the bit rate of address VQ by more than 20% for the same distortion, and does so at only a tiny fraction of address VQ's computational cost.     

Multistage order statistic filters for image sequence processing

The application of multistage order statistic filters (MOS) to the task of noise suppression in time-varying imagery is studied. It is shown that MOS filters efficiently preserve image structures under motion without motion compensation preprocessing. In particular, the families of multistage median and weighted median filters are considered. Motion preservation and statistical smoothing measures are derived. It is shown that spatiotemporal filtering allows for a significant improvement over both spatial and temporal filtering in terms of output image resolution and noise suppression     

ℒ-M-S filters for image restoration applications

A new filtering architecture is proposed, generalizing some previously introduced multilevel median filters. An efficient design procedure for the new filtering architecture is demonstrated for image restoration application. Simulation results show a good noise rejection performance, combined with a fine detail preservation capability.     

Complex, linear-phase filters for efficient image coding

With the exception of the Haar basis, real-valued orthogonal wavelet filter banks with compact support lack symmetry and therefore do not possess linear phase. This has led to the use of biorthogonal filters for coding of images and other multidimensional data. There are, however, complex solutions permitting the construction of compactly supported, orthogonal linear phase QMF filter banks. By explicitly seeking solutions in which the imaginary part of the filter coefficients is small enough to be approximated to zero, real symmetric filters can be obtained that achieve excellent compression performance     

Analysis of multichannel narrow-band filters for image texturesegmentation

A model for texture analysis and segmentation using multiple oriented channel filters is analyzed in the general framework. Several different arguments are applied leading to the conclusion that the two-dimensional Gabor filters possess strong optimality properties for this task. Properties of the multiple-channel segmentation approach are analyzed. In particular, perturbations of textures from an ideal model are found to have important effects on the segmentation that can usually be ameliorated by simply preceding the segmentation process by a logarithmic operation and using a low-pass postfilter prior to making region assignments. The difficult problems of space-variant textures and multiple component textures are also considered. Local spatial frequency estimation approaches are suggested that use the responses as constraints in estimating the locally emergent texture frequencies. Complex texture aggregates containing multiple shared frequency components can be analyzed if the textures are distinct and few in number     

Distributed current-mode image processing filters

Presented is a new design method for distributed focal plane image processing, which allows complementary metal oxide semiconductor (CMOS) implementation of two-dimensional, reconfigurable, image-processing kernels at the pixel level.