A multiscale operator for spatiotemporal isotropic attention is proposed to reliably extract attention points during image sequence analysis. Its consecutive local maxima indicate attention points as the centers of image fragments of variable size with high intensity contrast, region homogeneity, regional shape saliency, and temporal change presence. The scale-adaptive estimation of temporal change (motion) and its aggregation with the regional shape saliency contribute to the accurate determination of attention points in image sequences. Multilocation descriptors of an image sequence are extracted at the attention points in the form of a set of multidimensional descriptor vectors. A fast recursive implementation is also proposed to make the operator's computational complexity independent from the spatial scale size, which is the window size in the spatial averaging filter. Experiments on the accuracy of attention-point detection have proved the operator consistency and its high potential for multiscale feature extraction from image sequences.
We deal with the problem of time-efficient extraction of structural features in a large class of structural texture images. The proposed approach of multiscale morphological texture modeling describes explicitly and concisely both shape and intensity parameters in the structural texture model. The modeling is based on a morphological skeletal representation of structural texture cells as objects of interest and the genomic growth of a texture region starting from a seed cell. This representation offers the advantage of concise description of texture cells as compared to the existing edge-based or contour-based approaches. A computationally efficient estimation of the structural texture parameters for texture segmentation tasks is proposed. The model parameter estimation and subsequent feature extraction rely on cell localization and scale-based locally adaptive binarization of the localized cells using isotropic matched filtering. The multiscale isotropic matched filter (MIMF) provides a scale- and orientation-invariant detection of structural cells regarded as multiple objects of interest in texture regions. Results of experiments pertaining to the parameter estimation from synthetic and real texture images as well as the segmentation of texture regions based on structural features are also provided.
Accurate and automatic extraction of skeletal shape of objects of interest from satellite images provides an efficient solution to such image analysis tasks as object detection, object identification, and shape description. The problem of skeletal shape extraction can be effectively solved in three basic steps: intensity clustering (i.e. segmentation) of objects, extraction of a structural graph of the object shape, and refinement of structural graph by the orthogonal regression fitting. The objects of interest are segmented from the background by a clustering transformation of primary features (spectral components) with respect to each pixel. The structural graph is composed of connected skeleton vertices and represents the topology of the skeleton. In the general case, it is a quite rough piecewise-linear representation of object skeletons. The positions of skeleton vertices on the image plane are adjusted by means of the orthogonal regression fitting. It consists of changing positions of existing vertices according to the minimum of the mean orthogonal distances and, eventually, adding new vertices in-between if a given accuracy if not yet satisfied. Vertices of initial piecewise-linear skeletons are extracted by using a multi-scale image relevance function. The relevance function is an image local operator that has local maximums at the centers of the objects of interest.
Conventional methods for cartographic shape representation of objects from satellite images are usually inaccurate and will provide only a rough shape description if they are to work in a fully automated mode. For example, existing algorithms for skeletal thinning fail to provide a correctly shaped skeleton if the input images contain noise or the objects of interest are sparse and exhibit discontinuities. The proposed method for extraction of skeletons of 2-D objects is based on an efficient algorithm for multi-scale structural analysis of images obtained from satellite data. The form and topology of hydrological objects, such as rivers and lakes, can be extracted by applying a multi-scale relevance function in a quick, reliable and scale-independent way. The description of objects is obtained in the form of piecewise linear skeletons (multi-scale structural graph) and includes local scales at graph vertices, which correspond to local maxima of the relevance function. The experimental test results using Landsat-7 images show good accuracy of the relevance function approach and its potential for fully automated hydrographic mapping.
The reliable detection of objects of interest in images with inhomogeneous or textured background is a typical detection and recognition problem in many practical applications such as the medical and industrial diagnostic imaging. In this paper, a method for object detection is described in the framework of a visual attention mechanism based on the concept of multi-scale relevance function. The relevance function is an image local operator that has local maxima at centers of location of supposed objects of interest or their relevant parts termed as primitive objects. The visual attention mechanism based on the relevance function provides the following advantageous features in object detection. The model-based approach is used which exploits multi- scale morphological representation of objects (as object support regions in images) and regression representation of their intensity in order to perform time-effective image analysis. The introduced multi-scale relevance function in application to object detection provides a quick location of local objects of interest invariantly to object size and orientation.
The reliable detection of objects of interest on inhomogeneous background base don image data is a typical detection and recognition problem in many practical applications. In this paper, an algorithm of local object detection is described in the context of change detection based on the difference between two images obtained from the same scene. The proposed detection method using multi-scale relevance function is a model based-approach which takes into account the planar shape model of objects of interest and the regression model of intensity function with respect to objects and background. The image relevance function is an image local operator whose local extrema indicate on the locations of objects or their salient parts termed as primitive patterns. The image fragment centered at the maximum point of the relevance function represents a region of attention. A structure-adaptive binarizaiton is performed within each region of attention by using variable threshold. The comparative testing of the proposed algorithm and the known techniques have shown better performance of the relevance function approach at the approximately same dely of detection.
Detection and binarization of local objects of interest (defects and abnormalities) in radiographic images is considered with application to industrial (non-destructive testing) and medical diagnostic imaging. The known standard approaches such as the histogram-based binarization or the method of dynamic thresholding yield poor segmentation results on the images containing small low-contrast objects and noisy background. The proposed method for object detection using binary segmentation has the following advantageous features. A model-based approach is applied which exploits the object multi-scale morphological representation in order to perform a time-effective image analysis. The intensity function is modeled by a polynomial regression representation with the so- called conformable two-region model. The estimation of the model parameters is made by using a robust non-linear estimation procedure. The concept of a multi-scale relevance function has been introduced for rapid location of local objects invariantly to the object shape, size, and orientation. The relevance function is a function that has the local maximum at the location center of an object of interest or its relevant part such as the corner edge. The developed segmentation method has been comparatively tested on radiographic images in non-destructive testing of weld joins and medical images from chest radiography.
In the proposed paper, the problem of robust estimation of the polynomial regression parameters is considered with application to image processing. The polynomial regression model states that the intensity function of an image can be represented as a polynomial function of defined order within a sample window plus independent noise which is assumed to be Gaussian distributed with a small fracture of outliers. The developed procedure for robust estimation of the polynomial regression parameters is based on computation of partial optimal estimates using the least squares method which exploits the fact that the majority of the regression residuals have Gaussian distribution. The final estimate is selected by the principle of maximum a posteriori probability. In direct form, the proposed technique is computationally expensive. Since the regression parameters can be represented as a linear combination of local moments, it allows to decrease the computational complexity of the proposed technique by an order (i.e. by O(N), where N is the size of the used subsamples) because local moments can be calculated recursively. The estimated regression parameters can be used for robust estimation of image and background intensity, noise variance, as well as for adaptive image filtering and segmentation.
In the proposed paper, the problem of noise evaluation is considered with application to image filtering and segmentation. The underlying structural model of original image is considered which describes the shape of image objects or their parts. The distinctive feature of the presented model is the separate modeling of object's planar shape as well as the image intensity function. For the intensity function model of original image, a piecewise polynomial model of low degrees (up to the second one) is considered. Then, noise to be evaluated is treated in a broad sense, namely as the intensity residuals of the piecewise polynomial modeling. It is also assumed that most of the pixels satisfy the polynomial model except for a relatively small number of edge points between homogeneous regions and fine details. A robust noise variance estimator is proposed for the images corrupted by outliers, i.e. impulsive noise.
Robust statistical estimators have found wide application in image processing and computer vision because conventional estimation methods fail to work when outliers from the assumed image model are present in real image data. In this paper, the method of partial robust estimates is described in which the final estimate of model parameters is made by the concept of maximum a posteriori probability or by the adaptive linear combination depending on the image contents. The underlying image model consists of a polynomial regression representation of the image intensity function and a structural model of local objects on non-homogeneous background. The developed estimation procedures have been tested on radiographic images in applications to detail-preserving smoothing and detection of local objects of interest. The obtained results and theoretical investigation confirm the model adequacy to real image data and robustness of the developed estimators of the model parameters.
In this article a structure-adaptive approach to the evaluation of image local properties for adaptive filtering is described. The adaptive procedure is based on selection of the most homogeneous neighborhood region from several possible structuring regions by the principle of maximum posterior probability. Then, an optimal evaluation of the pixel value at this point is performed involving pixels from the determined neighborhood region and the symmetric structuring region. The trimmed mean filters are used for the robust evaluation of local properties during estimation of object and background intensities when the supposed additive noise has a mixed conditional distribution, e.g., normal distribution with outliers. A time-efficient scheme for fast implementation of this method is proposed as well.
Detection and binary segmentation of low-contrast flaws (defects) in noisy radiographic images is considered with an application to non-destructive evaluation of materials and industrial articles. The known approaches, like the edge detection or unsharp masking with a consecutive thresholding operation, yield poor results for such images. In the presented method of object detection, a model-based approach is adopted which relies on shape constraints of the objects to be detected as well as exploits the image multiresolution representation. For detection of local objects, the maximum likelihood principle and statistical hypothesis testing is used with the confidence control during all stages of the image analysis. The proposed novel procedure of estimation of the image intensity from noisy pixels ensures a robust evaluation of basic model parameters in the presence of outliers which are considered as impulsive noise.
In this paper, a structure-adaptive approach to the nonlinear image filtering is described. The adaptive procedure is based on selection of the most homogenous neighborhood region from several possible structuring regions by the principle of maximum a posteriori probability. Then, an optimal evaluation of the pixel value is performed involving pixels from the determined neighborhood region. Trimmed mean filters are used for the robust evaluation of local properties during estimation of object and background intensities when the noise has a mixed conditional distribution, e.g. normal distribution with outliers.
KEYWORDS: Mathematical modeling, Image processing algorithms and systems, Signal to noise ratio, Defect detection, Image segmentation, Digital filtering, Diagnostics, Linear filtering, Image filtering, Nonlinear filtering
The problem of detection of the region of interest by means of nonlinear filters is addressed as applied to flaws detection in diagnostics imaging. The solution of this problem by conventional methods of linear high-pass filters with subsequent thresholding does not yield a satisfactory result due to a poor quality of images in the applications. To evaluate the basic local features in images, it is proposed to use adaptive trimmed mean filters with fast recursive calculation of order statistics. Theoretical and experimental investigation of the proposed algorithm for adaptive filtering confirms its efficiency for the detection of small isolated objects and crack- like flaws in diagnostic imaging.
During the binary segmentation an image transforms into a binary representation, in which the regions of interest as objects (or their parts) for further analysis are detected as connected components. The underlying image model for binary segmentation and analysis is composed of two separated parts: the first one is to model image domain by using notions and operations of mathematical morphology and the second one is to model the values of intensity function, defined on this domain. The proposed morphological operator transforms gray-scale images into binary ones by comparing image local properties within the structuring elements or structuring regions with a tolerance threshold, giving eroded objects as connected components and dilated contours for further analysis. Since the implementation of this operation is rather complicated, fast algorithms to calculate local properties of intensity function (e.g. mean, square deviation, median, absolute deviation, etc.), using spatial recursion, have been developed. They give a speed-up of order O(N), where N equals L X L is the structuring element size, for computing, e.g. local mean and variance, as compared with their naive calculation.
Nowadays various architectures are suggested for highly efficient image processing, including parallel processors of SIMD type, multiprocessor systems, pipelined processors, systolic arrays and pyramid machines. However, a maximal speed of algorithm execution can be reached only by specialized processor implemented on a custom chip. So merging of the properties of a specialized processor and a possibility of reprogramming in one approach should give a satisfactory result. The paper suggests a new approach to the developing of architecture of a high-speed parallel system for low-level image processing. The homogeneous computing structure (HCS) and homogeneous storing structure (HSS) are the basic elements of this approach. The high speed of the system is provided by structural method of organization of the computing process which is based on the hardware realization of all the nodes of the information graph and their interconnections. The size of the HCS matrix allows to use for each program instruction its own group of processor elements. The program is loaded once before starting to solve the problem, and the information streams processing is carried out without intermediate results storage. The data streams applied to the information inputs of the processor elements are processed in accordance with the program, moving from one element to another in the matrix of the HCS. The examples of execution of the image filtering algorithms on the system are presented.