Showing papers on "Structuring element published in 2008"

Morphological Image Processing

Abstract: Morphological processing (MP) has applications in diverse areas of image processing as filtering, segmentation, and pattern recognition, to both binary and grayscale images. One of the most important operations in morphological image processing is reconstruction from markers. The basic idea is to mark certain image components and then to reconstruct that portion of the image that contains the marked components. The basic fitting operation of morphology is the erosion of an image by a structuring element. Erosion is done by scanning the image with the structuring element. When the structuring element fits completely inside the object, the probe position is marked. The erosion result consists of all scanning locations, where the structuring element fits inside the object. The eroded image is usually a shrunken version of the image, and the shrinking effect is controlled by the structuring element size and shape. As an extension of the binary case, grayscale opening (closing) can be achieved simply by threshold decomposition, followed by binary opening (closing) and stack reconstruction. Grayscale opening and closing have the same properties as their binary equivalents.

New class of top-hat transformation to enhance infrared small targets

Abstract: To improve the performance of a top-hat transformation for infrared small target enhancement, a new class of top-hat transformation through structuring element construction and operation reorganization is proposed. The structuring element construction and operation reorganization are based on the property of the infrared small target image and thus can greatly improve the performance of small target enhancement. Experimental results verified that it was very efficient.

Low-Complexity Binary Morphology Architectures With Flat Rectangular Structuring Elements

Abstract: This article describes and evaluates algorithms and their hardware architectures for binary morphological erosion and dilation. In particular, a fast stall-free low-complexity architecture is proposed that takes advantage of the morphological duality principle and structuring element (SE) decomposition. The design is intended to be used as a hardware accelerator in real-time embedded processing applications. Hence, the aim is to minimize the number of operations, memory requirement, and memory accesses per pixel. The main advantage of the proposed architecture is that for the common class of flat and rectangular SEs, complexity and number of memory accesses per pixel is low and independent of both image and SE size. The proposed design is compared to the more common delay-line architecture in terms of complexity, memory requirements and execution time, both for an actual implementation and as a function of image resolution and SE size. The architecture is implemented for the UMC 0.13- mum CMOS process using a resolution of 640 t 480. A maximum SE of 63 t 63 is supported at an estimated clock frequency of 333 MHz.

An Efficient Hardware Architecture without Line Memories for Morphological Image Processing

Abstract: In this paper, we present a novel hardware architecture to achieve erosion and dilation with a large structuring element. We are proposing a modification of HGW algorithm with a block mirroring scheme to ease the propagation and memory access and to minimize memory consumption. It allows to suppress the needs for backward scanning and gives the possibility for hardware architecture to process very large lines with a low latency. It compares well with the Lemonnier's architecture in terms of ASIC gates area and shows the interest of our solution by dividing the circuit area by an average of 10.

Spatially-Variant Directional Mathematical Morphology Operators Based on a Diffused Average Squared Gradient Field

Abstract: This paper proposes an approach for mathematical morphology operators whose structuring element can locally adapt its orientation across the pixels of the image. The orientation at each pixel is extracted by means of a diffusion process of the average squared gradient field. The resulting vector field, the average squared gradient vector flow, extends the orientation information from the edges of the objects to the homogeneous areas of the image. The provided orientation field is then used to perform a spatially variant filtering with a linear structuring element. Results of erosion, dilation, opening and closing spatially-variant on binary images prove the validity of this theoretical sound and novel approach.

Modified top-hat transformation based on contour structuring element to detect infrared small target

Abstract: Top-hat transformation has been widely used for infrared small target detection, but it is sensitive to the clutter and may destroy the details of the image, which make it an ineffective way for small target detection. To detect the infrared small target simply and efficiently, a new Top-hat transformation based on contour structuring element is proposed and then modified according to the property of the target region to enhance and detect the infrared small target in this paper. Experimental results indicated that the proposed method was efficient under the conditions of heavy clutter and dim target intensity.

Noisy Image Edge Detection Based on Multi-scale and Multi-structuring Element Order Morphology Transformation

Abstract: The image edge detection is important tool of image processing and also is the foundation of pattern recognition and computer vision, which will affect the later processes. To noisy image, the edge detection is more important for noise is so common in image. This paper deeply studies the edge detection methods of the noisy images and concentrates on the order morphology method. By analysis and study the theory of order morphology, this paper constructs three edge detection operations and analyzes the specialities of these operations and the structure elements. Based on this, the new edge detection operations for noisy image based on multi-scale and multi-structuring element order morphology are proposed. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the traditional edge detection operations and the order morphology operations, the operations are more effective on noise restraining and retaining the image details.

Research advances and prospects of mathematical morphology in image processing

Abstract: Mathematical morphology, due to its basic concept of set theory, has an inherent advantage for image processing. Morphological framework shows mighty vitality not only because it can perform tasks from the simplest to the most demanding: noise reduction, edge detection, segmentation, texture and shape analysis, etc, but also because it can be applied to almost all application fields dealing with digital image processing. This paper discusses the extension of set operations, structuring element conformation and combination with other image processing methods, which are three main aspects to research the development of morphology. Besides, an objective forecast for future tendency about mathematical morphology is proposed.

New features for classification of cancerous masses in mammograms based on morphological dilation

Abstract: In the current research in the field of analysis of mammograms by means of image processing tools the task of malignancy and specularity assessment is investigated extensively. Early detection of malignant masses may significantly lower the risk of metastasis. It is known that malignancy is closely related to the shape of a mass (existence of spicules emanating from the center of the mass). Therefore the tasks of malignancy and specularity analysis are very often treated jointly. In this paper we introduce a new set of features useful in performing this task. For a contour of a cancerous mass a sequence of dilations is computed, the number of pixels on the outer contour of each dilation is counted, and this number is plotted against the size of the disk-shaped structuring element. Next, the linear trend is removed, and after denoising, the proposed features are calculated. The crucial point is that the proposed features are zero iff the input contour is circular and that all the features are invariant under translation, rotation, and scaling. These distinctive properties ensure successful classification irrespective to location, orientation and scale of the mass with the Az values of the ROC curve higher than for features given in the literature. The additional advantage of our approach is the relative simplicity of the proposed features. In contrast to many traditional features, no sophisticated algorithms are employed, so reimplementation of the new features is easy.

Image denoising algorithm based on the median morphological filter

Abstract: With respect to the instance of image denoising by morphological filter that causes useful information missing, a new median morphological filter algorithm is presented. The algorithm does the median value operation through erosion and dilation operations, improves and optimizes structuring element units. The experiment proves that the new method overcomes the inherent inadequate of traditional morphological limit operation, and effectively removes the impulse noise of image; especially in low signal to noise ratio environment, the denoising performance has obvious advantages than the traditional morphological filter and median filter algorithms. Therefore, it has a broad application prospect in image processing.

Grey-scale 1-d dilations with spatially-variant structuring elements in linear time

Abstract: Spatially variant morphological operators can significantly improve filtering capabilities or object detection score of various applications. Whereas an effort has been made to define the theoretical background, the efficient implementation of adaptable algorithms remained far less considered. In this paper, we present an efficient, one-scan, linear algorithm for 1-D grey-scale dilations/erosions with spatially variant structuring elements. The proposed algorithm processes data in stream, can work in place and produces results with minimal latency. The computing time is independent of the structuring element size.

An adaptive morphological algorithm to segment Chinese square seal in bank check image

Abstract: In this paper, an adaptive morphological segmentation algorithm is proposed to extract a binary Chinese square seal from a bank check image. The grayscale Chinese square seal is extracted from the color bank check image according to the color information. Different Chinese characters have different stroke features and background evenness. To process each character in the square seal respectively, the extracted square seal is divided into four sub-squares. The background across each sub-square of the grayscale seal image is smoothed by top-hat transformation. The size of structuring element in top-hat transformation might have a great influence on the segmentation. The optimal size of the structuring element for the top-hat transformation on each sub-square is iteratively estimated according to the local foreground area. Each top-hat processed sub-square is binarized by Otsu's method. In each binary sub-square, holes smaller than a threshold are filled which is proportional to the ratio of the foreground area to the area of the whole sub-square. The experiment result shows that the proposed algorithm can correctly segment Chinese characters with intricate and dense strokes in a bank check square seal. Adhesion and incompleteness distortions in the segmentation results are reduced, even when the original square seal has a poor quality.

Multiscale Morphological Image Simplification

Abstract: Image simplification reduces the information content of an image, being frequently used as a preprocessing stage in several algorithms to suppress undesired details such as noise. Morphological filters, commonly used for this purpose, have as main drawbacks the asymmetric treatment of peaks and valleys and the difficulty to choose an appropriate structuring element size. Here, we propose a self-dual multiscale image simplification operator with sound edge preservation properties. This enables us to represent the inherent multiscale nature of real-world images by embedding the original signal into a family of derived signals, which represent simplified versions of the image obtained by successively removing its structures across scales. Thus, it is possible to analyze the different representation levels to extract the interest features, and the definition of a structure element size does not constitute a problem anymore. Based on these notions, we present some experiments on image segmentation, a basic step of various pattern recognition approaches.

A Method of Vehicle License Recognition Based on Mathematical Morphology

Abstract: The features of Mathematical Morphology are quick and brief function and so on.The paper uses Mathematical Morphology for the vehicle license recognition.After the pretreatment and threshold values segmentation,the erosion operation of Morphology is to edge detection and recognition of the vehicle license.The way is going to detect only pixel of edge breadth of the object,so it is high position precision.Finally,the matlab 7.0 is used to simulate the images.A lot of experiments show that,the algorithm is of practicality.

Extraction of fine blood vessels from an ultrasound image by adaptive local image processing

Abstract: This paper discusses some useful image processing techniques for an ultrasound image using adaptive morphological operations. They are defined in such a way that the used structuring element is a function of local image to respond its characteristics. The defined mathematical morphology has almost the same mathematical structure and properties as the conventional one. Besides, the former has an advantageous image processing to others. For example, the simultaneous smoothing and enhancement can be implemented. We applied this technique to ultrasound images to extract blood vessels and showed its useful function. We discussed parameter evaluation and setting from a statistical point of view. Experimental results are also demonstrated.

Mathematical Morphology (MM) Features for Classification of Cancerous Masses in Mammograms

Abstract: One of the important attributes of cancerous masses is their malignancy as it suggests a rapid growth of the cancer and possibility of metastasis. Malignancy, which denotes a special pathology of the tissue, is closely related to the existence of quasi-linear structures (spicules) emanating from the central mass. Hence, the tasks of malignancy and spicularity assessment are very often treated jointly. We propose a novel set of features enriching already existing pool of features for classification of masses. Our features are based on simple MM operations, pixel counting, and some basic algebra. To be more specific, given a contour of a cancerous mass we compute a sequence of dilations, and then count the number of pixels on the inner and the outer contour of each dilation. The contour pixel numbers are plotted against the size of the disk-shaped structuring element. The MM features are calculated from the plot via simple algebraic operations. The crucial point is that all the features are zero iff the input contour is circular. This distinctive property forms a basis for successful classification with the A z values higher than for the features existing in the literature. The additional advantage of our approach is the simplicity of the proposed features. In contrast to many features found in the literature, no sophisticated algorithms are employed, so reimplementation of the features should be easy for anyone interested.

Improved morphological method in the detection of log CT image with defect

Abstract: Mathematical morphology is a new subject established based on rigorous mathematical theories. In the basis of set theory, mathematical morphology is used for image processing, analysing and comprehending. It is a powerful tool in the geometric morphological analysis and description. It has become a new theory in the digital image processing field. Morphology offers a unified and powerful approach to numerous image processing problems. Mathematical morphology is a mathematic tool to analyze the image based on the structuring element. Moreover, it has deep influence on the image processing theory and technology. Edge is the basic feature in the image. It involves a lot of valuable target information of boundary, which is used for image processing, target identifying and image filtering. The mathematical morphology is an effective theory used to locate the image edge. In the paper improved multi-scale and structuring element in mathematical morphology is used to detect log CT image with defect, and the omnidirectional structure element provides a new method in log defect recognition.

A Detailed Protected Method of Extracting Digital Terrain Model from Airborne Laser Point Cloud in Urban Areas

Abstract: Recent progresses in airborne light detection and ranging(LIDAR) technology allow rapid and inexpensive measurements of topography over large areas.This technology becomes a primary method for generating high-resolution digital terrain model(DTM) that is essential to numerous applications.LIDAR techniques represent a new and fruitful approach in the determination of digital surface model.LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path.One of the goals in processing LiDAR point cloud is to set up filtering methods in order to extract the digital terrain model automatically.There are a lot of methods of extracting DTM from LiDAR point cloud,but removing nonground points is still a challenging task.In order to generate a DTM,measurements from nonground features(buildings,vehicles and vegetation etc.) have to be removed.Mathematical morphology is based on set theory to extract features from an image and fits for signal analysis.It has been demonstrated that operation opening can remove objects on ground(vegetations,buildings,etc.) from LiDAR point cloud and reserve objects larger than structuring element.To find out a proper structuring element is very difficult.The larger window size of structuring element would over filter DTM and the smaller window would not filter all nonground objects.All nonground objects could be removed by one time opening operation theoretically if the structuring element is appropriate.Unfortunately there is no such structuring element in reality because of the complexity of terrain surface.This can be resolved by gradually increasing structuring element,which can filter objects on ground,but some terrain measurements also have been filtered at the same time.This results in too flat on terrain surface and the extracted DTM is lower than the real.In this paper,a restricted filter method is developed to solve the terrain details loss caused by over filtered.The overall process starts from an initial set of points,then gradually increases the structuring element which is restricted with elevation difference thresholds at the same time.Elevation difference threshold is set by undulation of terrain,and it can control result.The ratio of elevation difference of before and after opening to structuring element window size is k,which represents undulating grade.The larger the value of k is,the steeper the area is,and vice versa in order to reserve the terrain details,the threshold of k is set k_0.Result value of filtered remains invariable when kk_0 otherwise equals result value of opening.This procedure iterates while the window size of structuring element gradually increases.The times of iteration are determined by window size of and increasing style.The different size of nonground objects(vegetation,building etc.) are filtered by iteration and the details could be reserved.The feasibility and validity of the method is demonstrated with an example data of airborne laser scanning system developed by China.The results show that the filter can remove most of the nonground points effectively.

Facial features matching using a virtual structuring element

Abstract: Face analysis in a real-world environment is a complex task as it should deal with challenging problems such as pose variations, illumination changes and complex backgrounds. The use of active appearance models for facial features detection is often successful in restricted environments, but the performance decreases when applied in unconstrained environments. Therefore, in this paper, we introduce a novel method that integrates the knowledge of a face detector inside the shape and the appearance models by using what we call a 'virtual structuring element' (VSE). In this way the possible settings of the active appearance models are constrained in an appearance-driven manner. The use of a virtual structuring element in an active appearance model provides increased performance in both accuracy and robustness over standard active appearance models applied to different environments.

Improved multi-scale and structuring element morphological detection in the log CT image

Abstract: Mathematical morphology is a new subject established based on rigorous mathematical theories. In the basis of set theory, mathematical morphology is used for image processing, analysing and comprehending. It is a powerful tool in the geometric morphological analysis and description. It has become a new theory in the digital image processing field. Moreover, it has deep influence on the image processing theory and technology. Edge is the basic feature in the medical image. It involves a lot of valuable target information of boundary, which is used for image processing, target identifying and image filtering. The mathematical morphology is an effective theory used to locate the image edge. In the paper multi-scale and structuring element in mathematical morphology is used to detect log CT image with defect, and provides a new method in log defect recognition.

A new video object segmentation algorithm using the morphological technique

Abstract: In this paper, we propose a new video object segmentation algorithm using the morphological technique. Several video object segmentation algorithms use mathematical morphology to generate the object masks, however the operations of the mathematical morphology have two drawbacks: (1) high computation complexity and (2) the quality of the object masks depends on the chosen morphological structuring element. There are many techniques to speed up the morphological operations by hardware implementation, but without discussions about reducing the influence of the choice of the structuring elements. By adding a pre-processing mechanism, the proposed algorithm effectively reduce the influences of the chosen structuring elements based on continuity of shape features and times of morphological operations. Experimental results show that our algorithm can improve the speed of filling operations of the object masks and accuracy of segmentation.

Medical Ultrasound Image Denoising Based on Adaptive Morphological Despeckling Filter

Abstract: In view of speckle noise in medical ultrasound image, a denosing method based on adaptive morphological filter was proposed. Firstly, a set of structuring factors was constructed to detect different sudden changes of pixel values in images. Then a corresponding structuring element for morphological filtering was established for each structuring factor. Finally, the most possible structures of sudden changes in each pixel’s neighborhood were detected by using the set of structuring factors and a morphological filtering was performed to the pixel with the corresponding structuring element. The comparison experiment was carried out on simulated and real ultrasound images by using the proposed method, the speckle reducing anisotropic diffusion and different scale morphological filters, respectively. It is shown that the proposed method averagely improves the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR) and Figure of Merit (FOM) of ultrasound images by 15%, 37% and 69%. Therefore, the adaptive morphological despeckling filter may be an effective method to suppress speckle in ultrasound images.

Omnidirectional morphology applied to wood defects testing by using computed tomography

Abstract: Wood non-destructive testing technology is a new and comprehensive subject. An approach of image edge detection based on morphology of omnidirectional and multi-scale structuring element is constructed by weighting method of morphological operation. In recent years wood non-destructive testing has achieved fast development. Computed tomography (CT) scanning technology has been applied to the detection of internal defects in the logs for the purpose of obtaining prior information, which can be used to arrive at better log sawing decision. In addition to the recognition of wood internal defects, computed tomography (CT) technology can be applied to wood non-destructive testing. Mathematical morphology of omnidirectional and multi-scale element is defined to suppress noise and adapt to different edge in the image. The results of simulation in wood image demonstrate that the method performs better not only in edge detection but also in noise-suppression than classical edge detection operator.

Novel CB Morphological Series-parallel Complex Filter for Suppressing Speckle Noise

Abstract: Based on Contour Bougie morphology (CB morphology) and generalized multiple structuring elements morphology,a novel series-parallel complex morphological filter was constructed for suppressing the speckle noise in images. Firstly,an omnidirectional multiple structuring element morphology open-Max and close-Min parallel filter was adopted to filter the noise at the cost of minimal loss of image geometrical details,and then a CB morphology open and close operation series filter was used to filter the remaining speckle noise. Thus,it can not only effectively suppress the speckle noise in images but also preserve more geometrical structure features of the images. The results of analysis and simulation prove that the algorithm presented in the paper is efficient.

Removing DC offset and de-noising for inspecting signal based on mathematical morphology filter processing

Abstract: A new signal processing method based on the theory of mathematical morphology filter for Removing DC offset and noise pollution about the on-site sampled data is proposed. Conclusion that the ratio of the signal numerical frequency to length of flat structuring element determines the attenuation magnitude of signal and the principia of length of flat structuring element has been found, by analyzing inhesion meaning of base operation of mathematical morphology and then making up combination morphological filter with Maragos type of open-close and close-open. This method possesses following advantages: high calculation speed, easy implementation of hardware and better use value. The arithmetic is simulated with Matlab software and then transplanted to DSP hardware platform and practiced in on-site. It is proved that the proposed method can deal with the sampled data corrupted by on-site noise and the DC of set efficiently. Also, the processed results can meet the requirement in real time well.

Feature Extraction Algorithm for Distributed Spacecraft System Based-on Dynamically Structured Element

Abstract: It is vital to build and select structuring element in feature extraction based-on mathematical morphology. According to the character of the distributed spacecraft system, a feature extraction algorithm based-on dynamically structured element is proposed in this paper. The thought of this algorithm is summarized as follows: (1) firstly, approximate relative position and attitude is calculated according to the approximate attitude and orbital information of the spacecraft; (2) secondly, a structured element database is built according to the shape of the objective spacecraft and the result above; (3) thirdly, according to the results of (1) and (2), feature extraction is implemented based on dynamically re-structured element; (4) finally, the simulation results show that proposed algorithm is valid for the distributed spacecraft system.

Application of omnidirectional structure element of mathematical morphology to wood computed tomography testing

Abstract: Wood nondestructive testing technology is a new and comprehensive subject. In recent years it has achieved fast development. X-ray computed tomography (CT) scanning technology has been applied to the detection of internal defects in the logs for the purpose of obtaining prior information, which can be used to arrive at better log sawing decision. In addition to the recognition of wood internal defects, computed tomography (CT) technology can be applied to wood nondestructive testing. Mathematical morphology of omnidirectional and multi-scale element, an algorithm of image edge-detection was proposed in the paper. Mathematical morphology of omnidirectional and multi-scale element is defined to suppress noise and adapt to different edge in the image. An approach of image edge detection based on morphology of omnidirectional and multi-scale structuring element is constructed by weighting method of morphological operation. The results of simulation in wood image demonstrate that the method performs better not only in edge detection but also in noise-suppression than classical edge detection operator.