Showing papers on "Structuring element published in 2007"

Automatic Building Extraction in VHR Images Using Advanced Morphological Operators

Abstract: This paper presents a new method for buildings extraction in Very High Resolution (VHR) remotely sensed images based on binary mathematical morphology (MM) operators. The proposed approach involves several advanced morphological operators among which an adaptive hit-or-miss transform with varying sizes and shapes of the structuring element and a bidimensional granulometry intended to determine the optimal filtering parameters automatically. A clustering-based approach for image binarization is also introduced. This one avoids an empirical thresholding of input panchromatic images. Experiments made on a Quickbird VHR-image show the effectiveness of the method.

New Tensorial Representation of Color Images: Tensorial Morphological Gradient Applied to Color Image Segmentation

Abstract: This paper proposes a new Tensorial Representation of HSI color images, where each pixel is a 2 times 2 second order tensor, that can be represented by an ellipse. A proposed tensorial morphological gradient (TMG) is defined as the maximum dissimilarity over the neighborhood determined by a structuring element, and is used in the watershed segmentation framework. Many tensor dissimilarity functions are tested and other color gradients are compared. The comparison uses a new methodology for qualitative evaluation of color image segmentation by watershed, where the watershed lines of the n most significant regions are overlaid on the original image for visual comparison. Experiments show that the TMG using Frobenius norm dissimilarity function presents superior segmentation results, in comparison to other tested gradients.

Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification

Abstract: The paper presents the classification performance of an automatic classifier of the electrocardiogram (ECG) for the detection abnormal beats with new concept of feature extraction stage. Feature sets were based on ECG morphology and RR-intervals. Configuration adopted a Kohonen self-organizing maps (SOM) for analysis of signal features and clustering. In this study, a classifier was developed with SOM and learning vector quantization (LVQ) algorithms using the data from the records recommended by ANSI/AAMI EC57 standard. This paper compares two strategies for classification of annotated QRS complexes: based on original ECG morphology features and proposed new approach - based on preprocessed ECG morphology features. The mathematical morphology filtering is used for the preprocessing of ECG signal. The problem of choosing an appropriate structuring element of mathematical morphology filtering for ECG signal processing was studied. The performance of the algorithm is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. Using this method the results of recognition beats either as normal or arrhythmias was improved.

Word-wise Script Identification from Bilingual Documents Based on Morphological Reconstruction

Abstract: In a multi-lingual country like India, english has proven to be the binding language. So, a line of a bilingual document page may contain text words in regional language and numerals in English (printed or handwritten). For optical character recognition (OCR) of such a document page, it is necessary to identify different script forms before running an individual OCR of the scripts. In this paper an automatic technique for script identification at word level based on morphological reconstruction is proposed for two printed bilingual documents of Kannada and Devnagari containing English numerals (printed and handwritten). The technique developed includes a feature extractor and a classifier. The feature extractor consists of two stages. In the first stage, shape (eccentricity, aspect ratio) and directional stroke features (horizontal and vertical) are extracted based on morphological erosion and opening by reconstruction using the line structuring element. The average height of all the connected components of an image is used to threshold the length of the structuring element. In the second stage, average pixel distribution is obtained from these resulting images. The k-nearest neighbour algorithm is used to classify the new word images. The proposed algorithm is tested on 2250 sample words with various font styles and sizes. The results obtained are quite encouraging.

An efficient method for text detection in video based on stroke width similarity

Abstract: Text appearing in video provides semantic knowledge and significant information for video indexing and retrieval system. This paper proposes an effective method for text detection in video based on the similarity in stroke width of text (which is defined as the distance between two edges of a stroke). From the observation that text regions can be characterized by a dominant fixed stroke width, edge detection with local adaptive thresholds is first devised to keep text- while reducing background-regions. Second, morphological dilation operator with adaptive structuring element size determined by stroke width value is exploited to roughly localize text regions. Finally, to reduce false alarm and refine text location, a new multi-frame refinement method is applied. Experimental results show that the proposed method is not only robust to different levels of background complexity, but also effective to different fonts (size, color) and languages of text.

Utilisation of the image analysis method for the detection of the morphological anisotropy of calcite grains in marble

Abstract: This paper presents the development and utilisation of an automated image processing algorithm for detection and analysis of grains. Using optical polarising microscopy, a set of colored images are collected from an area on a thin section. A filtering operation, using rotation of a morphological alternating sequence filter (based on a structuring element), is used to remove twinning features within individual grains. Filtering is followed by the watershed segmentation technique to determine grain boundaries. The method is used for the identification of calcite grains in marble and the subsequent analysis of morphological anisotropy.

Morphological Segmentation of Urban Structures

Abstract: Automatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.

X-Ray Image Enhancement Based on Multiscale Morphology

Abstract: For improving the quality of x-ray image, this paper presents a method for enhancing x-ray image using multiscale mathematical morphology. The conventional theoretical concept of image enhancement has been extended to the regime of multiscal mathematical morphology. Structuring element in this method is multiscale. Bright and dark features at various scales of x-ray image are extracted using multiscale (white or black) tophat transformation. These multiscale features are combined to reconstruct the final modified image. Therefore the contrast of x- ray image is enhanced locally and the features of the original image are more clear. Morphological difference towers are bulk to implement the method. The proposed algorithm has been executed on the orthopaedic x-ray image for testing its result. Some images with other standard or improved methods for contrast enhancement are given in the paper. Compared with these images multiscale mathematical morpholgical X-ray image enhancement technique has achieved a better visual effect. Multiscale mathematical morphology can be used in x-ray image process.

Shoeprint Image Retrieval by Topological and Pattern Spectra

Abstract: In this paper, we propose a novel technique for the automatic classification of noisy and incomplete shoeprint images, based on topological and pattern spectra. We first consider the pattern spectrum proposed by Maragos. We extend each spectrum with the spectrum for the complement image. We also propose a topological spectrum for a shoeprint image, based on repeated open operations with increasing size of structuring element, giving a distribution of Euler numbers. The normalised differential of this series gives the topological spectrum. We secondly propose a hybrid algorithm which uses a distance measure based on a combination of both spectra as the feature of a shoeprint image. To evaluate the performance of the techniques, we use a database of 500 'clean' shoeprints to generate five test databases each with 2500 degraded images, such as Gaussian noise, incompletion, rotation, rescale, and scene background. The statistical evaluations in terms of precision vs. recall are given in the final section. Tests show that our hybrid technique combining both spectra gives significant improvements over previously published results for edge direction histogram.

Segmentation of high-resolution multispectral image based on extended morphological profiles

Abstract: High-resolution multispectral remote sensing image provides both spectral and structural information about land cover/land use types. In segmentation of such complex image scenes with obvious texture, the efficient image segmentation is required. In this study, a method for high resolution image segmentation based on the extended morphological profiles is proposed. First, fundamental morphological vector operations (erosion and dilation) are defined by the extension, taking into account the spatial and spectral information in simultaneous fashion. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profiles, which is constructed based on the repeated use of openings and closings by reconstruction with a structuring element (SE) of increasing size. Then, the morphological multiscale characteristic (MMC) of each pixel is gained through the derivative of the extended morphological profiles (DEMP). A modified method was proposed to obtain the right morphological characteristics of the pixel, which will be used for the final segmentation results. Finally, a simple region merging method based on the distance between two centroids of the neighboring regions was adopted to further improve the segmentation result. The proposed approach is applied to high- resolution QuickBird multispectral images from urban, agricultural and forest areas for evaluation and comparison with existing methods, in terms of qualitative visual inspection and quantitative criteria. The proposed method demonstrated better performance than the classical morphological segmentation approaches.

Fingerprint Thinning Algorithm Based on Mathematical Morphology

Abstract: Thinning plays an important role in automatic fingerprint identification system (AFIS). This paper applies hit-miss transform which is based on mathematical morphology to fingerprint thinning, It proposes a new view on thinning algorithm based on the transform, and improves structuring element on the ground of predecessor's research, getting improved thinning effect.

Image processing method and image inspecting method

Abstract: The present invention relates to an image processing method and image inspecting method with high versatility which enable efficient and highly accurate proof of authenticity of a digital image. The image processing method subjects at least a part of a digital image which can exist temporarily or continuously in a falsification-vulnerable environment, to a first morphology operation using a predetermined structuring element, to process the digital image. The image inspecting method subjects the digital image thus processed, to a second morphology operation using the same structuring element as in the first morphology operation. The morphology operations have the idempotent and the presence or absence of falsification can be detected by determining identity of images before and after the second morphology operation.

A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Images

Abstract: Mathematical morphology (MM) is a theory for the analysis of spatial structures, based on set-theoretical notions and on the concept of translation. MM has many applications in image analysis such as edge detection, noise removal, object recognition, pattern recognition and image segmentation in a.o. geosciences, materials science, the biological and medical world [13, 15]. MM was originally developed for binary images only. The basic tools of MM are the morphological operators, which transform an image A we want to analyse, using a structuring element B into a new image P(A, B) in order to obtain additional information about the objects in A like shape, size, orientation, image measurements. Apart from the threshold and umbra approach, binary morphology can be extended to morphology for greyscale images using fuzzy set theory, called fuzzy morphology. In this work we will present a new vector-based approach for the extension of MM for greyscale images to colour morphology. We will extend the basic morphological operators dilation and erosion based on the threshold and fuzzy set approach to colour images. Finally in the last section we illustrate an image denoising method using MM to reduce stripes' artefacts in satellite images.

Enhancement of Vascular Angiogram by Multiscale Morphology

Abstract: A scheme for enhancing local contrast of coronary artery based on multiscale morphology is presented in this paper. For the vessel of different diameter imposed on the background with varying intensity, it is impossible to extract properly all of the contrast information by using only one structuring element in morphological top-hat operator. We use a group of Gabor wavelet with different orientations and scales to estimate the vessel diameter on each pixel of image. Then, a combined top-hat operator is implemented on the image for enhancement with the estimated scale of each pixel. Experimentation with clinical angiogram is given in this paper.

Local enhancement of car image for license plate detection

Abstract: In this paper we address the problem of car plate detection. In the first part of algorithm we propose a method for enhancing car plate regions. We estimate the local density of vertical edges in the image as a criterion for local enhancement. In the second part, the vertical edges from the enhanced image is then extracted and feed to a morphological filter to constitute candidate regions for the place of car plate. This filter aims to connect vertical edges closer than the size of a defined structuring element. The output of this process is a number of connected components among which the plate region is. We use some geometrical features of car plate such as shape and aspect ratio to filter out non-plate regions from the candidate list. Using the correlation between the candidate regions and the model of car plate, the most probable region is found. The result of enhancement on car images shows the ability of the proposed method for improving the contrast of plate region(s) in the image. The experimental results on out-door car images confirms the robustness of the proposed method against the severe imaging condition.

Extending Morphological Signatures for Visual Pattern Recognition

Abstract: Morphological signatures are powerful descriptions of the image content which are based on the framework of mathematical morphology These signatures can be computed on a global or local scale: they are called pattern spectra (or granulometries and antigranulometries) when measured on the complete images and morphological profiles when related to single pixels Their goal is to measure shape distribution instead of intensity distribution, thus they can be considered as a relevant alternative to classical intensity histograms, in the context of visual pattern recognition A morphological signature (either a pattern spectrum or a morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element Even if it can be used directly to solve various pattern recognition problems related to image data, the simple definitions given in the binary and grayscale cases limit its usefulness in many applications In this paper, we introduce several 2-D extensions to the classical 1-D morphological signature More precisely, we elaborate morphological signatures which try to gather more image information and do not only include a dimension related to the object size, but also consider on a second dimension a complementary information relative to size, intensity or spectral information Each of the 2-D morphological signature proposed in this paper can be defined either on a global or local scale and for a particular kind of images among the most commonly ones (binary, grayscale or multispectral images) We also illustrate these signatures by several real-life applications related to object recognition and remote sensing

Enhancement of coronary angiogram by estimation of local background

Abstract: Coronary angiogram is an important examination tool in clinical medicine for the precise diagnosis of cardiac disease. It is obtained by injecting of the patient with a contrast medium through a catheter. This paper presents a method to increase vessel contrast and to attenuate background. The enhancement is achieved by subtracting the estimated background from a live (contrast-containing) angiogram. The multi-scale morphology opening, with structuring elements of different dimension for each pixel, is employed to get the estimation of background. The dimension of structuring element for each pixel is calculated by the response difference between opening filtering results of original image with different structuring elements. The proposed algorithm is tested on real x-ray angiogram.

Filter Design Based on the Theory of the Generalized Morphological Filter with Omnidirectional Structuring Element

Abstract: In this paper, a new region-based approach to multi-resolution image fusion is presented. To make fusion decision better, a master image is selected from the source images. Then, it is segmented using the background subtraction based Mumford and Shah algorithm, which significantly improves the segmentation at quality and convergence speed. After that, the fusion rules on every region of the image are determined based on the best priority-first strategy. Moreover, the fusion on the whole image domain is replaced by using different rules on every segmented region of the image. The corresponding regions in other images are located by linear mapping methods. Some numerical results have been given and compared to the pixel-based fusion methods. Despite an increase in complexity, more intelligent fusion rules may be applied to remove side effects like reducing contrast and sensitive to error of registration.

Facial Feature Detection 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 feature 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.

Gray Level Topological Corner Detection

Abstract: A new corner detection algorithm based on the topological median filter is proposed. Topological quasi dilation and erosion operators with circular structuring element are used to detect the corners on gray level images.

Corner Detection Based on Convex Corner Point Element

Abstract: Corner detection in image processing plays an important role in matching, representation, recognition, registration, and computer vision. As one of the template based approaches, the morphological detector has some problems which are still under research, such as the element selection, high computation cost, and inaccurate representation of detected corners. In this paper, a modified morphological method with convex corner points structuring element is proposed in improving running efficiency and detecting accuracy. A new simple structuring element is used in the detector, and the speed performance is improved in a large degree. In experiments, the performance of the new detector is tested on a set of standard images. Six other detectors are involved to provide comparative performance information.

Structuring elements selection in morphology filter

Abstract: As a kind of non-linear filter,morphology filter is applied widely in image processing such as object extraction and noise removing etc.The paper proposes a new adaptive approach for selecting the scale of structuring element in binary images,which depends on the research of the element decomposition and morphology filter.The experiments show that the method can select the scale of structuring element exactly,and at the same time remove the noise completely and effectively.

Detecting Dim Small Targets in Image Data Using Morphological Neural Networks of Background Adaptive Prediction

Abstract: An effective morphological neural network of background clutter prediction for detecting dim small targets in image data was proposed.The target of interest was assumed to have a very small spatial spread,and was obscured by heavy background clutter.The clutter was predicted exactly by morphological neural networks and subtracted from the input signal,leaving components of the target signal in the residual noise.The traditional 3-layer feed forward BP network modal of morphological opening and closing operation was modified by extending the input layer data.For tracking complex background including different sub-structures,the raw image was partitioned to some sub-blocks,in which the training samples were chosen for optimizing the weights of structuring element in the corresponding block.Computer simulations of real image data show better performance compared with other traditional methods.

Morphology based adaptive preprocessing method of infrared image sequence

Abstract: The paper presents an adaptive preprocessing method for infrared image sequence. Firstly an adaptive structuring element (SE) construction algorithm is proposed. Secondly, based on morphological top-hat operation, the adaptive background suppression of image sequence is taken. Finally, the image sequence adaptive segmentation is achieved by using morphological opening operation. Some experimental results demonstrate that our proposed method is effective and adaptive for infrared image sequence preprocessing.

Generalized Morphological 3D Shape Decomposition Grayscale Interframe Interpolation Method

Abstract: One of the main image representations in Mathematical Morphology is the 3D Shape Decomposition Representation, useful for Image Compression, and Pattern Recognition. The 3D Morphological Shape Decomposition representation can be generalized a number of times, to extend the scope of its algebraic characteristics as much as possible. With these generalizations, the Morphological Shape Decomposition 's role to serve as an efficient image decomposition tool was extended to discrete images and grayscale images. This work follows the above line, and further develops it. A new evolutionary branch is added to the 3D Morphological Shape Decomposition's development, by the introduction of a 3D Multi Structuring Element Morphological Shape Decomposition, which permits 3D Morphological Shape Decomposition of 3D binary images (grayscale iamges) into "multiparameter" families of elements. At the beginning, 3D Morphological Shape Decomposition representations are based only on "1 parameter" families of elements for image decomposition. This paper addresses the gray scale interframe interpolation by means of mathematical morphology. The new interframe interpolation method, called 3D Shape Decomposition interpolation is based on morphological 3D Shape Decomposition. This article will present the theoretical background of the morphological interframe interpolation, deduce the new representation and show some application examples. Computer simulations could illustrate results.