Showing papers on "Structuring element published in 2013"

Fast recursive grayscale morphology operators: from the algorithm to the pipeline architecture

Abstract: This paper presents a new algorithm for efficient computation of morphological operations for gray images and the specific hardware. The method is based on a new recursive morphological decomposition method of 8-convex structuring elements by only causal two-pixel structuring elements (2PSE). Whatever the element size, erosion or/and dilation can then be performed during a unique raster-like image scan involving a fixed reduced analysis neighborhood. The resulting process offers low computation complexity combined with easy description of the element form. The dedicated hardware is generic and fully regular, built from elementary interconnected stages. It has been synthesized into an FPGA and achieves high-frequency performances for any shape and size of structuring element.

Machine Vision for the Inspection of Natural Products

Abstract: List of Contributors 1. Like Two Peas in a Pod B.G. Batchelor Editorial Introduction 1.1 Advantages of Being Able to See 1.2 Machine Vision 1.2.1 Model for Machine Vision Systems 1.2.2 Applications Classified by Task 1.2.3 Other Applications of Machine Vision 1.2.4 Machine Vision Is Not Natural 1.3 Product Variability 1.3.1 Linear Dimensions 1.3.2 Shape 1.3.3 Why Physical Tolerances Matter 1.3.4 Flexible and Articulated Objects 1.3.5 Soft and Semi-fluid Objects 1.3.6 Colour Variations 1.3.7 Transient Phenomena 1.3.8 Very Complex Objects 1.3.9 Uncooperative Objects 1.3.10 Texture 1.4 Systems Issues 1.5 References 2. Basic Machine Vision Techniques B.G. Batchelor and P.F. Whelan Editorial Introduction 2.1 Representation of Images 2.2 Elementary Image Processing Functions 2.2.1 Monadic Point-by-point Operators 2.2.2 Dyadic Point-by-point Operators 2.2.3 Local Operators 2.2.4 Linear Local Operators 2.2.5 Non-linear Local Operators 2.2.6 N-tuple Operators 2.2.7 Edge Effects 2.2.8 Intensity Histogram [hpi, hgi, he, hgc} 2.3 Binary Images 2.3.1 Measurements on Binary Images 2.3.2 Shape Descriptors 2.4 Binary Mathematical Morphology 2.4.1 Opening and Closing Operations 2.4.2 Structuring Element Decomposition 2.5 Grey-scale Morphology 2.6 Global Image Transforms 2.6.1 Hough Transform 2.6.2 Two-dimensional Discrete Fourier Transform 2.7 Texture Analysis 2.7.1 Statistical Approaches 2.7.2 Co-occurrence Matrix Approach 2.7.3 Structural Approaches 2.7.4 Morphological Texture Analysis 2.8 Implementation Considerations 2.8.1 Morphological System Implementation 2.9 Commercial Devices 2.9.1 Plug-in Boards: Frame-grabbers 2.9.2 Plug-in Boards: Dedicated Function 2.9.3 Self-contained Systems 2.9.4 Turn-key Systems 2.9.5 Software 2.10 Further Remarks 2.11References 3. Intelligent Image Processing B.G. Batchelor Editorial Introduction 3.1 Why We Need Intelligence 3.2 Pattern Recognition 3.2.1 Similarity and Distance 3.2.2 Compactness Hypothesis 3.2.3 Pattern Recognition Models 3.3 Rule-based Systems 3.3.1 How Rules are Used 3.3.2 Combining Rules and Image Processing 3.4 Colour Recognition 3.4.1 RGB Representation 3.4.2 Pattern Recognition 3.4.3 Programmable Colour Filter 3.4.4 Colour Triangle 3.5 Methods and Applications 3.5.1 Human Artifacts 3.5.2 Plants 3.5.3 Semi-processed Natural Products 3.5.4 Food Products 3.6 Concluding Remarks 3.7 References 4. Using Natural Phenomena to Aid Food Produce Inspection G. Long Editorial Introduction 4.1 Introduction 4.2 Techniques to Exploit Natural Phenomena 4.3 Potato Sizing and Inspection 4.4 Stone Detection in Soft Fruit Using Auto-fluorescence 4.5 Brazil Nut Inspection 4.6 Intact Egg Inspection 4.7 Wafer Sizing 4.8 Enrobed Chocolates 4.9 Conclusion 4.10 References 5. Colour Sorting in the Food Industry S.C. Bee and M.J. Honeywood Editorial Introduction 5.1 Introduction 5.2 The Optical Sorting Machine 5.2.1 The Feed System 5.2.2 The Optical System 5.2.3 The Ejection System 5.2.4 The Image Processing Algorithms 5.3 Assessment of Objects for Colour Sorting 5.3.1 Spectrophotometry 5.3.2 Monochromatic Sorting 5.3.3 Bichromatic Sorting 5.3.4 Dual Monochromatic Sorting 5.3.5 Trichromatic Sorting 5.3.6 Fluorescence Techniques 5.3.7 Infrared Techniques 5.3.8 Optical Sorting with Lasers 5.4 The Optical Inspection System 5.4.1 Illumination 5.4.2 Background and Aperture 5.4.3 Optical Filters 5.4.4 Detectors 5.5 The Sorting System 5.5.1 Feed 5.5.2 Ejection 5.5.3 Cleaning and Dust Extraction 5.5.4 The Electronic Processing System 5.6 The Lim

Contrast Enhancement Techniques for Images- A Visual Analysis

Abstract: Image enhancement is one of the most interesting and visually appealing areas of image processing. It involves operations such as enhancing contrast, reducing noise for improving the quality of the image. This paper presents an analysis of the mathematical morphological approach with comparison to various other state-of-art techniques for addressing the problems of low contrast in images. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. This method is simple and effective for global contrast enhancement of images but it suffers from some drawbacks. Contrast Limited Adaptive Histogram Equalization (CLAHE) enhances the local contrast of the images without the amplification of the noise. Morphological Contrast enhancement is performed using the white and black top-hat transformation. It can be performed at a single scale or at multiple scales of the structuring element. The structuring element can be of various shapes and sizes.

A Learning Framework for Morphological Operators Using Counter–Harmonic Mean

Abstract: We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.

Swarm Optimization of Structuring Elements for VHR Image Classification

Abstract: Mathematical morphology has shown to be an effective tool to extract spatial information for remote-sensing image classification. Its application is performed by means of a structuring element (SE), whose shape and size play a fundamental role for appropriately extracting structures in complex regions such as urban areas. In this letter, we propose a novel method, which automatically tailors both the shape and the size of the SE according to the considered classification task. For this purpose, the SE design is formulated as an optimization problem within a particle swarm optimization framework. The experiments conducted on two real images suggest that better accuracies can be achieved with respect to the common procedure for finding the best regular SE, which, so far, is heuristically done.

Image Processing (IP) Through Erosion and Dilation Methods

Abstract: The regulated morphological transforms still have some redundancies, though it takes more memory space and time for processing and searching the multimedia data. In this paper, the redundancies that are present in the regulated morphological transform are removed. To store the image in the for m of reduced regulated morphological transforms requires less memory space, less processing and searching time. Morphological processing is constructed with operations on sets of pixels. Binary morphology uses only set membership and is indifferent to the value, such as gray level or color, of a pixel. We will examine some basic set operations and their usefulness in image processing. A standard morphological operation is the reflection of all of the points in a set about the origin of the set. The origin of a set is not necessarily the origin of the base. Shown at the right is an image and its reflection about a point with the original image in green and the reflected image in white. Dilation and erosion are basic morphological processing operations. They are defined in terms of more elementary set operations, but are employed as the basic elements of many algorithms. Both dilation and erosion are produced by the interaction of a set called a structuring element with a set of pixels of interest in the image.

Estimation of tortuosity and reconstruction of geodesic paths in 3d

Abstract: The morphological tortuosity of a geodesic path in a medium can be defined as the ratio between its geodesic length and the Euclidean distance between its two extremities. Thus, the minimum tortuosity of all the geodesic paths into a medium in 2D or in 3D can be estimated by image processing methods using mathematical morphology. Considering a medium, the morphological tortuosities of its internal paths are estimated according to one direction, which is perpendicular to both starting and ending opposite extremities of the geodesic paths. The used algorithm estimates the morphological tortuosities from geodesic distance maps, which are obtained from geodesic propagations. The shape of the propagated structuring element used to estimate the geodesic distance maps on a discrete grid has a direct influence on the morphological tortuosity and has to be chosen very carefully. The results of our algorithm is an image with pixels p having a value equal to the length of the shortest path containing p and connected to two considered opposite boundaries A and B of the image. The analysis of the histogram of the morphological tortuosities gives access to their statistical distribution. Moreover, for each tortuosity the paths can be extracted from the original image, which highlights the location of them into the sample. However, these geodesic paths have to be reconstructed for further processing. The extraction, because applying a threshold on the tortuosities, results in disconnected components, especially for highly tortuous paths. This reconstruction consists in reconnecting these components to the geodesic path linking the two opposite faces, by means of a backtracking algorithm.

Automatic Detection of Orientation of Mapped Units via Directional Granulometric Analysis

Abstract: Automatic detection of orientation of mapped units via directional granulometries is addressed in this letter. A flat symmetric structuring element (B) of size 3 × 3 with nine elements, which is a disk in eight-connectivity grid, is decomposed into four 1-D directional structuring elements (Bis). Multiscale opening transformations are performed on each mapped unit with respect to these four directional structuring elements to eventually compute direction-specific morphologic entropy values. Based on these values, the orientations of mapped units are classified into four classes that include those units with orientations of: i) South East-North West (B1), ii) North-South (B2), iii) South West-North East (B3), and iv) East-West (B4). We demonstrated this approach on five model objects, and nine major river basins extracted from DEM of Indian peninsular. This approach yields quantitative results, based on which the mapped units could be automatically classified into four different orientations.

Comparison of automatic blood vessel segmentation methods in retinal images

Abstract: Blood vessel segmentation can be used for automatic retinal disease screening system. In this paper, comparison of automatic blood vessel segmentation methods in retinal images is presented and discussed. Morphological operation for the automatic segmentation of blood vessels in retinal images has been proposed and the results are compared with matched filter technique using local entropy thresholding. Blood vessels in retinal images are enhanced by the application of matched filter. The gray levels are spatially distributed by using local entropy based thresholding. Label filtering is performed by connected pixel labeling to eliminate the misclassified and isolated pixels. The blood vessels are segmented by using morphological opening based on structuring element. These methods were evaluated on the publicly available DRIVE database and the database contains retinal images along with the ground truth data that has been precisely marked by the experts. Morphological operation is more suitable for blood vessel segmentation as compared to the local entropy thresholding.

Automatic urban road extraction on DSM data based on fuzzy ART, region growing, morphological operations and radon transform

Abstract: In recent years, an automatic urban road extraction, as part of Intelligent Transportation research, has attracted the researchers due to the important role for the next modern transportation where urban area plays the main role within the transportation system. In this work, we propose a new combination of fuzzy ART clustering, Region growing, Morphological Operations and Radon transform (ARMOR) for automatic extraction of urban road networks from the digital surface model (DSM). The DSM data, which is based-on the elevation of surface, overcome a serious building's shadow problem as in the aerial photo image. Due to the different elevation between the road and the buildings, the thresholding technique yields a fast initial road extraction. The threshold values are obtained from Fuzzy ART clustering of the geometrical points in the histogram. The initial road is then expanded using region growing. Though most of the road regions are extracted, it contains a lot of non-road areas and the edge is still rough. A fast way to smoothing the region is by employing the morphology closing operation. Furthermore, we perform the road line filter by opening operation with a line shape structuring element, where the line orientation is obtained from the Radon Transform. Finally, the road network is constructed based-on B-Spline from the extracted road skeleton. The experimental result shows that the proposed method running faster and increases the quality and the accuracy about 10% higher than the highest result of the compared method.

Defects Detection of Sheet Metal Parts Based on HALCON and Region Morphology

Abstract: In order to realize the manufacturing defects detection of sheet metal parts, a kind of defects detection method of the sheet metal parts based on region morphology has been put forward based on HALCON using the mathematical morphology knowledge: by choosing the proper structuring element and neatly applying dilation, erosion, opening and closing on the defects images, the defect part is extracted. The experiment shows that the effect of this method is good and the calculation and processing speed is fast.

Similarity colour morphology

Abstract: Mathematical morphology was developed for binary images and extended to grey-level images. To date there is no widely accepted extension of mathematical morphology to colour. We present a unifying concept for binary, grey-level and colour morphology introducing similarity measures to form classes of colour morphological operators. We define similarity criteria as the basis for mathematical morphology with flat and non-flat structuring elements. Results for dilation, erosion and hit-or-miss transforms on binary, grey-level and colour images are presented.

Object Extraction and Boundary Tracing Algorithms for Digital Image Processing: Comparative Analysis: A Review

Abstract: Adaptive Histogram Equalization and Edge detection techniques for particle analysis, a comparative study have been shown and a new algorithm is proposed for removing the problem of non-uniform background illumination in biological images such as visualizing and estimation of growth of a fungus in a particular sample to transform the input image to its indexed form with maximum accuracy involving morphological openings and structuring element design using Morphological Opening. For applications, including particles/objects to be studied or analyzed, these techniques give faulty results due to changes in actual shapes and sizes of the particles in the resulting image. Morphology is related to the shapes and digital morphology is a way to describe and analyze the shape of a digital object. In biology, morphology relates more directly to shape of an organism such as bacteria. Morphological opening is a name specific technology that creates an output image such that value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. Other techniques include edge detection using sobel and canny filters, and other sharpening filters that enhance the edges of the objects present in the image. A new algorithm for object extraction, boundary tracing and image enhancement have been proposed based on combination of Morphology, Weiner filters and thresholding techniques.

The role of structuring element in morphological image segmentation

Abstract: Image segmentation is one of the most commonly used techniques in digital image processing. In image segmentation edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less relevant, preserving the important Structuring properties of an image. In recent days new edge detection algorithms are published each year and among these, watershed algorithm which is inherently based on mathematical morphology is an accepted one. In watershed algorithm, structuring elements plays a very unique role. The basic philosophy of using the structuring element in mathematical morphological operation lies in the fact that it serve as a seed or needle to collect the image information. In this paper a new approach of image segmentation and edge detection is presented with watershed algorithm using twelve new and proposed arbitrary structuring elements and morphological smoothing operation to reduce the over segmentation problem has been proposed and accordingly their performances are compared and discussed.

A Binary Morphology-Based Clustering Algorithm Directed by Genetic Algorithm

Abstract: Mathematical morphology is a formalism largely used in image processing for implementing many different tasks. Several operators that support the formalism have also been successfully used for inducing data clusters. Particularly, the Binary Morphology Clustering Algorithm (BMCA) is one of such inductive methods which, given a set of input patterns and morphological operators, produces clusters of patterns as output. BMCA results, however, are dependent on suitable user-defined values for the set of parameters the algorithm employs namely, the resolution of its initial discretization process, the threshold associated with a distance metric, the threshold associated with region density and the structuring element embedded in morphological operators. This paper proposes a combined approach where an evolutionary algorithm is employed for searching suitable parameter values for BMCA aiming at producing more efficient results as far as the clustering process is concerned. The proposal was implemented as the system BMCAbyGA, used in several successful clustering experiments described in the final part of the paper. BMCAbyGA has been applied to a Cartesian Genetic Programming approach for the automatic construction of image Alters in hardware.

Simd algorithm for image dilation and erosion processing

Abstract: Systems and methods may receive an input image for data processing, divide the input image into a plurality of blocks, each block including a plurality of rows, and each row including a plurality of pixels and process each pixel in the input image within a row in parallel with a user-defined template. In one example, the user-defined template is to include a structuring element and a row pixel mask.

Mathematical Morphology Applied in Object Tracking on Position-Based Visual Servoing

Abstract: Position-based visual servoing is a technique for vision-based robot control, which operates in the 3D workspace and uses a known model of the object to perform tasks of feature extraction and positioning. Among the many applications, position-based visual servoing is used in industrial environments, for object manipulation operations on conveyor belts. Mathematical morphology, a well-known computer vision technique, can be used for features extraction, representation of objects and to perform actions such as recognition by images. The representation of the object occurs with the assistance of a structuring element and the morphological operations are used to locate the object center. This work explores the binary mathematical morphology whose operators are trained by a reconfigurable system developed in a FPGA with the representation of the 3D model of the object to be applied in visual servoing in controlled applications such as tasks in industrial environments.

Vascular Tree Segmentation in Fundus Images Using Curvelet Transform

Abstract: In retinal images, vessel segmentation methods are an important component of circulatory blood vessel analysis systems. This paper introduces an effective approach to segment the vessels in the fundus images. The fundus images are first enhanced using curvelet transform, then segmentation is performed using morphological operations with a modified structuring element and length filtering. The proposed method has been tested on 40 images of the DRIVE database. The results demonstrate that the proposed algorithm segments blood vessels in the retinal images effectively with an accuracy of 94.33%.

Morphology based particle filtering algorithm for point target tracking

Abstract: The problem of tracking maneuvering point target in optical image sequence using a single sensor is considered in this paper. A morphology based method is proposed to pre-detect target area. This pre-detection begins with expansion of the target area by dilation using a selected rectangle structuring element. Then the target energy is enhanced by frame-addition. Finally, the target area is detected by getting the maximum intensity based on the character of target correlation between frames. Therefore, the conventional uniform distribution of particles over the whole sensor field-of-view is shrunk to the pre-detection target area. The proposed morphology based particle filtering algorithm thus enhances the particles representation. To demonstrate the efficiency of the proposed method, it is compared with the particle filter proposed by Salmond. The simulations indicate that desired tracking quality is ensured by narrowing initial particle distribution derived from morphology based pre-detection.

Time Complexity Measurement on CUDA-based GPU Parallel Architecture of Morphology Operation

Abstract: Operation time of a function or procedure is a thing that always needs to be optimized. Parallelizing the operation is the general method to reduce the operation time of the function. One of the most powerful parallelizing methods is using GPU. In image processing field, one of the most commonly used operations is morphology operation. Three types of morphology operations kernel, naive, global and shared, are presented in this paper. All kernels are made using CUDA and work parallel on GPU. Four morphology operations (erosion, dilation, opening. and closing) using square structuring element are tested on MRI images with different size to measure the speedup of the GPU implementation over CPU implementation. The results show that the speedup of dilation is similar for all kernels. However, on erosion, opening, and closing, shared kernel works faster than other kernels.

Urban road network extraction based on fuzzy ART, radon transform and morphological operations on DSM data

Abstract: In urban areas, the main disadvantage of an aerial photo for road extraction is the shadow cast by buildings and the complexity of the road network. For this case, we used Digital Surface Model (DSM) data, which are based on the elevation of land surfaces. However, one of the problems associated with DSM data is the non-road area with the same road elevations, like parking places, parks, empty ground and so on. In this paper, we propose the Mixed ART clustering on histogram followed by region growing to extract the initial road and perform the road filter by opening operation with a line shape structuring element, where the line orientation is obtained from the Radon Transform. Finally, the road networks are constructed based on B-Spline curve from the skeleton of the extracted road. The experimental result shows that the proposed method improved the quality and the accuracy average within an acceptable time.

Algorithm for decomposition of binary morphological convex structuring elements based on periodic lines

Abstract: This paper presents a new and simple algorithm for decomposing a convex structuring element into dilations of periodic lines with a union of a residue component The algorithm proposed in this paper is based on the boundaries of convex structuring elements and the properties of dilations Besides simplicity of the algorithm, the advantage of the method is that periodic lines possess a faster implementation of the corresponding dilations and erosions The examples of optimal decomposition demonstrate that the algorithm offers a low complexity of morphological operators The results indicate that the present decomposition algorithm is more robust when compared with other algorithms

A 3 dimensional Digital Image Skeltonization using 3x3x3 Structuring Element

Abstract: Image Skeletonization promises to be a powerful complexity-cutting tool for compact shape description, pattern recognition, robot vision, animation, petrography pore space fluid flow analysis, model/analysis of bone/lung/circulation, and image compression for telemedicine. The existing image thinning/skeletonization techniques using boundary erosion, distance coding, and Voronoi diagram are first overviewed to assess/compare their feasibility of extending from 2D to 3D. Previously, skeletons have been a common tool for identifying shape components in a solid object. However, obtaining skeletons of a grayscale volume poses new challenges due to the lack of a clear boundary between object and background. In this paper we propose a fast, efficient and robust algorithm to generate the skeleton of large, complex 3D images such as CT, MRI data which make use of 3X3X3 structuring elements for processing. This algorithm has been developed in the frame work of cellular logic array processing. Cellular logic array processing is a logico mathematical paradigm developed using the fundamental notions of normal algorithms and cellular automata. The algorithm provides a straightforward computation which is robust and not sensitive to noise or object boundary complexity. Because 3D skeleton may not be unique, several application-dependent skeletonization options will be explored for meeting specific quality/speed requirements.

Morphological Dilation as Fractal Scaling in Roughness Measurement

Abstract: In this work we propose, that the morphological dilation acts as fractal filters rebuilding white noise roughness surfaces into fractal 1/fm noise surfaces. The fractality indicates that the dilation does not have characteristic length scale, and the structuring element follows power-law distribution. Yashchuk's binary pseudo-random grating standard has been dilated with spherical and free form tips between 50 nm and 2000 nm and two scaling regions are referred to the tip diameter versus scaling exponent diagram. The first one in the smaller tip diameter region has a fast slope and the second one in the intermediate and larger tip diameter has a gradual slope. The results show that the dilated surfaces arise from the activity of at least two dynamical systems.

Unsupervised structuring element estimation of morphological opening and closing with GA

Abstract: In this paper, we propose an unsupervised design method for optimal structuring element (SE) of morphological filters. We formulate the design of SE as an optimization problem, and estimate an optimal shape and brightness of SE directly from degraded images. We estimated an optimal shape and brightness by using a Genetic Algorithm (GA). Estimation of SE that can remove noise while preserving original signals is achieved by objective function that use Rank-Ordered Logarithmic Differences (ROLD) statistic for the impulse noise detection.

Dim Small Target Detection Based on Histogram Adjustment and Hit-miss Transform

Abstract: It is hard to detect the dim small target with few pixels and low intensity in the image of complex background.According to the intensity features of dim small target,a scheme based on histogram adjustment and hit-miss transform is presented to detect dim small target.In this method,histogram adjustment is adopted to enhance the signal-cluster-ratio,and hit-miss transform is used to get the suspicious targets.The experiment shows that the method is efficient in detecting dim small target with complex background.