Adaptive morphological operators, fast algorithms and their applications

In this paper, novel binary adaptive neighborhood morphological operators are proposed to solve the problem that the fixed structure elements cannot adapt to the whole image when processing images in classical mathematical morphology. The key of adaptive morphology is to select suitable structure elements according to the different attributes of an image. On the basis of image local features and pixel position information, the structure elements selected in this paper can be adaptively adjusted. Besides, the new structure elements have symmetry, namely they are symmetrical about the center point. New binary adaptive neighborhood morphological operators are also defined according to the new structure elements. Comparing with the experimental results of the classical morphological operations, the new operators can retain the details of the image better and be more similar to the original image, which shows the new operators is more effective and practical.

Edge detection is a commonly performed operation in image processing. In this paper, we propose a new method for edge detection using the logarithm function. The pixel values within a mask are normalized so that they can be regarded as probabilities. A positive constant is added to the probabilities to avoid zero values. Then, the sum of the logarithms of the adjusted probabilities is calculated. A threshold is chosen for generating the black and white edge image. Morphological operation is applied as post processing for refining the edges. Simulation results show that the proposed method has consistent performance on various sample images, which is competitive with the Sobel and Canny operators for edge detection. It is shown that the proposed method has almost identical performance as the entropy method. This phenomenon is well explained by using the theory of quasientropy.

The increased use of power- and space-constrained embedded processors in a wide variety of autonomous imaging and surveillance applications implies increased speed of computational resources that follow image acquisition in the processing stream. In such early vision applications, one typically processes an entire image data stream prior to spatial downselection operations such as focus-of-attention involving area-of-interest (AOI) selection. Downselection is especially useful in the emerging technologies of spatiotemporal adaptive processing (STA) and biomimetic automated target recognition (ATR). Here, progressive data reduction employs operations or sub-algorithms that process fewer data but in a more involved manner at each step of an algorithm. Implementationally, the STAP approach is amenable to embedded hardware using processors with deep pipelines and fine-grained parallelism. In part 1 of this series of two papers, we showed how compression of an image or sequence of images could facilitated more efficient image computation or ATR by processing fewer (compressed) data. This technique, called compressive processing or compressive computation, typically utilizes fewer operations than the corresponding ATR or image processing operation over noncompressed data, and can produce space, time, error, and power (STEP) savings on the order of the compression ratio (CR). Part 1 featured algorithms for edge detection in images compressed by vector quantization (VQ), visual pattern image coding (VPIC), and EBLAST, which is a recently-reported block-oriented high- compression transform. In this paper, we continue presentation of theory for compressive computation of early processing operations such as morphological erosion and dilation, as well as higher- level operations such as connected component labeling. We also discuss the algorithm and hardware modeling technique that supports analysis and verification of compressive processing efficiency. This methodology emphasizes 1) translation of each image processing algorithm or operation to a prespecified compressive format, 2) determination of the operation mix M for each algorithms produced in Step 1), and 3) simulation of M on various architectural models, to estimate performance. As in part 1, algorithms are expressed in image algebra, a rigorous, concise notation that unified linear and nonlinear mathematics in the image domain.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A description of the diamond grid for topological and combinatorial analysis

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed. To increase the contrast of the liver CT images, the intensity values are adjusted and high frequencies are removed using histogram equalization and median filter approach. It is followed by transforming the CT image to NS domain, which is described using three subsets (percentage of truth T, the percentage of indeterminacy I, and percentage of falsity F). The obtained NS image is enhanced by adaptive threshold and morphological operators to focus on liver parenchyma. The enhanced NS image passed to a watershed algorithm for post-segmentation process and liver parenchyma is extracted using the connected component algorithm. Finally, the liver tumors are segmented from the segmented liver using fast fuzzy c-mean (FFCM). A quantitative analysis is carried out to evaluate segmentation results using six different indices. The results show that the overall accuracy offered by the employed neutrosophic sets is accurate, less time consuming, less sensitive to noise and performs better on non-uniform CT images.

This volume contains the articles accepted for presentation at the 14th International Symposium on Mathematical Morphology (ISMM 2019), held in Saarbrucken,Germany, during July 8–10, 2019. The 13 previous editions of this conference were very successful, and the ISMM has established itself as the main scientific event in the field. We received 54 high-quality papers, each of which was sent to at least three Program Committee members for review. Based on 191 detailed reviews, we accepted 41 papers (including 40 regular papers, plus one related to an invited plenary talk). The authors of these 41 articles are from 11 different countries: Austria, Brazil, France, Germany, Greece, India, Italy, The Netherlands, Sweden, UK, and USA. The ISMM 2019 papers highlight the current trends and advances in mathematical morphology, be they purely theoretical contributions, algorithmic developments, or novel applications, where real-life imaging and computer vision problems are tackled with morphological tools. We wish to thank the members of the Program Committee for their efforts in reviewing all submissions on time and giving extensive feedback. We would like to take this opportunity to thank all the other people involved in this conference: firstly, the Steering Committee for giving us the chance to organize ISMM 2019; secondly, the three invited speakers, David Coeurjolly, Fred A. Hamprecht, and Boguslaw Obara, for accepting to share their recognized expertise; and finally, the most important component of any scientific conference, the authors for producing the high-quality and original contributions. We acknowledge the EasyChair conference management system that was invaluable in handling the paper submission, the review process, and putting this volume together. We also acknowledge Springer for making possible the publication of these proceedings in the LNCS series.

Modern technological trends like Internet of Things (IoT’s) essentially require prompt development of software systems. To manage this, Model Driven Architecture (MDA) is frequently applied for development of different systems like industry automation, medical, surveillance, tracking and security etc. Image processing is an integral part of such systems. Particularly, image enhancement and classification operations are mandatory in order to effectively recognize objects for different purposes. Currently, such critical image processing operations are not managed through MDA and low level implementations are performed distinctly during system development. This severely delays the system development due to integration issues. Furthermore, system testing becomes problematic as few components of systems are developed through MDA and image processing operations are implemented in isolation. This article introduces a novel framework i.e. MIEORF – Model-driven Image Enhancement and Object Recognition Framework. Particularly, a meta-model is proposed, that allows modeling and visualization of complex image processing and object recognition tasks. Subsequently, an open source customized tree editor (developed using Eclipse Modeling Framework (EMF)) and graphical modeling tool/workbench (developed using Sirius) have been developed (both distributable via eclipse plugin). Consequently, the proposed framework allows modeling and graphical visualization of major image processing operations. Moreover, it provides strong grounds for model transformation operations e.g. Model to Text Transformations (M2T) using Acceleo for generating executable Matlab code. Furthermore, it systematically combines MDA and image processing concepts which are detailed enough to be easily integrated into wide variety of systems such as industrial automation, medical, surveillance, security and biometrics etc. The feasibility of proposed framework is demonstrated via real world medical imagery case study. The results prove that the proposed framework provides a complete solution for modeling and visualization of image processing tasks and highly effective for MDA based systems development.

A morphological image enhancement method based on the GAN (General Adaptive Neighborhood) structuring elements is proposed for some problems such as low overall contrast, noise and inter-target edge of some remote sensing images. Firstly, a pixel to be processed is selected as a seed point, and an adaptive neighborhood is defined according to the image pixel characteristic and the neighborhood constraint relation to obtain the self-adaptive structuring elements set. A corresponding self-adaptive morphological operation based on the self-adaptive structuring elements set is constructed with reference to the classical morphological operation definition, and the self-adaptive alternative filter is constructed by using the adaptive morphological operation combination so as to realize the enhancement of the remote sensing image. The experimental results show that the method can preserve the accurate positioning of the objects edge while enhancing the image, avoid the loss of information, has better noise removing performance. Compared with the classical morphological filter operator, the single-scale Retinex algorithm and bi-histogram equalization (BHE) algorithm, the contrast ratio and the signal-to-noise ratio of the enhanced image are improved.

A novel approach of combined edge detection and segmentation for tool wear measurement in machining

Magnetic Resonance Imaging (MRI) techniques are a fundamental and imperative part of the medical image processing field. The images acquired from the MRI machines are affected by the noise. This noise degrades the quality of the images. Acquisition of MRI with noise may give erroneous results. Hence, to enhance the image quality, it is necessary to reduce or remove this noise. To enhance the image quality of MRI, a plethora of filtering algorithms are available along with the morphological operations. In this paper, we have implemented numerous filters like Adaptive Median filter, Median filter, Mean filter, bilateral filter, NLM filter, Gaussian filter, Weiner filter, and morphological operations to eliminate the noise in the MRI of the spinal cord. The scenarios considered are 1. Application of filters, 2. Application of filters followed by morphological operations, and 3. Morphological operations followed by the application of filters. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are found for all three approaches and are used to analyze the performance of these techniques. NLM filters are found to give the best performance when compared to other filters. Morphological operations affect the performance of the filters. Application of morphological operations before filtering degrades the filter performance while applying them after improves the performance. The dataset comprises of 250 spinal cord MRIs with noise. The author inferred that the performance of the filters is improved by applying the filtering techniques after the morphological operation.

Directional information has a strong meaning in line-drawing images. Therefore, the proposed approach for line-drawing images processing is based on a directional decomposition of the input image into a set of images each containing line segments in a respective range of directions. The directional information is then processed by using newly defined directional morphological operators, called tube-directional, that have accurate selectivity and controllable strictness. When using morphological operators, adaptation to a specific task is achieved only globally by setting the structure of the morphological kernel. The concept of one global adjustment for many local operations is some how conflicting. In the proposed approach the parameters of the tube-directional morphological operators are determined locally for each element of the processed image.

On the basis of Canny operator on image processing, The edge extraction of the fuzzy droplet images is carried out by combination of removing a small target and morphological open operation .First, Use edge detection operator to detect the image edge preliminary, and then call remove a small target which belongs to MATLAB internal function to filtering the results of the edge detection, and finally with morphological operations for final processing.The results showed that: the combination of Canny operator, open operation and remove a small target works much better than the classical Canny operator.

In addition to causing damage to vehicles, road defects are one of the main causes of vehicle accidents which lead to loss of human lives. Many methods of detecting defects have been introduced over the years to reduce the consequences of these defects. One of these methods is image processing. Use of image and video processing has many applications in medicine, science, agriculture, and defect detection in structures. It has been used for defect detection on roads because timely detection and analysis of defect is very important for road serviceability and safety of the people. Detection of a defect by image processing broadly follows some of the basic steps which include feature extraction, edge detection, morphological operators, and training of data. Different approaches are used for various kinds of defect detection and analysis which have replaced the manual inspection method of roads saving time and resources. This chapter discusses the basic steps involved in defect detection using image processing along with existing systems that use machine learning and artificial intelligence for the detection of defects from a distance. To write this chapter, papers on the topic of image and computer vision-based defect detection systems have been consulted.

The two fundamental operations in morphological image processing are dilation and erosion. These processes are defined via structuring elements. It is of practical interest to consider a variety of structuring element shapes. The realisation of dilation/erosion for convex structuring elements by use of partial differential equations (PDEs) allows for digital scalability and subpixel accuracy. However, numerical schemes suffer from blur by dissipative artifacts. In our paper we present a family of so-called flux-corrected transport (FCT) schemes that addresses this problem for arbitrary convex structuring elements. The main characteristics of the FCT-schemes are: (i) They keep edges very sharp during the morphological evolution process, and (ii) they feature a high degree of rotational invariance. We validate the FCT-scheme theoretically by proving consistency and stability. Numerical experiments with diamonds and ellipses as structuring elements show that FCT-schemes are superior to standard schemes in the field of PDE-based morphology.

An FPGA Based SIMD Architecture Implemented with 2D Systolic Architecture for Image Processing