Structuring element in detection of IR images based on soft mathematical morphology

2003 ◽  
Author(s):  
Yan-zhi Dong ◽  
Chang-jin Wang ◽  
Xiao-dong Zhou
Keyword(s):  
Mathematical Morphology ◽  
Structuring Element

Silhouette Processing Via Mathematical Morphology with Pose-Aware Structuring Elements Based on 3D Model

2012 ◽  
Vol 17 (4) ◽  
pp. 71-78
Author(s):  
Mirosław Jabłoński
Keyword(s):  
Mathematical Morphology ◽  
3D Model ◽  
Structuring Element ◽  
Software Packages ◽  
Camera Image ◽  
Platform Comparison ◽  
Element Computation ◽  
Camera Parameters ◽  
The Individual

Abstract In the paper, the method of poseaware silhouette processing is presented. Morphological closing is proposed to enhance segmented silhouette object. The contribution of the work is adaptation of structuring element used for mathematical morphology erosions and dilations. It is proposed to use camera parameters, 3D model of the scene, model of the silhouette and its position to compute structuring element adequate to the individual projected to the camera image. Structuring element computation and basic morphology operators were implemented in OpenCL environment and tested on parallel GPU platform. Comparison with utility software packages is provided and results are briefly discussed.

scholarly journals Trajectory-Based Morphological Operators: A Model for Efficient Image Processing

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Antonio Jimeno-Morenilla ◽  
Francisco A. Pujol ◽  
Rafael Molina-Carmona ◽  
José L. Sánchez-Romero ◽  
Mar Pujol
Keyword(s):  
Mathematical Morphology ◽  
Morphological Operations ◽  
Real Time Systems ◽  
Morphological Operation ◽  
Industrial Systems ◽  
Structuring Element ◽  
Element Size ◽  
High Resolution Images ◽  
Set Of Points ◽  
Time Systems

Mathematical morphology has been an area of intensive research over the last few years. Although many remarkable advances have been achieved throughout these years, there is still a great interest in accelerating morphological operations in order for them to be implemented in real-time systems. In this work, we present a new model for computing mathematical morphology operations, the so-called morphological trajectory model (MTM), in which a morphological filter will be divided into a sequence of basic operations. Then, a trajectory-based morphological operation (such as dilation, and erosion) is defined as the set of points resulting from the ordered application of the instant basic operations. The MTM approach allows working with different structuring elements, such as disks, and from the experiments, it can be extracted that our method is independent of the structuring element size and can be easily applied to industrial systems and high-resolution images.

Application of Fuzzy Mathematical Morphology with Adaptive Structuring Elements to Seal Defect Testing

2002 ◽  
Vol 6 (1) ◽  
pp. 62-69
Author(s):  
Takuo Kikuchi ◽  
◽  
Shuta Murakami ◽  
Keyword(s):  
Mathematical Morphology ◽  
Input Image ◽  
Performance Evaluations ◽  
Automatic Inspection ◽  
Morphological Operators ◽  
Fuzzy Morphology ◽  
Structuring Element ◽  
Extraction Performance ◽  
Selection Of ◽  
Fuzzy Mathematical Morphology

Fuzzy mathematical morphology has been proposed as a new method of image processing, especially in the analysis of features from an ambiguous image. Fuzzy morphological operators work with 2 images: an original image to be processed and a structuring element. Generally, we must decide on the shape and value of the structuring element before applying fuzzy morphology. A problem arises when the applied image changes largely by the selection of the structuring element, so it is difficult to apply fuzzy morphological operators to inspection for defect testing. In this paper, we propose a new structuring element for fuzzy morphology, called an adaptive structuring element. The adaptive structuring element determines shapes and values of structuring elements dynamically from an input image by searching for the local features in the image. Consequently, the adaptive structuring element is sensitive to ambiguous images, useful for automatic inspection using fuzzy morphology, and effective for extraction. Performance evaluations via simulations show that the adaptive structuring element efficiently extracts features from an ambiguous image. The adaptive structuring element also shows a higher performance in package defect testing than other filters. We attained experimental results (more than 95.5%) by applying the adaptive structuring element to seal defect testing.

Development of Deep Learning Framework for Mathematical Morphology

2019 ◽  
Vol 33 (06) ◽  
pp. 1954024 ◽  
Author(s):  
Frank Y. Shih ◽  
Yucong Shen ◽  
Xin Zhong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mathematical Morphology ◽  
Experimental Results ◽  
Morphological Operations ◽  
Learning Framework ◽  
Structuring Element ◽  
The Neural Network ◽  
Object Features

Mathematical morphology has been applied as a collection of nonlinear operations related to object features in images. In this paper, we present morphological layers in deep learning framework, namely MorphNet, to perform atomic morphological operations, such as dilation and erosion. For propagation of losses through the proposed deep learning framework, we approximate the dilation and erosion operations by differential and smooth multivariable functions of the softmax function, and therefore enable the neural network to be optimized. The proposed operations are analyzed by the derivative of approximation functions in the deep learning framework. Experimental results show that the output structuring element of a morphological neuron and the target structuring element are matched to confirm the efficiency and correctness of the proposed framework.

scholarly journals Efficient Morphological Analysis Using Arbitrary Structuring Elements for Security Purposes

2013 ◽  
pp. 80-85
Author(s):  
Akansha Saxena ◽  
Santosh Kumar
Keyword(s):  
Mathematical Morphology ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Morphological Analysis ◽  
Image Data ◽  
Speckle Noise ◽  
Boundary Extraction ◽  
Structuring Element ◽  
Optical Character ◽  
Salt And Pepper

The term Mathematical Morphology (MM) mostly deals with the mathematical theory of describing shapes using sets. In morphology, images are represented as sets. This task is investigated by the interaction between an image and a certain chosen arbitrary structuring element using the basic operations of erosion and dilation. The various applications of morphologyinclude skeletonization, prunning, optical character recognition,image analysis,artifacts removal,boundary extraction, etc. It is further extended by the fact that mathematical morphology provides better quality image data for analysis and diagnostic purposes. The process is very efficient due to the use of MATLAB algorithmswhich are helpful for securing meaningful information against different threats like-speckle noise, salt and pepper noise,etc.

scholarly journals Color Image Indexing Using Mathematical Morphology

2014 ◽  
Vol 22 (1) ◽  
pp. 281-288
Author(s):  
Eugen Zaharescu
Keyword(s):  
Feature Extraction ◽  
Mathematical Morphology ◽  
Color Image ◽  
Image Indexing ◽  
Morphological Operations ◽  
Pattern Spectrum ◽  
Structuring Element ◽  
Extraction Algorithm ◽  
Color Gradient ◽  
Illustrative Application

AbstractA mathematical morphology based approach for color image indexing is explored in this paper. Morphological signatures are powerful descriptions of the image content in the framework of mathematical morphology. A morphological signature (either a pattern spectrum or a differential morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. For image indexing it is considered a morphological feature extraction algorithm which includes more complex morphological operators: i.e. color gradient, homotopic skeleton, Hit-or-Miss transform. In the end, illustrative application examples of the presented approach on real acquired images are also provided.

scholarly journals Consolidated morphology

2006 ◽  
Author(s):  
Gaetan Lehmann ◽  
Richard Beare
Keyword(s):  
Image Analysis ◽  
Mathematical Morphology ◽  
Computation Time ◽  
3D Images ◽  
Structuring Element

Grayscale dilation and erosion are basic transformations of mathematical morphology. Used together or with other transformations, they are very useful tools for image analysis. However, they can be very time consuming, especially with 3D images, and with large structuring elements. Several algorithms have been created to decrease the computation time, some of them with some limitations of shape of structuring element. We have implemented several algorithms, studied their performance in different conditions, and shown that all of them are more efficients than the others in certain conditions. We finally introduce a new structuring element class and a some meta filter designed to select the best algorithm depending on the image and the structuring element, and to smoothely integrate the different algorithms available in the toolkit.

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