scholarly journals Boundary Morphology for Hierarchical Simplification of Archaeological Fragments

2020 ◽  
Vol 4 (1) ◽  
pp. 46-63
Author(s):  
Hanan ElNaghy ◽  
Leo Dorst
Keyword(s):  
Mathematical Morphology ◽  
Masking Effect ◽  
Morphological Operations ◽  
Fracture Surfaces ◽  
Morphological Approach ◽  
Archaeological Artifacts ◽  
Scale Spaces ◽  
Extrusion Method

AbstractWhen fitting archaeological artifacts, one would like to have a representation that simplifies fragments while preserving their complementarity. In this paper, we propose to employ the scale-spaces of mathematical morphology to hierarchically simplify potentially fitting fracture surfaces. We study the masking effect when morphological operations are applied to selected subsets of objects. Since fitting locally depends on the complementarity of fractures only, we introduce ‘Boundary Morphology’ on surfaces rather than volumes. Moreover, demonstrating the Lipschitz nature of the terracotta fractures informs our novel extrusion method to compute both closing and opening operations simultaneously. We also show that in this proposed representation the effects of abrasion and uncertainty are naturally bounded, justifying the morphological approach. This work is an extension of our contribution earlier published in the proceedings of ISMM2019 [10].

scholarly journals Deterministic mathematical morphology for CAD/CAM

2014 ◽  
Vol 31 (7) ◽  
pp. 1221-1241 ◽  
Author(s):  
Rubén Sarabia-Pérez ◽  
Antonio Jimeno-Morenilla ◽  
Rafael Molina-Carmona
Keyword(s):  
Mathematical Morphology ◽  
Geometric Model ◽  
Three Dimensional ◽  
Morphological Operations ◽  
Basic Operation ◽  
Geometric Data Structures ◽  
Content Type ◽  
Morphological Operators ◽  
Cad Cam ◽  
Definition Of

Purpose – The purpose of this paper is to present a new geometric model based on the mathematical morphology paradigm, specialized to provide determinism to the classic morphological operations. The determinism is needed to model dynamic processes that require an order of application, as is the case for designing and manufacturing objects in CAD/CAM environments. Design/methodology/approach – The basic trajectory-based operation is the basis of the proposed morphological specialization. This operation allows the definition of morphological operators that obtain sequentially ordered sets of points from the boundary of the target objects, inexistent determinism in the classical morphological paradigm. From this basic operation, the complete set of morphological operators is redefined, incorporating the concept of boundary and determinism: trajectory-based erosion and dilation, and other morphological filtering operations. Findings – This new morphological framework allows the definition of complex three-dimensional objects, providing arithmetical support to generating machining trajectories, one of the most complex problems currently occurring in CAD/CAM. Originality/value – The model proposes the integration of the processes of design and manufacture, so that it avoids the problems of accuracy and integrity that present other classic geometric models that divide these processes in two phases. Furthermore, the morphological operative is based on points sets, so the geometric data structures and the operations are intrinsically simple and efficient. Another important value that no excessive computational resources are needed, because only the points in the boundary are processed.

scholarly journals Robust Object Detection in Colour Images Using a Multivariate Percentage Occupancy Hit-or-Miss Transform

2021 ◽  
Vol 5 (1) ◽  
pp. 128-152
Author(s):  
Fraser Macfarlane ◽  
Paul Murray ◽  
Stephen Marshall ◽  
Benjamin Perret ◽  
Adrian Evans ◽  
...  
Keyword(s):  
Object Detection ◽  
Mathematical Morphology ◽  
Morphological Operations ◽  
Design Tools ◽  
Partial Occlusion ◽  
Colour Space ◽  
Multivariate Images ◽  
Optimal Values ◽  
Ordering Schemes ◽  
Robust To Noise

Abstract The extension of Mathematical Morphology to colour and multivariate images is challenging due to the need to define a total ordering in the colour space. No one general way of ordering multivariate data exists and, therefore, there is no single, definitive way of performing morphological operations on colour images. In this paper, we propose an extension to mathematical morphology, based on reduced ordering, specifically the morphological Hit-or-Miss Transform which is used for object detection. The reduced ordering employed transforms multivariate observations to scalar comparisons allowing for an order to be derived and for both flat and non-flat structuring elements to be used. We also compare other definitions of the Hit-or-Miss Transform and test alternative colour ordering schemes presented in the literature. Our proposed method is shown to be intuitive and outperforms other approaches to multivariate Hit-or-Miss Transforms. Furthermore, methods of setting the parameters of the proposed Hit-or-Miss Transform are introduced in order to make the transform robust to noise and partial occlusion of objects and, finally, a set of design tools are presented in order to obtain optimal values for setting these parameters accordingly.

Parallel Segmentation of Multi-Channel Images Using Multi-Dimentional Mathematical Morphology

2006 ◽  
pp. 270-291
Author(s):  
Antonio Plaza ◽  
Javier Plaza ◽  
David Valencia ◽  
Pablo Martiez
Keyword(s):  
Mathematical Morphology ◽  
Parallel Implementation ◽  
Region Growing ◽  
Large Data ◽  
Remotely Sensed ◽  
Morphological Operations ◽  
Remotely Sensed Data ◽  
Imaging Spectrometer ◽  
Seeded Region Growing ◽  
Multi Channel Imaging

Multi-channel images are characteristic of certain applications, such as medical imaging or remotely sensed data analysis. Mathematical morphology-based segmentation of multi-channel imagery has not been fully accomplished yet, mainly due to the lack of vector-based strategies to extend classic morphological operations to multidimensional imagery. For instance, the most important morphological approach for image segmentation is the watershed transformation, a hybrid of seeded region growing and edge detection. In this chapter, we describe a vector-preserving framework to extend morphological operations to multi-channel images, and further propose a fully automatic multi-channel watershed segmentation algorithm that naturally combines spatial and spectral/temporal information. Due to the large data volumes often associated with multi-channel imaging, this chapter also develops a parallel implementation strategy to speed up performance. The proposed parallel algorithm is evaluated using magnetic resonance images and remotely sensed hyperspectral scenes collected by the NASA Jet Propulsion Laboratory Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).

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.

scholarly journals Dermoscopy Images Enhancement via Multi-Scale Morphological Operations

2021 ◽  
Vol 11 (19) ◽  
pp. 9302
Author(s):  
Julio César Mello-Román ◽  
José Luis Vázquez Noguera ◽  
Horacio Legal-Ayala ◽  
Miguel García-Torres ◽  
Jacques Facon ◽  
...  
Keyword(s):  
Color Images ◽  
Morphological Operations ◽  
Light Conditions ◽  
Low Contrast ◽  
Multi Scale ◽  
Melanocytic Lesions ◽  
Morphological Approach ◽  
Detail Enhancement ◽  
Surrounding Skin

Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.

A Novel Text Extraction Method from Pure Text Images Using Morphological Operations

2014 ◽  
Vol 989-994 ◽  
pp. 3768-3772
Author(s):  
Xuan Qi Chen ◽  
Biao He ◽  
Guo Cheng Wang ◽  
Yao Xin Li
Keyword(s):  
Mathematical Morphology ◽  
Extraction Method ◽  
New Method ◽  
Morphological Operations ◽  
Text Extraction ◽  
Text Images ◽  
Robust To Noise

This paper presents a new method to achieve effective text extraction using mathematical morphology. Firstly, the document is segmented and divided into several parts based on the layout. And then, every part is dilated to big connected regions, whose biggest skeleton will be extracted and serve as a structure element (SE). Finally, a proposed region-concatenated operation with the SE will be employed, whose result can be the input of subsequent OCR system. Experimentally, the proposed method is robust to noise, the text orientation, font style and size, language and layout.

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 Application of morphological processing methods in computer stereo vision

2021 ◽  
Vol 6 (131) ◽  
pp. 18-27
Author(s):  
Oleh Prokopchuk ◽  
Serhii Vovk
Keyword(s):  
Mathematical Morphology ◽  
Stereo Vision ◽  
Real World ◽  
Stereo Matching ◽  
Morphological Processing ◽  
Morphological Operations ◽  
Disparity Map ◽  
Real Objects ◽  
Disparity Maps ◽  
The Impact

Computer vision algorithms are important for many areas of human activity. In particular, the number of applications related to the need to process images of real-world objects with computerized tools and the subsequent use of descriptive information in a variety of interactive and automated decision-making systems is increased. An important tool for analyzing real-world scenes are approaches to the application of stereo vision algorithms. The important step of many stereo matching algorithms is a disparity map. Depending on the content of the observed scene, part of the values on the disparity map can be immediately attributed to background values on a certain basis, or form a "natural" background, which is characterized by loss of informative data due to unacceptable error of subsequent resultant distance values. The calculated disparity map of any algorithm may contain some shortcomings in the form of discontinuities of continuous information areas caused by the complexity of shooting conditions, the impact of noise of various natures, hardware imperfections, and so on. An approach to mitigating the undesirable influence of negative factors on the resulting disparity is the use of mathematical morphology operations to process disparity maps at the post-processing stage. This paper presents information technology for increasing the content of disparity maps based on the mathematical morphology methods. The technology is based on a combination of morphological operations of erosion and dilation, which eliminates the typical problems of discontinuities of monotone regions and erroneous values on disparity maps. The proposed approach allows reducing the impact of common problems that arise during the operation of stereo matching algorithms, as well as increase the overall informativeness of disparity maps for images of real objects in the absence of partial or complete initial data on the characteristics of the observed scene. The results of testing morphological operations with disparity maps for real objects allow us to conclude about the possibility of partial restoration of areas of disparity maps with gaps in continuous information areas, as well as to reduce the impact of random anomalous values on the overall content of the disparity maps.

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.

Parallel Segmentation of Multi-Channel Images Using Multi-Dimensional Mathematical Morphology

2011 ◽  
pp. 321-340 ◽  
Author(s):  
Antonio Plaza ◽  
Javier Plaza ◽  
David Valencia ◽  
Pablo Martinez
Keyword(s):  
Mathematical Morphology ◽  
Parallel Implementation ◽  
Region Growing ◽  
Large Data ◽  
Remotely Sensed ◽  
Morphological Operations ◽  
Remotely Sensed Data ◽  
Imaging Spectrometer ◽  
Seeded Region Growing ◽  
Multi Channel Imaging

Multi-channel images are characteristic of certain applications, such as medical imaging or remotely sensed data analysis. Mathematical morphology-based segmentation of multi-channel imagery has not been fully accomplished yet, mainly due to the lack of vector-based strategies to extend classic morphological operations to multidimensional imagery. For instance, the most important morphological approach for image segmentation is the watershed transformation, a hybrid of seeded region growing and edge detection. In this chapter, we describe a vector-preserving framework to extend morphological operations to multi-channel images, and further propose a fully automatic multi-channel watershed segmentation algorithm that naturally combines spatial and spectral/temporal information. Due to the large data volumes often associated with multi-channel imaging, this chapter also develops a parallel implementation strategy to speed up performance. The proposed parallel algorithm is evaluated using magnetic resonance images and remotely sensed hyperspectral scenes collected by the NASA Jet Propulsion Laboratory Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).

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