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

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.

Author(s):  
Frank Y. Shih ◽  
Yucong Shen ◽  
Xin Zhong

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.


2014 ◽  
Vol 22 (1) ◽  
pp. 281-288
Author(s):  
Eugen Zaharescu

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.


IEE Review ◽  
1992 ◽  
Vol 38 (3) ◽  
pp. 112
Author(s):  
Stuart Bennett

Author(s):  
Pallab Banerjee ◽  
◽  
Riya Shree ◽  
Richa Kumari Verma ◽  
◽  
...  

2013 ◽  
Vol 32 (2) ◽  
pp. 573-577
Author(s):  
Zhi-bang YANG ◽  
Cheng XU ◽  
Xu ZHOU ◽  
Xue-qing ZHU

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