STRUCTURALLY ADAPTIVE MATHEMATICAL MORPHOLOGY BASED ON NONLINEAR SCALE-SPACE DECOMPOSITIONS

Standard formulation of morphological operators is translation invariant in the space and in the intensity: the same processing is considered for each point of the image. A current challenging topic in mathematical morphology is the construction of adaptive operators. In previous works, the adaptive operators are based either on spatially variable neighbourhoods according to the local regularity, or on size variable neighbourhoods according to the local intensity. This paper introduces a new framework: the structurally adaptive mathematical morphology. More precisely, the rationale behind the present approach is to work on a nonlinear multi-scale image decomposition, and then to adapt intrinsically the size of the operator to the local scale of the structures. The properties of the derived operators are investigated and their practical performances are compared with respect to standard morphological operators using natural image examples.

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Structurally adaptive mathematical morphology on nonlinear scale-space representations

2010 IEEE International Conference on Image Processing ◽

10.1109/icip.2010.5651969 ◽

2010 ◽

Cited By ~ 2

Author(s):

Jesus Angulo ◽

Santiago Velasco-Forero

Keyword(s):

Mathematical Morphology ◽

Scale Space ◽

Nonlinear Scale Space ◽

Nonlinear Scale

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Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

Journal of Real-Time Image Processing ◽

10.1007/s11554-021-01089-9 ◽

2021 ◽

Author(s):

Parastoo Soleimani ◽

David W. Capson ◽

Kin Fun Li

Keyword(s):

Real Time ◽

Memory Management ◽

Scale Space ◽

Image Resolution ◽

Hardware Architecture ◽

Frame Rate ◽

Time Sharing ◽

Scale Invariant ◽

Nonlinear Scale Space ◽

Nonlinear Scale

AbstractThe first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a $$1280 \times 768$$ 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

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