A variational approach to scene reconstruction and image segmentation from motion-blur cues

Variational approach to semi-automated 2D image segmentation

Applications of Digital Image Processing XLII ◽

10.1117/12.2529374 ◽

2019 ◽

Author(s):

Jaromir Kukal ◽

Zuzana Krbcová ◽

Iva Nachtigalova ◽

Jan Švihlík ◽

Karel Fliegel

Keyword(s):

Image Segmentation ◽

Variational Approach

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SPARSE TEMPLATE-BASED VARIATIONAL IMAGE SEGMENTATION

Advances in Adaptive Data Analysis ◽

10.1142/s1793536911000805 ◽

2011 ◽

Vol 03 (01n02) ◽

pp. 149-166 ◽

Cited By ~ 1

Author(s):

DIRK BREITENREICHER ◽

JAN LELLMANN ◽

CHRISTOPH SCHNÖRR

Keyword(s):

Image Segmentation ◽

Objective Function ◽

Prior Knowledge ◽

Variational Approach ◽

Large Scale ◽

Convex Relaxation ◽

Variational Inference ◽

Numerical Examples ◽

Variational Image ◽

Data Term

We introduce a variational approach to image segmentation based on sparse coverings of image domains by shape templates. The objective function combines a data term that achieves robustness by tolerating overlapping templates with a regularizer enforcing sparsity. A suitable convex relaxation leads to the variational approach that is amenable to large-scale convex programming. Our approach takes implicitly into account prior knowledge about the shape of objects and their parts, without resorting to combinatorially difficult problems of variational inference. We illustrate our approach by numerical examples and indicate how prior knowledge acquisition may be achieved by learning from examples.

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A Variational Approach to Simultaneous Image Segmentation and Bias Correction

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2014.2352343 ◽

2015 ◽

Vol 45 (8) ◽

pp. 1426-1437 ◽

Cited By ~ 79

Author(s):

Kaihua Zhang ◽

Qingshan Liu ◽

Huihui Song ◽

Xuelong Li

Keyword(s):

Image Segmentation ◽

Bias Correction ◽

Variational Approach

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Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm

Sensors ◽

10.3390/s21093224 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3224

Author(s):

Hang-Chan Jo ◽

Hyeonwoo Jeong ◽

Junhyuk Lee ◽

Kyung-Sun Na ◽

Dae-Yu Kim

Keyword(s):

Blood Flow ◽

Image Segmentation ◽

Deep Learning ◽

Flow Velocity ◽

Blood Flow Velocity ◽

Template Matching ◽

Motion Correction ◽

Motion Blur ◽

Motion Artifacts ◽

Human Conjunctiva

The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps—registration and template matching—are used to correct for large displacements and fine movements. The image displacement values decrease to 4–7 μm during registration (first step) and less than 1 μm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.

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No role for motion blur in either motion detection or motion-based image segmentation

Journal of the Optical Society of America A ◽

10.1364/josaa.15.000297 ◽

1998 ◽

Vol 15 (2) ◽

pp. 297 ◽

Cited By ~ 3

Author(s):

Felix A. Wichmann ◽

G. Bruce Henning

Keyword(s):

Image Segmentation ◽

Motion Detection ◽

Motion Blur

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Construction of Target Controllable Image Segmentation Model Based on Homotopy Perturbation Technology

Abstract and Applied Analysis ◽

10.1155/2013/131207 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Shu-Li Mei

Keyword(s):

Image Segmentation ◽

Numerical Method ◽

Variational Approach ◽

Homotopy Perturbation Method ◽

Nonlinear Pdes ◽

Variational Model ◽

The Novel ◽

Lower Efficiency ◽

Homotopy Perturbation ◽

Novel Model

Based on the basic idea of the homotopy perturbation method which was proposed by Jihuan He, a target controllable image segmentation model and the corresponding multiscale wavelet numerical method are constructed. Using the novel model, we can get the only right object from the multiobject images, which is helpful to avoid the oversegmentation and insufficient segmentation. The solution of the variational model is the nonlinear PDEs deduced by the variational approach. So, the bottleneck of the variational model on image segmentation is the lower efficiency of the algorithm. Combining the multiscale wavelet interpolation operator and HPM, a semianalytical numerical method can be obtained, which can improve the computational efficiency and accuracy greatly. The numerical results on some images segmentation show that the novel model and the numerical method are effective and practical.

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