Curve Propagation, Level Set Methods and Grouping

Paragios, Nikos

doi:10.1007/0-387-28831-7_9

Cited by 6 publications

(7 citation statements)

References 40 publications

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“…The alignment of images requires establishing mathematical relationships that map pixel coordinates from the unaligned images to their aligned versions. Five parametric 2D planar transformations have been defined [27] (see Figure 1). Each one of these transformations can be described by a transformation matrix (τ(p)), with p being a vector of parameters.…”

Section: Limitations Of Existing Alignment Methodsmentioning

confidence: 99%

“…These methods are usually slow since the number of pixel pairs to evaluate can be very large. Direct methods work by directly minimising pixel-to-pixel dissimilarities; a different class of algorithms works by extracting a sparse set of features and then matching these to each other [27,30,31]. Feature-based approaches have the advantage of being more robust against scene movement, are potentially faster, and can be used to automatically discover the adjacency (overlap) relationships among an unordered set of images [32].…”

Section: Limitations Of Existing Alignment Methodsmentioning

confidence: 99%

“…Several works have developed hierarchical coarse-to-fine search techniques based on image pyramids [27] when the approximate image alignment is unknown. In this work, since the approximate position of each image is assumed to come from the known camera pose, it has been decided to limit the search space by setting lower and upper bounds for the transformation parameters.…”

Section: Fine Pixel-based Alignment Methodsmentioning

confidence: 99%

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Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries

Mineo

Montinaro

Fustaino

et al. 2022

Sensors

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Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulation of the thermographic instrumentation enables the possibility of performing inspections of large components with complex geometries by collecting multiple thermographic images from optimal positions. The robotisation of the thermographic inspection is highly desirable to improve assessment speed and repeatability without compromising inspection accuracy. Although integrating a robotic setup for thermographic data capture is not challenging, the application of robotic thermography has not grown significantly to date due to the absence of a suitable approach for merging multiple thermographic images into a single presentation. Indeed, such an approach must guarantee accurate alignment and consistent pixel blending, which is crucial to facilitate defect detection and sizing. In this work, an innovative inspection platform was conceptualised and implemented, consisting of a pulsed thermography setup, a six-axis robotic manipulator and an algorithm for image alignment, correction and blending. The performance of the inspection platform is tested on a convex-shaped specimen with artificial defects, which highlights the potential of the new combined approach. This work bridges a technology gap, making thermographic inspections more deployable in industrial environments. The proposed fine image alignment approach can find applicability beyond thermographic non-destructive testing.

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Section: Limitations Of Existing Alignment Methodsmentioning

confidence: 99%

Section: Limitations Of Existing Alignment Methodsmentioning

confidence: 99%

Section: Fine Pixel-based Alignment Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries

Mineo

Montinaro

Fustaino

et al. 2022

Sensors

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“…C 1 is curve, F is speed function that controls the motion of the contour, N is inward normal vector to the contour. The level set surface needs to remain with the same smoothness level; hence initialization has to be given at regular intervals -this process is called as reinitialization [29,30].…”

Section: Level Set Formulationmentioning

confidence: 99%

Modified distance regularized level set evolution for brain ventricles segmentation

Jayaraman

Reddy

Mahadevappa

et al. 2020

Vis. Comput. Ind. Biomed. Art

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Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

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“…Minimal path computation [6] as well as distance transform [7] are used in 2D and 3D image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Computing the Eccentricity Transform of a Polygonal Shape

Kropatsch¹,

Ion²,

Peltier³

Lecture Notes in Computer Science

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Abstract. The eccentricity transform associates to each point of a shape the distance to the point farthest away from it. The transform is defined in any dimension, for open and closed manyfolds, is robust to Salt & Pepper noise, and is quasi-invariant to articulated motion. This paper presents and algorithm to efficiently compute the eccentricity transform of a polygonal shape with or without holes. In particular, based on existing and new properties, we provide an algorithm to decompose a polygon using parallel steps, and use the result to derive the eccentricity value of any point.

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Curve Propagation, Level Set Methods and Grouping

Cited by 6 publications

References 40 publications

Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries

Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries

Modified distance regularized level set evolution for brain ventricles segmentation

Computing the Eccentricity Transform of a Polygonal Shape

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