Efficient Shape Priors for Spline-Based Snakes

Delgado-Gonzalo, Ricard; Schmitter, Daniel; Uhlmann, Virginie; Unser, Michaël

doi:10.1109/tip.2015.2457335

Cited by 16 publications

(7 citation statements)

References 40 publications

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“…Using our proposed framework, we first computed the aligned training set {r k } k=1,...,20 using the similarity transformation and then computed r mean as given by Theorem 3. The resulting learned shape ( Figure 5, red shape in middle row) can be further used either for classification (see Section IX-B) or as a trained shape prior for segmentation problems [41], [42]. It characterizes the population in terms of its shape and, hence, can be viewed as an average shape.…”

Section: ) Learning Shape Priorsmentioning

confidence: 99%

Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain

Schmitter

Unser

2018

IEEE Trans. on Image Process.

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Abstract-We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose elements are subspaces that contain all affine transformations of the training samples. The alignment relies on orthogonal projection operators that have a closed form. We then present algorithms to learn shape dictionaries according to the structure of the data that needs to be encoded: 1) projectionbased functional principal-component analysis for homogeneous data and 2) continuous-domain sparse shape encoding to learn dictionaries that contain imbalanced data, outliers, or different types of shape structures. Through parametric spline curves, we provide a detailed and exact implementation of our method. We demonstrate that it requires fewer parameters than purely discrete methods and that it is computationally more efficient and accurate. We illustrate the use of our framework for dictionary learning of structures in biomedical images as well as for shape analysis in bioimaging.

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Section: ) Learning Shape Priorsmentioning

confidence: 99%

Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain

Schmitter

Unser

2018

IEEE Trans. on Image Process.

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“…Finally, the last term, E shape , corresponds to the shape-prior energy contribution detailed in [28]. This term measures the similarity between the snake and its projection on a given reference curve.…”

Section: Methodsmentioning

confidence: 99%

FlyLimbTracker: An active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila

et al. 2017

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Understanding the biological underpinnings of movement and action requires the development of tools for quantitative measurements of animal behavior. Drosophila melanogaster provides an ideal model for developing such tools: the fly has unparalleled genetic accessibility and depends on a relatively compact nervous system to generate sophisticated limbed behaviors including walking, reaching, grooming, courtship, and boxing. Here we describe a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving D. melanogaster. We show that this approach yields a more than 6-fold reduction in user intervention when compared with fully manual annotation and can be used to annotate videos with low spatial or temporal resolution for a variety of locomotor and grooming behaviors. FlyLimbTracker, the software implementation of this method, is open-source and our approach is generalizable. This opens up the possibility of tracking leg movements in other species by modifications of underlying active contour models.

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“…Finally, the last term, E shape , corresponds to the shape-prior energy contribution detailed in [25]. This term measures the similarity between the snake and its projection on a given reference curve.…”

Section: Materials and Methods Drosophilamentioning

confidence: 99%

FlyLimbTracker: an active contour based approach for leg segment tracking in unmarked, freely behavingDrosophila

Uhlmann

Ramdya

Delgado-Gonzalo

et al. 2016

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24Understanding the biological underpinnings of movement and action requires 25 the development of tools for precise, quantitative, and high-throughput 26 measurements of animal behavior. Drosophila melanogaster provides an ideal 27 model for developing such tools: the fly has unparalleled genetic accessibility 28 and depends on a relatively compact nervous system to generate 29 sophisticated limbed behaviors including walking, reaching, grooming, 30 courtship, and boxing. Here we describe a method that uses active contours 31 to semi-automatically track body and leg segments from video image 32 sequences of unmarked, freely behaving Drosophila. We show that this 33 approach is robust to wide variations in video spatial and temporal resolution 34 and that it can be used to measure leg segment motions during a variety of 35 locomotor and grooming behaviors. FlyLimbTracker, the software 36 implementation of this method, is open-source and our approach is 37 generalizable. This opens up the possibility of tracking leg movements in 38 other species by modifications of underlying active contour models. 39 40 41 42 43 Author Summary 44 In terrestrial animals, including humans, fundamental actions like locomotion 45 and grooming emerge from the displacement of multiple limbs through space. 46 Therefore, precise measurements of limb movements are critical for 47 investigating and, ultimately, understanding the neural basis for behavior. The 48 vinegar fly, Drosophila melanogaster, is an attractive animal model for 49 uncovering general principles about limb control since its genome and 50 nervous system are easy to manipulate. However, existing methods for 51 measuring leg movements in freely behaving Drosophila have significant 52 drawbacks: they require complicated experimental setups and provide limited 53 information about each leg. Here we report a new method -and provide its 54 open-source software implementation, FlyLimbTracker -for tracking the body 55 and leg segments of freely behaving flies using only computational image 56 processing approaches. We illustrate the power of this method by tracking fly 57 limbs during five distinct walking and grooming behaviors and from videos 58 across a wide range of spatial and temporal resolutions. Our approach is 59 generalizable, allowing researchers to use and customize our software for 60 limb tracking in Drosophila and in other species. 61 62 FlyLimbTracker is implemented in Java as a freely available plug-in for 131 Icy, a cross-platform, multi-purpose image processing environment [16]. 132 Briefly, FlyLimbTracker performs leg segment tracking in several steps. First, 133 the user is asked to manually initialize the position of a fly's body and leg 134 segments in a single frame of the image sequence. This information is 135 combined with image features to propagate body and leg segmentation to the 136

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Efficient Shape Priors for Spline-Based Snakes

Cited by 16 publications

References 40 publications

Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain

Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain

FlyLimbTracker: An active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila

FlyLimbTracker: an active contour based approach for leg segment tracking in unmarked, freely behavingDrosophila

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