Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization

Evangelidis, Georgios; Horaud, Radu

doi:10.1109/tpami.2017.2717829

Cited by 121 publications

(133 citation statements)

References 53 publications

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“…The identify step is based on a joint mixture-of-Gaussians with affine-alignment model, similar to the approach proposed by Evangelidis and Horaud (Evangelidis and Horaud, 2018). The model estimates a statistical atlas that summarizes the mean location and color, for each cell, along with the variability of these features ( Fig.3C & Fig.8A).…”

Section: Automating the Identification Of Neuronal Cell Classesmentioning

confidence: 99%

NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging inC. elegans

Yemini

Lin

Nejatbakhsh

et al. 2019

Preprint

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ABSTRACTComprehensively resolving single neurons and their cellular identities from whole-brain fluorescent images is a major challenge. We achieve this in C. elegans through the engineering and use of a multicolor transgene called NeuroPAL (a Neuronal Polychromatic Atlas of Landmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that allows comprehensive determination of neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. Here we showcase three studies that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover novel changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding and novel neuronal asymmetries for these stimuli. We present a software package that enables semi-automated determination of all neuronal identities based on color and positional information. The NeuroPAL framework and software provide a means to design landmark atlases for other tissues and organisms. In conclusion, we expect NeuroPAL to serve as an invaluable tool for gene expression analysis, neuronal fate studies, and for mapping whole-brain activity patterns.

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Section: Automating the Identification Of Neuronal Cell Classesmentioning

confidence: 99%

NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging inC. elegans

Yemini

Lin

Nejatbakhsh

et al. 2019

Preprint

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“…One also notices that the proposed algorithm could be used to find an initial segmentation before being applied incrementally as new data become available, e.g. (Evangelidis and Horaud, 2017).…”

Section: Discussionmentioning

confidence: 99%

Plane-extraction from depth-data using a Gaussian mixture regression model

Marriott

Pashevich

Horaud

2018

Pattern Recognition Letters

Self Cite

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We propose a novel algorithm for unsupervised extraction of piecewise planar models from depth-data. Among other applications, such models are a good way of enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive their surroundings and to navigate in three dimensions. We propose to do this by fitting the data with a piecewise-linear Gaussian mixture regression model whose components are skewed over planes, making them flat in appearance rather than being ellipsoidal, by embedding an outlier-trimming process that is formally incorporated into the proposed expectation-maximization algorithm, and by selectively fusing contiguous, coplanar components. Part of our motivation is an attempt to estimate more accurate plane-extraction by allowing each model component to make use of all available data through probabilistic clustering. The algorithm is thoroughly evaluated against a standard benchmark and is shown to rank among the best of the existing state-of-the-art methods. 2013), or any other similar device.Recently, dense depth-data have become readily available due to the development of affordable structured light and time-of-flight cameras. Each of these sensor-types produces images of depth-related values that can be projected as clouds of 3D points. These point-clouds, however, are nothing more than a noisy set of points that only sample the environment. The observer must then be able to make sense of these observations by using them to construct a model of some form, e.g. a set of planar surfaces.An alternative to a piecewise-planar model might be to attempt to represent the environment as a set of known objects. To do so, however, comprehensive objectrecognition training would be required. In practice, in a dynamic, real-world environment, such a technique would ultimately only be able to complement a more general, unsupervised approach. Planar primitives are sufficiently general to model most environments. They are particularly appropriate in the home and office, where planar surfaces are prevalent, but can also handle more complex scenes, approximating curved surfaces in a piecewise fashion. Although a piecewise planar representation of the environment may not allow many objects to be iden-

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“…The practical meaning of x i can be regarded as the representation for the quality of smart meter i. N is the total number of smart meters to be clustered; µ k is the expected vector of the kth Gaussian model and Σ k is the variance of the kth Gaussian model, respectively. In the GMM clustering process, the parameters of each Gaussian distribution model need to be estimated and the estimation is performed by the expectation maximum (EM) algorithm [23]. Therefore, the steps of the GMM clustering algorithm for operation quality of smart meters' data are as follows.…”

Section: Index Mining Of Operation Quality Assessment Of Smart Metersmentioning

confidence: 99%

Comprehensive Quality Assessment Algorithm for Smart Meters

Liu

Ye²,

Lin

et al. 2019

Energies

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With the improvement of operation monitoring and data acquisition levels of smart meters, mining data associated with smart meters becomes possible. Besides, precisely assessing the operation quality of smart meters plays an important role in purchasing metering equipment and improving the economic benefits of power utilities. First, seven indexes for assessing operation quality of smart meters are defined based on the metering data and the Gaussian mixture model (GMM) clustering algorithm is applied to extract the typical index data from the massive data of smart meters. Then, the combination optimization model of index’s weight is presented with the subject experience of experts and object difference of data considered; and the comprehensive assessment algorithm based on the revised technique for order preference by similarity to an ideal solution (TOPSIS) is proposed to evaluate the operation quality of smart meters. Finally, the proposed data-driven assessment algorithm is illustrated by the actual metering data from Zhejiang Ningbo power supply company of China and practical application is briefly introduced. The results show that the proposed algorithm is effective for assessing the operation quality of smart meters and could be helpful for energy measurement and asset management.

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Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization

Cited by 121 publications

References 53 publications

NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging inC. elegans

NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging inC. elegans

Plane-extraction from depth-data using a Gaussian mixture regression model

Comprehensive Quality Assessment Algorithm for Smart Meters

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