The task of deblurring, a form of image restoration, is to recover an image from its blurred version. Whereas most existing methods assume a small amount of additive noise, image restoration under ...significant additive noise remains an interesting research problem. We describe two techniques to improve the noise handling characteristics of a recently proposed variational framework for semi-blind image deblurring that is based on joint segmentation and deblurring. One technique uses a structure tensor as a robust edge-indicating function. The other uses nonlocal image averaging to suppress noise. We report promising results with these techniques for the case of a known blur kernel
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for ...tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.
Primary cilia act as antenna receivers of environmental signals and enable effective neuronal or glial responses. Disruption of their function is associated with circuit disorders. To understand the ...signals these cilia receive, we comprehensively mapped cilia’s contacts within the human cortical connectome using serial-section EM reconstruction of a 1 mm3 cortical volume, spanning the entire cortical thickness. We mapped the “contactome” of cilia emerging from neurons and astrocytes in every cortical layer. Depending on the layer and cell type, cilia make distinct patterns of contact. Primary cilia display cell-type- and layer-specific variations in size, shape, and microtubule axoneme core, which may affect their signaling competencies. Neuronal cilia are intrinsic components of a subset of cortical synapses and thus a part of the connectome. This diversity in the structure, contactome, and connectome of primary cilia endows each neuron or glial cell with a unique barcode of access to the surrounding neural circuitry.
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•Cortical neuronal and astroglial cilia are structurally and connectomically diverse•Unlike neuronal cilia, astrocyte cilia are mostly in pockets or embedded inside soma•The contactome of each cilium enables unique access to surrounding neural circuitry•Human neuronal primary cilia are components of tetrapartite synapses
The unique pattern of primary cilia contacts with the surrounding cortical circuitry may enable cilia signaling to serve as a mechanism through which local environmental signals can shape and refine neuronal circuits. Disruptions in the primary cilia connectome may thus contribute to circuit dysfunction in ciliopathies and other human brain disorders.
To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic ...millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.
We present a system which registers image sequences acquired by very different sources, so that multiple views could be transformed to the same coordinates system. This enables the functionality of ...automatic object identification and confirmation across views and platforms. The capability of the system comes from three ingredients: 1) image context enlargement through temporal integration; 2) robust motion estimation using the G-RANSAC framework with a relaxed correspondence criteria; 3) constrained motion estimation within the G-RANSAC framework. The proposed system has worked successfully on thousands of frames from multiple collections with significant variations in scale and resolution.
Opaque Attribute Alignment Sleeman, J.; Alonso, R.; Hua Li ...
2012 IEEE 28th International Conference on Data Engineering Workshops,
2012-April
Conference Proceeding
Ontology alignment describes a process of mapping ontological concepts, classes and attributes between different ontologies providing a way to achieve interoperability. While there has been ...considerable research in this area, most approaches that rely upon the alignment of attributes use labelbased string comparisons of property names. The ability to process opaque or non-interpreted attribute names is a necessary component of attribute alignment. We describe a new attribute alignment approach to support ontology alignment that uses the density estimation as a means for determining alignment among objects. Using the combination of similarity hashing, Kernel Density Estimation (KDE) and Cross entropy, we are able to show promising F-Measure scores using the standard Ontology Alignment Evaluation Initiative (OAEI) 2011 benchmark.
This paper explores the problem of effectively simulating realistic populations of background agents in large-scale virtual environments for training and mission rehearsal. It explains why such ...populations are needed, and surveys the behaviors one would want to see them exhibit. It argues that a successful simulation of a background population ought to be credible, scalable and maintainable, while defining what is meant by those terms. It identifies the key technical challenges as being the efficiency of behavior authoring, and the efficiency of simulation. Finally, it describes some ways such a background population simulation can be verified and validated.
A major account of Renaissance portraiture by one of the
twentieth century's most eminent art historians In this
book, John Pope-Hennessy provides an unprecedented look at two
centuries of experiment ...in portraiture during the Renaissance.
Pope-Hennessy shows how the Renaissance cult of individuality
brought with it a demand that the features of the individual be
perpetuated, a concept first manifested in the portraits that fill
the great Florentine fresco cycles and led, later in the fifteenth
century, to the creation of the independent portrait by such
artists as Sandro Botticelli, Antonio del Pollaiuolo, Giovanni
Bellini, and Antonello da Messina. Pope-Hennessy goes on to
describe the process by which Titian and the great artists of the
High Renaissance transformed the portrait from a record of
appearance into an analysis of character.
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