1,180 research outputs found
Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images
Efficient and easy segmentation of images and volumes is of great practical
importance. Segmentation problems that motivate our approach originate from
microscopy imaging commonly used in materials science, medicine, and biology.
We formulate image segmentation as a probabilistic pixel classification
problem, and we apply segmentation as a step towards characterising image
content. Our method allows the user to define structures of interest by
interactively marking a subset of pixels. Thanks to the real-time feedback, the
user can place new markings strategically, depending on the current outcome.
The final pixel classification may be obtained from a very modest user input.
An important ingredient of our method is a graph that encodes image content.
This graph is built in an unsupervised manner during initialisation and is
based on clustering of image features. Since we combine a limited amount of
user-labelled data with the clustering information obtained from the unlabelled
parts of the image, our method fits in the general framework of semi-supervised
learning. We demonstrate how this can be a very efficient approach to
segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe
Hippodamus of Miletus and the Character of the Athenian Dikastic Oath (Arist. Pol. 2.8)
Aristotleâs discussion of Hippodamusâ proposal of a reformed legal procedure implies that the Athenian dikastsâ oath to vote âin accordance with justiceâ was not understood to be restricted to exceptional cases
Computing segmentations directly from x-ray projection data via parametric deformable curves:Paper
Prising av fastlegepraksiser
Med utgangspunkt i 78 overdragelser av fastlegepraksiser i perioden 2006 til 2012 har vi utfÞrt en empirisk studie av verdidriverne i dette markedet. Vi har konsentrert oss om praksiser prissatt av en nemnd som operer pÄ vegne av Den Norske Legeforening, da nemndsavgjÞrelsene innholder den informasjonen vi er ute etter til analyseformÄl. Ved Ä undersÞke informasjonen i nemndsavgjÞrelsene har vi forsÞkt Ä identifisere hvilke egenskaper ved en fastlegepraksis som pÄvirker, og i hvilken grad de pÄvirker, den prisen de blir omsatt for i dagens marked. Ved Ä benytte oss av hedonisk prising har vi kommet fram til at praksisens pasientliste, samt omsetning, signifikant pÄvirker prisen pÄ praksisen. Praksisens beliggenhet var ogsÄ utslagsgivende for den prisen nemnda kommer fram til, i tillegg til at gruppepraksiser blir priset signifikant hÞyere enn solopraksiser. Manglende journaler og avtaler, i tillegg til diskontinuitet i praksisen, er med pÄ Ä redusere prisen. Vi finner ogsÄ at mangelfulle lokaler, samt manglende hjelpepersonell, er med pÄ Ä redusere den prisen en selgende lege fÄr. Til slutt i oppgaven har vi kommet fram til en prismodell som kan brukes til Ä gi en indikasjon pÄ hvilken pris en lege kan forvente Ä fÄ solgt praksisen sin for. Denne prismodellen reflekterer det overdragelssystemet som praktiseres i dag, og ikke de Þkonomiske verdiene som faktisk finnes i praksisen.
SĂ„ vidt oss bekjent er det ikke gjort liknende studier i Norge tidligere
Effective Image Database Search via Dimensionality Reduction
Image search using the bag-of-words image representa-tion is investigated further in this paper. This approach has shown promising results for large scale image collections making it relevant for Internet applications. The steps in-volved in the bag-of-words approach are feature extraction, vocabulary building, and searching with a query image. It is important to keep the computational cost low through all steps. In this paper we focus on the efficiency of the technique. To do that we substantially reduce the dimen-sionality of the features by the use of PCA and addition of color. Building of the visual vocabulary is typically done using k-means. We investigate a clustering algorithm based on the leader follower principle (LF-clustering), in which the number of clusters is not fixed. The adaptive nature of LF-clustering is shown to improve the quality of the visual vocabulary using this. In the query step, features from the query image are assigned to the visual vocabulary. The di-mensionality reduction enables us to do exact feature la-beling using kD-tree, instead of approximate approaches normally used. Despite the dimensionality reduction to be-tween 6 and 15 dimensions we obtain improved results com-pared to the traditional bag-of-words approach based on 128 dimensional SIFT feature and k-means clustering. 1
- âŠ