'Feature type' diaporamas de présentation

Functional Annotation

Functional Annotation. Episode 2: Preliminary Results. The Group. Recap. What is Functional Annotation The I mportance of Functional Annotation The Biology of H . haemolyticus Background for Functional Annotation Pros/Cons of Available Approaches Planned Approach Breadth Depth .

1.04k views • 68 slides

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. Present by ???. Outline. introduction Related work MAP formulation Affinity model Results Conclusion. overview. STAGE 1. STAGE 2. STAGE 3. STAGE 4. Introduction.

725 views • 58 slides

GIS211

GIS211. GIS Data Week 3 HW Britz. The Vector Data Model.

1.44k views • 52 slides

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. Yuan Li, Chang Huang and Ram Nevatia. Yuan Li. Outline. introduction Related work MAP formulation Affinity model Results Conclusion. overview. STAGE 1. STAGE 2. STAGE 3. STAGE 4. Introduction.

596 views • 49 slides

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. Yuan Li, Chang Huang and Ram Nevatia. Yuan Li. O utline. introduction Related work MAP formulation Affinity model Results Conclusion. overview. STAGE 1. STAGE 2. STAGE 3. STAGE 4. Introduction.

677 views • 48 slides

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. Yuan Li, Chang Huang and Ram Nevatia. Yuan Li. outline. Instruction Related work MAP formulation Affinity model Results Conclusion. overview. Introduction.

529 views • 42 slides

Discussion about Phase 1

Discussion about Phase 1. A polyline feature class will be spatially and topologically adjusted to represent the centerline of undivided roadways and the centerline of both directions of divided roadways, including one-way couplets, ramps, wyes, and frontage roads.

431 views • 36 slides

Learning Sensorimotor Contingencies

487 views • 32 slides

404 views • 31 slides

Learning visual representations for unfamiliar environments

Learning visual representations for unfamiliar environments. Kate Saenko , Brian Kulis , Trevor Darrell UC Berkeley EECS & ICSI. The challenge of large scale visual interaction. ?. Last decade has proven the superiority of models learned from data vs. hand engineered structures!.

442 views • 28 slides

Managing Data Interoperability with FME

451 views • 27 slides

What is iTSIP ?

What is iTSIP ?. Integrated TSIP Data Set Exchange Profile. Regional Data Sets. From TSDEA Functional Requirements Document [2006] What needs to be added to meet regional integration requirements? Spatial Location Connection Seg Code Lists OperatorID (in RTDP AgencyList )

289 views • 21 slides

335 views • 21 slides

Online resources for georeferencing

Online resources for georeferencing. http://herpnet.org/Gazetteer/GeorefResources.htm. Online resources for georeferencing. http://canadensys.net/digitization/georeferencing. Google Maps. http://maps.google.com Search & directions Free text search, zoom -dependent

290 views • 19 slides

pre-ordering dependency subtreeS for phrase-based smt

276 views • 15 slides

Digital Gazetteer Standards for History and Culture

292 views • 15 slides

Desert Garden

Created by Maria Edited as demo by Mr. Towler. Desert Garden. The Problem Question:. How can the locations and identities of different plants in Desert Garden be made available to visitors?. Getting Started: Create Base Map.

262 views • 14 slides

Data Mining Recommender

Yoonjung Choi. Data Mining Recommender. Description. The Knowledge Discovery in Databases (KDD) is concerned with the development of methods and techniques for making sense of data . One of the important step in KDD is data mining

211 views • 11 slides

MTDB Quality Measures

MTDB Quality Measures. Scott Fifield Chief, Geographic Process and Quality Management Branch Quality, Assessments, and Evaluations Work Group Chair. Spatial Update Quality. Every edge in the MTDB has a Means ID (MID) based on authority, task, and source

209 views • 9 slides

Transfer of UML models between Rational Rose and Argo using XMI. Experience from Norway.

323 views • 8 slides