Ppt on edge detection tutorial

Recent Advances in Face Detection

Classifier design: ensemble, cascade Post processing: combing detection results In This Tutorial Focus on detecting Face Detection Video Color Gray Scale Single Image Upright frontal Pose Rotation Occlusion Focus on detecting upright, frontal faces in a single gray-scale /cells with a basically uniform intensity” to search for candidates Level 2: local histogram equalization followed by edge detection Level 3: search for eye and mouth features for validation Knowledge-Based Method: [Kotropoulos & Pitas /


Object Detection and Recognition

we haven’t mentioned moments, statistics metrics: Earth mover’s distance, ... edges, curves metrics: Hausdorff, shape context, ... 3D: surfaces, spin images /cascaded classifiers) Next few slides adapted Grauman & Liebe’s tutorial http://www.vision.ee.ethz.ch/~bleibe/teaching/tutorial-aaai08/ Also see Paul Viola’s talk (video) / K. Grauman, B. Leibe Freund & Schapire 1995 50 Cascading classifiers for detection For efficiency, apply less accurate but faster classifiers first to immediately discard windows/


Privacy in Social Networks:

Privacy in Social Networks: Introduction Model: Social Graph From SIGMOD11-tutorial Model: Social Graph From SIGMOD11-tutorial Model: Social Graph From SIGMOD11-tutorial Model: Social Graph Facebook graph from: http://www.flickr.com/photos/greenem/11696663/ Model: /of friends on LiveJournal 4.4∙106 nodes, 77∙106 edges Uniqueness: With 7 nodes, an average of 70 nodes can be de-anonymized Although log(4.4∙106) ≈ 15 Efficiency: |T| is typically ~9∙104 Detectability: Only 7 nodes Many subgraphs of 7 nodes in G/


High Throughput Sequencing: Microscope in the Big Data Era

data-driven scheduling, demand forecast, staffing, …. Scope of this tutorial Assembly: three points of view Software engineering Computational complexity theoretic Information / network flow Transcripts as paths Sparsest decomposition of edge-flow into paths Deals with inter-transcript repeats Practical/ assembly Genome wide association studies Information bounds Phylogenetic tree reconstruction Pathogen detection Compression Compress memory? Privacy Information theoretic methods? Engineering challenges to /


1 1 OpenCV Tutorial Omri Perez Adapted from: Gary Bradski Senior Scientist, Willow Garage Consulting Professor: Stanford CS Dept.

Tutorial Omri Perez Adapted from: Gary Bradski Senior Scientist, Willow Garage Consulting Professor: Stanford CS Dept. http://opencv.willowgarage.com www.willowgarage.com Vision is Hard Camera Model, Lens, Problems and Corrections OpenCV OpenCV Tour 2CS324 What is it? – Turning sensor readings into perception. Why is it hard? – It’s just numbers. Maybe try gradients to find edges/– Use of the distanceTransform on edge images and voroni tessel. Edge (Canny edge detection) CS32451 Ffilldemo (flood fill methods/


11/11/2015Copyright James D. Johnston 20041 A Lightning Tutorial on Spatial Hearing James D. Johnston Neural Audio Kirkland, Wa, USA.

Second, diffuse signals should not be recorded at one point and then reproduced as plane (or spherical) waves, because the auditory system will detect any “leading edges” in them as false directional cues, leading to the classic problem of being “sucked into” loudspeakers during things like applause, or in multichannel/, the field is just now becoming feasible. 11/11/2015Copyright James D. Johnston 200425 A variety of tutorials can be found at: www.aes.org/sections/pnw Look at the “slide deck”” tab.


Science of Science Research and Tools Tutorial #09 of 12 Dr. Katy Börner Cyberinfrastructure for Network Science Center, Director Information Visualization.

tool. 12 Tutorials in 12 Days at NIH—Feedback from Tutorial #8 3 1.Science of Science Research 2.Information Visualization 3.CIShell Powered Tools: Network Workbench and Science of Science Tool 4.Temporal Analysis—Burst Detection 5.Geospatial/ Function File but note the space after ;) 17 SAS Dataset – Extract Co-Author Network cont. Nodes: 127,879 authors Edges: 640,861 co-author relationships 18 [#09] Large Network Analysis and Visualization  General Overview  Designing Effective Network Visualizations /


CSE 185 Introduction to Computer Vision Face Detection.

Classifier design: ensemble, cascade Post processing: combing detection results In This Tutorial Face Detection Video ColorGray Scale Single Image ColorGray Scale Upright frontal Pose Rotation Occlusion Motion Depth Voice Focus on detecting –upright, frontal faces –in a single gray/ with a basically uniform intensity” to search for candidates Level 2: local histogram equalization followed by edge detection Level 3: search for eye and mouth features for validation Knowledge-Based Method : [Kotropoulos & Pitas/


Local features: detection and description Tuesday October 6 th 2015 Devi Parikh Virginia Tech Slide credit: Kristen Grauman 1 Disclaimer: Most slides have.

11 Recall: Edge detection f Source: S. Seitz Edge Derivative of Gaussian Edge = maximum of derivative 12 f Edge Second derivative of Gaussian (Laplacian) Edge = zero crossing of second derivative Source: S. Seitz Recall: Edge detection 13 From edges to blobs Edge = ripple Blob/ translation, rotation Slide credit: Kristen Grauman 30 Photometric transformations Figure from T. Tuytelaars ECCV 2006 tutorial Slide credit: Kristen Grauman 31 Raw patches as local descriptors The simplest way to describe the /


1 Information Visualization: Principles, Promise, and Pragmatics Marti Hearst CHI 2003 Tutorial.

of information using spatial or graphical representations, to facilitate comparison, pattern recognition, change detection, and other cognitive skills by making use of the visual system. 6 Information/ usually do not accurately predict how their invention will be used This tutorial will emphasize –Getting past the coolness factor –Examining usability studies 19 /Aggregate nodes using an icon (e.g. all the checkout pages) Edges represent transitions –Wider means more transitions 135 Slide by Wayne Kao Customer/


Practical Online Retrieval Evaluation SIGIR 2011 Tutorial Filip Radlinski (Microsoft) Yisong Yue (CMU)

Document: … Challenges with Offline Evaluation Query: “ski jump world record” Document: Tutorial Goals Provide an overview of online evaluation – Online metrics: What works when / problem: Does there exist an interleaving algorithm not subject to such edge cases? Clicks versus Relevance Presentation bias affects clicks – Interleaving addresses /SIGIR). RADLINSKI, F., BENNETT, P., AND YILMAZ, E. 2011. Detecting Duplicate Web Documents using Clickthrough Data. In Proceedings of the ACM International Conference/


ICCV Tutorial 2007 Philip Torr Papers, presentations and videos on web.....

ICCV Tutorial 2007 Philip Torr Papers, presentations and/ ) = 0.9 Cow Instance Do we really need accurate models? Segmentation boundary can be extracted from edges Rough 3D Shape-prior enough for region disambiguation Energy of the Pose-specific MRF Energy to be minimized Unary/ face detector to find location of face in an image Initialisation Place shape prior relative to face detection Define region over which to perform segmentation Adjustment Vary parameters of shape prior to find lowest segmentation /


Probabilistic Learning Tutorial: P. Smyth, UC Irvine, August 2005 Principles and Applications of Probabilistic Learning Padhraic Smyth Department of Computer.

Morgan Kaufmann, 1990 Probabilistic Learning Tutorial: P. Smyth, UC Irvine, August 2005 Graphical Models Represent dependency structure with a directed graph –Node random variable –Edges encode dependencies Absence of edge -> conditional independence –Directed and /–Discovering trends over time –Detecting unusual papers and authors –Interactive browsing of a digital library via topics –Parsing documents (and parts of documents) by topic –and more….. Probabilistic Learning Tutorial: P. Smyth, UC Irvine/


Scanning Tutorial. Scanning Stations The following tutorial will assist you in learning how to scan images and documents. The following tutorial will.

in learning how to scan images and documents. The following tutorial will assist you in learning how to scan images and /is optional. Use this only if you want more accurate edges on your image. NOTE: This step is optional. Use this only if you want more accurate edges on your image. Click Prescan for a larger view / letter or word(s) in the Change to field then click Change In this example the computer has detected an error when scanning the R in the text “INFORMATIC”. The OCR proofreader believes the “R” to/


LUDWIG- MAXIMILIANS- UNIVERSITÄT MÜNCHEN DATABASE SYSTEMS GROUP INSTITUTE FOR INFORMATICS Outlier Detection Techniques Hans-Peter Kriegel, Peer Kröger,

provided –Unsupervised Scenario In most applications there are no training data available In this tutorial, we focus on the unsupervised scenario Kriegel/Kröger/Zimek: Outlier Detection Techniques (KDD 2010) DATABASE SYSTEMS GROUP 11 Introduction Are outliers just a side product/Distance-based Approaches Variant –Outlier Detection using In-degree Number [Hautamaki et al. 2004] Idea –Construct the kNN graph for a data set »Vertices: data points »Edge: if q  kNN(p) then there is a directed edge from p to q –/


ELEC 516 Digital VLSI System Design and Design Automation (Spring 2010) Tutorial on VHDL Language -- Introduction and Design Methodology By Qian zhiliang.

Z_XOR <= A xor B; wait on A,B; end process A_O_X ; end RTL; Clocked Process: Clock Edge Detection If clock_signal_ nameEVENT and clock_signal_name=1 [clock_signal_ name=1 and clock_signal_ nameEVENT not clock_signal_ nameSTABLE and clock_signal_ name=1 clock_signal_ /lo_time; end loop end process; end ALG; ELEC 516 Digital VLSI System Design and Design Automation (Spring 2010) Tutorial on Verilog Language --Brief overview By Qian zhiliang (Toby) Reference Professor Don Thomas (Carnegie Mellon University ) ‘/


Science of Science Research and Tools Tutorial #02 of 12 Dr. Katy Börner Cyberinfrastructure for Network Science Center, Director Information Visualization.

interactivity 12 Tutorials in 12 Days at NIH—Feedback from Tutorial #1 4 1.Science of Science Research 2.Information Visualization 3.CIShell Powered Tools: Network Workbench and Science of Science Tool 4.Temporal Analysis—Burst Detection 5.Geospatial /a  Title  Name of map maker  Date of creation  Explanation of all visual encodings, i.e., what do nodes, edges, colors, etc. represent?  Information on dataset, dataset preparation, analysis.  Short explanation of unique features and insights (if space /


Tools for large graph mining WWW 2008 tutorial Part 4: Case studies Jure Leskovec and Christos Faloutsos Machine Learning Department Joint work with: Lada.

Huberman, Jon Kleinberg, Andreas Krause, Mary McGlohon, Ajit Singh, and Jeanne VanBriesen. Tutorial outline  Part 1: Structure and models for networks  What are properties of large/2008Part 4-68 Examples of graph features  Projection graph  Number of nodes/edges  Number of connected components  Size and density of the largest connected component/ fool reputation-based systems  Wow! This should lead to nice and easily detectable cliques of fraudsters …  Not quite  experiments with a real eBay dataset/


For Version 9.1 and later XVL Studio Premium Tutorial For Version 9.1 and later LATTICE TECHNOLOGY, INC.

standard location, this maps to: “C:Program FilesLatticeStudio2Samplesmodel”. In this tutorial, we will use the DigitalCamera.xv2 and DigitalCamera.xv3 models. Data/Detection 1.Select Evaluation > Dynamic Collision Detection to open the Dynamic Collision Detection dialog. 2.Select the parameter from the dialog. 3.Click Start to start dynamic collision detection. 4.Dynamic collision detection/size of notes/dimensions. Concave / Convex edge: Determines if this edge is displayed or not Extraction limit: Maximum /


DAQ-2 Shifter Tutorial, 10 May 2016H. Sakulin / CERN PH2 DAQ2 Tutorial Outline Part 1: Your tasks as a DAQ shifter Part 2: Overview of the DAQ-2 system.

Event Display) at pt.5 Mrg2 DAQ-2 Shifter Tutorial, 10 May 2016H. Sakulin / CERN PH38 HLT farm, DAQ2 2011 extension of DAQ-1 Dell Power Edge c6100 2012 extension of DAQ-1 Dell Power Edge c6220 HLT PC 2015 Megware S2600KP HLT PC 2016 Action/luminosity. 3-10 min down-time DAQ-2 Shifter Tutorial, 10 May 2016H. Sakulin / CERN PH80 Automatic soft error recovery … From 2012, new automatic recovery procedure in top-level control node 1.Sub-system detects soft error and signals by changing its state to /


Applications of Quantum Computing in Bioinformatics

has published more than 50 scientific-research papers in proceedings and journals. On Tutorial The tutorial “Applications of Quantum Computing in Bioinformatics” covers: - Computing with Cells and /an assignment into a finite sequence of assignments sorted by the length of unit assignments. (iv) Detect -- confirms presence or absence of a unit assignment in a tube. (v) Select -- selects/graph (map of cities) of 7 nodes (cities) and 13 edges (roads). Map of cities and roads The Traveling Salesman problem /


Integrated Plug-in for NX Users

Click the Unfold Part icon to begin the process. If a solid part is detected, users will be warned to first generate a middle surface before continuing. If /specific parameters and recalculate nesting. The highest utilization for the nesting result in this tutorial is 74.172%, as illustrated in Figure 24 (next slide). Figure 23 V/Figure 52. Expand the Region Options, and toggle on the Traverse Interior Edges option and Use Tangent Edge Angle option. Use the default Angle Tolerance (45 degrees), as illustrated/


VHDL in digital circuit synthesis (tutorial) dr inż. Miron Kłosowski EA 309

VHDL in digital circuit synthesis (tutorial) dr inż. Miron Kłosowski EA 309 klosowsk@ue.eti.pg.gda.pl Library declaration Other IEEE packages: IEEE.std_logic_signed.all IEEE./ for the moment. If clk changes to ‘1’ (i.e. rising edge occurred) the signal value from the d input is transferred to the q output, and q is frozen till the next clk edge. 0 for falling edge Sequential processes (2) ’event attribute detects every change of the signal, it is suggested to use rising_edge() or falling_edge/


Overview Tutorial Outline Label Encapsulations

LANs uses ‘Shim’ Header Inserted Between Layer 2 and Layer 3 Headers Tutorial Outline Label Distribution Protocols Overview Label Encapsulations Label Distribution Protocols MPLS & /consistent across the nodes at the start May require separate loop detection/mitigation method Requires more delay before packets can be forwarded /partitioning of functionality within the network - move granular processing of packets to edge; restrict core to packet forwarding - assists in maintaining scalability of IP protocols/


9/14/97SIGCOMM97 Tutorial, Copyright Ammar and Towsley Group (Multicast) Communication in Wide Area Networks Mostafa Ammar Don Towsley College of Computing.

many-many 9/14/97SIGCOMM97 Tutorial, Copyright Ammar and Towsley Multicast Routing 9/14/97SIGCOMM97 Tutorial, Copyright Ammar and Towsley Theoretical Basis The Steiner Tree Problem is NP-Complete –Graph G = (V, E) –Positive Edge Weights W(e) –R /u Independent from underlying unicast routing u Slight efficiency cost u Contains protocol mechanisms to: –detect leaf routers –avoid packet duplicates 9/14/97SIGCOMM97 Tutorial, Copyright Ammar and Towsley PIM - Sparse Mode u Rendezvous Point (Core): Receivers Meet /


Numerical Software Libraries for the Scalable Solution of PDEs PETSc Tutorial Satish Balay Kris Buschelman Bill Gropp Lois Curfman McInnes Barry Smith.

over I,J,K indices stencil [implicit] VecScatter AO VecScatterCreate( ) VecScatter( ) Loops over entities elements edges vertices unstructured mesh structured mesh 1 2 99 of 132 Driven Cavity Model Velocity-vorticity formulation, with flow driven/nonlinear beginner 103 of 132 Debugging and Error Handling Automatic generation of tracebacks Detecting memory corruption and leaks Optional user-defined error handlers tutorial outline: debugging and errors beginner developer 104 of 132 Sample Error Traceback /


For Version 11.0 and later XVL Studio Premium Tutorial For Version 11.0 and later LATTICE TECHNOLOGY, INC.

.0 and later XVL Studio Premium Tutorial For Version 11.0 and later /type of measurement. 2.Select target(s). –As you move your mouse cursor, the corresponding points, edges, or faces are highlighted. 3.Choose the measurement method. 4.Adjust the position of the dimension./less accurate the check result will be, but the calculation speed will Geometry Difference More Options Difference Detection OP December 2012 Lattice Technology, Inc. Visual Shape Comparison You can visually compare the geometric difference/


For Version 9.2 and later XVL Studio Basic/Standard/Pro Tutorial For Version 9.2 and later LATTICE TECHNOLOGY, INC.

and later XVL Studio Basic/Standard/Pro Tutorial For Version 9.2 and later LATTICE /Revision with illustration New Revision is updated with illustration February 2011 Lattice Technology, Inc. Auto Detection and Update 1/2 During the design update process, you can visually check which parts / Output dialog, specify line parameters and font size of notes/dimensions. Concave / Convex edge: Determines if this edge is displayed or not Extraction limit: Maximum angle of adjacent two faces. 2 1 Illustration/


Pin Tutorial Kim Hazelwood David Kaeli Dan Connors Vijay Janapa Reddi.

? Pin Tutorial 2007 5 How is Instrumentation used in Compiler Research? Program analysis –Code coverage –Call-graph generation –Memory-leak detection –Instruction profiling Thread analysis –Thread profiling –Race detection Pin Tutorial 2007 6/Pin Tutorial 2007 18 Instrumentation Points Instrument points relative to an instruction: Before (IPOINT_BEFORE) After: –Fall-through edge (IPOINT_AFTER) –Taken edge (IPOINT_TAKEN_BRANCH) cmp%esi, %edx jle mov$0x1, %edi : mov $0x8,%edi count() Pin Tutorial 2007/


Perceptual and Sensory Augmented Computing Visual Object Recognition Tutorial Visual Object Recognition Bastian Leibe & Computer Vision Laboratory ETH.

for small viewpoint variations on 3D objects Perceptual and Sensory Augmented Computing Visual Object Recognition Tutorial 5 K. Grauman, B. Leibe Hough Transform Origin: Detection of straight lines in clutter  Basic idea: each candidate point votes for all lines/have each feature (token) determine as many parameters as possible  For example, lines can be detected much more efficiently from small edge elements (or points with local gradients) than from just points  For object recognition, each token /


1 Securing Wireless Systems Panos Papadimitratos ACM CCS 2009 – Tutorial: Securing Wireless Systems.

Decision Support Systems 2007 [ReidGNTS06] J. Reid, J. Nieto, T. Tang, and B. Senadji, “Detecting relay attacks with timing-based protocols,” ASIACCS 2007 [SchallerSchBC09] P. Schaller, B. Schmidt, D. Basin/F B C E D A H Route : Sequence of nodes (and edges); for simplicity: (A, G, E) Source node Destination node Intermediate / Wireless Systems: Secure Vehicular Communications © 2009 P. Papadimitratos ACM CCS 2009 – Tutorial: Securing Wireless Systems 136 Secure and Privacy-Enhancing V2V and V2I Single- and /


Www.openairinterface.org OpenAir1 Tutorial Mobile Communications Department Eurecom (Collaboration with SoC Laboratory, Telecom ParisTech Sophia)

: Used for basic framing acquisition at UE allowing demodulation of physical channels EURECOM LTE-Tutorial, Mobile Communications Department 2009 Antenna Ports  LTE supports up to 4 physical antennas / in single-frequency networks - > dual-antenna receivers + MU-detection Application Scenario 2 (Civil Protection) Ground Sensor network Backhaul link Location/the Mesh using a logical flow for topological control signaling  Edge routers can provide these measurements to IP  Measurements are attached/


John R. Vig Consultant. Most of this Tutorial was prepared while the author was employed by the US Army Communications-Electronics Research, Development.

www.ieee.org/ieeexplore Notes and References In all pointed sentences [and tutorials], some degree of accuracy must be sacrificed to conciseness. Samuel Johnson / battery consumption Improved spectrum utilization Improved surveillance capability (e.g., slow-moving target detection, bistatic radar) Improved missile guidance (e.g., on-board radar vs. ground/Lower drive level sensitivity Planar stress compensated; lower f due to edge forces and bending Lower sensitivity to radiation Higher capacitance ratio (/


Quartz Crystal Resonators and Oscillators

during fabrication, especially at elevated temperatures. Processes such as the thermocompression bonding of mounting clips to the edges of quartz plates can readily produce twinning, if not done carefully. Twinning has also produced failures in /QUARTZ CRYSTAL RESONATORS AND OSCILLATORS For Frequency Control and Timing Applications - A TUTORIAL” Rev. 8.5.3.9, by John R. Vig, November 2008. Sensor Uncertainty (“Limit of Detection”) 2-Sample Statistics: 2 * Allan Variance for continuous measurements (no /


Large-Scale Data Processing with MapReduce

not fancy math Mapping well-known algorithms to MapReduce Not a tutorial on programming Hadoop Entry point to book Setting the Stage: / data Handles synchronization Gathers, sorts, and shuffles intermediate data Handles errors and faults Detects worker failures and restarts Everything happens on top of a distributed FS MapReduce Programmers /typically involve: Performing computations at each node: based on node features, edge features, and local link structure Propagating computations: “traversing” the graph/


Multimodal User Authentication: From Theory to Practice

cl.cam.ac.uk/users/jgd1000/ Iris 4 steps Acquisition ( < 1 meter) Find iris in the image (edge detection) 3. Features extraction: - Local regions of an iris are projected onto quadrature 2D Gabor wavelets, generating complex-valued/pp. 1065-1074, September 1999. Bibliography SPEECH D.A. Reynolds and L.P. Heck: “Speaker Verification: From Research to Reality”, Tutorial, ICASSP, Salt Lake City, Utah, May 7, 2001. G. Doddington: “Speaker Recognition Based on Idiolectal Differences between Speakers”, Eurospeech /


Large-Scale Data Processing with MapReduce AAAI 2011 Tutorial Jimmy Lin University of Maryland Sunday, August 7, 2011 This work is licensed under a Creative.

not fancy math Mapping well-known algorithms to MapReduce Not a tutorial on programming Hadoop Entry point to book Setting the Stage:/to data Handles synchronization Gathers, sorts, and shuffles intermediate data Handles errors and faults Detects worker failures and restarts Everything happens on top of a distributed FS MapReduce Programmers /typically involve: Performing computations at each node: based on node features, edge features, and local link structure Propagating computations: “traversing” the graph/


Software & Services Group 1 Pin: Intel’s Dynamic Binary Instrumentation Engine Pin Tutorial Intel Corporation Presented By: Tevi Devor CGO ISPASS 2012.

level]); } Synchronization point Software & Services Group 39 Intel Thread Checker Detect data races Instrumentation –Memory operations –Synchronization operations Analysis –Use dynamic history /, (AFUNPTR)docount2, IARG_INST_PTR, IARG_BRANCH_TARGET_ADDR, IARG_BRANCH_TAKEN, IARG_END); }... Instrumentation Analysis Software & Services Group 49 Edge Counting: a Faster Version void docount(COUNTER* pedge, INT32 taken) { pedg->count += taken; /tools, tutorials Pin User Group – http://tech.groups./


Lecture 4-1CS251: Intro to AI/Lisp II Where did that edge go? April 29th, 1999.

domain to frequency domain with Fourier transform Derivative is a high-frequency booster Lecture 4-1CS251: Intro to AI/Lisp II Edge Detection Schematic Edge enhancement Edge thresholding f(x, y) f c (x, y) g(x, y) Lecture 4-1CS251: Intro to AI// Action From CSC MathematicalCSC Mathematical Lecture 4-1CS251: Intro to AI/Lisp II Fourier Transforms Check out this tutorial on the Web: Fourier tutorial Lecture 4-1CS251: Intro to AI/Lisp II Computers are Discrete Creatures Discrete version FFT Lecture 4-1CS251:/


Presents The EDG Advantage™. Launching Innovations Globally Who is Jump Lab? JUMP LAB, LLC is one of the world’s most innovative product launch companies.

minimize permanent problems associated with it. Launching Innovations Globally EDG Advantages, cont. Wellness support data… REMINDERS: Alerts regarding pre-emptive health tests to schedule, reinforcing early detection of health problems, flu shot reminders. RECORDS: Emergency/firms/facilities and drug store chains can serve as a source of member educational content (gift coupons, video tutorials, recipes, shopping nutrition tips) in exchange for name recognition on the card. Your organization’s sponsors and /


Neuroradiology for Medical Students

Students By the end of this tutorial, you will be able to /negative and there is still a strong clinical suspicion→ LP may be used for the diagnosis Detection of a subarachnoid hemorrhage is crucial because the rehemorrhage rate of ruptured aneurysms is high and rehemorrhage/ Effaced sulci White matter “buckling” Thick cortical “mantle” Acute Crescent shaped, concave toward brain Tapering edges Crosses suture lines May extend into interhemispheric fissure Fracture may or may not be present Hyperdense, may /


Quanta2 Pipeline: A Training Tutorial Imaging of Dementia and Aging (IDeA) Laboratory, UC Davis School of Medicine: Neurology Amy Liu.

TCV Tracer: Example Continue tracing successive slices, staying along the interior edge of the dura mater and removing the structures labeled with green arrows/ intensities of the yellow Original TCV line. The fluctuation changes could be detected by lowering the Estimation Sensitivity (from default 25) at the start of/ to proceed to next stage of the pipeline. WMHI MODELING This section of the tutorial will detail sequential modeling of White Matter Hyperintensities (WMHI).  Quanta2 will calculate intensity/


Lemon Tutorial Lemon Overview Miroslav Siket, Dennis Waldron CERN-IT/FIO-FD.

Oracle 9i+ (with alarms system on 10g) validation of metric samples, metadata information Flat files based – FlatMon (edg-fmon-server) uses OS files for storing data for smaller sites (scalable to 1000 machines max.) General features: multithreaded/configuration option MinOccurs –MinOccurs gives an exception a level of tolerance, a delay factor between detecting a problem and raising an alarm Lemon Tutorial Quattor and Non-Quattor Configuration of the lemon-agent Miroslav Siket, Dennis Waldron http://cern.ch/


Inference Algorithms: A Tutorial Yuanlu Xu, SYSU, China 2013.3.20.

is just as difficult as integration, what’s the point of all this Monte Carlo stuff? This entire tutorial is about the following idea: Take samples from some simpler distribution and turn them into samples from the complicated/parameters derived from data: compute these before MCMC starts Cue Particles: Clustering in Model Space K-partition Particles: Edge Detection Particles Encode Probabilities Parzen Window Style From Slides by Tomasz Malisiewicz - Advanced Perception Cue Particles In Action Clustering in/


1 FEM Framework Tutorial Sayantan Chakravorty 10/19/2004.

Tutorial Sayantan Chakravorty 10/19/2004 2 Roadmap Why use FEM? FEM Concepts FEM Program Structure FEM Basic Calls FEM Advanced Calls Extra Features 3 Why use FEM? 4 Why use the FEM Framework? Makes parallelizing a serial code faster and easier Handles mesh partitioning Handles communication Handles load balancing (via Charm) Allows extra features NetFEM Visualizer Collision Detection/,tri2node); 0 1 2 36 Ghosts: Edge adjacency /* Edge-adjacency: triangles have 3 edges */ FEM_Add_ghost_layer(2,0); /* 2 nodes/


Copyright © 2004 OPNET Technologies, Inc. Confidential, not for distribution to third parties. Introduction to QoS Mechanisms Networking Tutorials Session.

levels of performance in terms of loss, delay, and throughput. This tutorial provides a theoretical overview of the basic functional blocks used to provision QoS/ to third parties. 28 1806 Introduction to QoS Mechanisms DiffServ Components  At the edge of DiffServ domain  Classification and Marking  Policing/Shaping  Within the core of/to Network Interface PPDPartial Packet Discard PQPriority Queueing REDRandom Early Detection RSVP Resource Reservation Protocol SLAService Level Agreement ToSType of /


Biclustering Tutorial: Gene-expression data Aaditya V Rangan, NYU Trying to find structure within a M-x-N Gene-expression data matrix In this tutorial.

matrix In this tutorial we’ll slowly walk through a biclustering analysis of a particular gene-expression data set. The biclustering method we will use is based on the simple ‘loop-counting’ algorithm proposed in (Rangan, A simple filter for detecting low-rank submatrices./biclusters from the previous slide; dots are joined with a purple edge if they share many rows in common (i.e., if their patient-subsets overlap), and dots are joined with an orange edge if they share many columns (i.e., if their gene-/


A shallow look at Deep Learning Computer Vision James Hays Many slides from CVPR 2014 Deep Learning Tutorial (Honglak Lee and Marc’Aurelio especially)

look at Deep Learning Computer Vision James Hays Many slides from CVPR 2014 Deep Learning Tutorial (Honglak Lee and Marc’Aurelio especially) and Rob Fergus https://sites.google.com/site/ is going on inside deep convolutional networks http://www.cc.gatech.edu/~zk15/DL2016/deep_learning_course.html Image Low-level vision features (edges, SIFT, HOG, etc.) Object detection / classification Input data (pixels) Learning Algorithm (e.g., SVM) feature representation (hand-crafted) Features are not learned /


#1 of 10 Tutorial Introduction PURPOSE -To explain how to configure and use the Timer Interface Module in common applications OBJECTIVES: -Identify the.

9 of 10 Timer Interface Module Input Capture Overview 16-BIT FREE-RUNNING COUNTER 16-BIT INPUT CAPTURE LATCH EDGE SELECT & DETECT ICx Latch Request Interrupt Rising Edges (05 & 08) Falling Edges (05 & 08) Any Edge (08 Only) Status Flag Interrupt Enable #10 of 10 Timer Interface Module Input Capture Applications Perform time /in buffered PWMs? Click on your choice. a)Yes b) No c) Does not matter #26 of 10 Tutorial Completion -TIM Configuration -Output Compare -Input Capture -Unbuffered PWM -Buffered PWM


NSF/SRC Engineering Research Center for Environmentally Benign Semiconductor Manufacturing Philipossian 1 Tutorial on Chemical Mechanical Polishing (CMP)

Center for Environmentally Benign Semiconductor Manufacturing Philipossian 3 Outline of the Tutorial Section E: Industry - University Gaps Section F: Environmental Health and/for Environmentally Benign Semiconductor Manufacturing Philipossian 14 In-situ Measurement: –End-point detection Consumables: Pad (polyurethane, impregnated felt, fixed abrasive) Slurry (silica, alumina/Advantages: –Most common cleaning methodology –Double-side and edge cleaning capability –High energy scrub capability –The contact /


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