Object Recognition + Gesture Recognition
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1 Object Recognition + Gesture Recognition Matt Loper CS148 Nov 1st, 2007
2 Motivation Consider the robot control loop Compare it to a human Decision Making Actuators World Perception Sensors
3 Motivation Consider the robot control loop Compare it to a human Decision Making B Actuators World Perception Sensors
4 Motivation Consider the robot control loop Compare it to a human Decision Making B Actuators A+ World Perception Sensors
5 Motivation Consider the robot control loop Compare it to a human Decision Making B Actuators A+ World Perception Sensors B+
6 Motivation Consider the robot control loop Compare it to a human Decision Making B Actuators A+ World Perception D Sensors B+
7 Motivation Consider the robot control loop Compare it to a human Decision Making D- B Actuators A+ World Perception D Sensors B+
8 Motivation Consider the robot control loop Compare it to a human Decision Making D- B Actuators A+ World Perception Sensors B+
9 Motivation Consider the robot control loop Compare it to a human Perception Localization People Perception Detection Object Recognition Action Recognition Others...
10 Motivation Consider the robot control loop Compare it to a human Perception Localization People Detection Object Recognition Action Recognition Others...
11 Object Recognition What is object recognition?
12 Object Recognition Two philosophies Model based Object Centric Top Down Template based View Centric Bottom up
13 Object Recognition Two philosophies Goal: Find the horse in the picture Model based Object Centric Top Down Template based View Centric Bottom up
14 Object Recognition Two philosophies Goal: Find the horse in the picture Model based Object Centric Top Down I have a geometric model of the horse. Template based View Centric Bottom up I have pictures of parts (hooves etc) of the horse.
15 Object Recognition Two philosophies Goal: Find the horse in the picture Model based Object Centric Top Down I have a geometric model of the horse. Template based View Centric Bottom up I have pictures of parts (hooves etc) of the horse.
16 Object Recognition Two philosophies Goal: Find the horse in the picture Model based Object Centric Top Down I have a geometric model of the horse. Template based View Centric Bottom up I have pictures of parts (hooves etc) of the horse.
17 Object Recognition Two steps to O.R. In theory, only two things to do: 1. Collect features 2.Perform classification, using those features
18 Object Recognition Two steps to O.R. In theory, only two things to do: 1. Collect features Tool: image processing 2.Perform classification, using those features Tool: machine learning approach
19 Object Recognition Feature Collection Image processing brings us closer... Corners Edges Blobs But now we must formalize these into features
20 Object Recognition Feature Collection Corners Edges Blobs
21 Object Recognition Feature Collection Corners Edges Blobs x y local brightness begin x,y end x,y curve polynomial mean blob x,y color distribution
22 Object Recognition Feature Limitations General limitations Rotational invariance Scale invariance Specific feature limitations Corners: Many objects are soft Edges: Aperture problem Blobs: subject to lighting changes, surface reflectance
23 Object Recognition SIFT Introduced in 1999 by David Lowe (S)cale (I)nvariant (F)eature (T)ransform Galvanized the field Three attributes 1. Scale invariance 2.Rotation invariance 3.Corner detection
24 Object Recognition > SIFT Scale Invariance How do you look for a variable sized object? Looking for a needle in a haystack... Needle Haystack
25 Object Recognition > SIFT Scale Invariance How do you look for a variable sized object? Looking for a needle in a haystack... Needle Haystack Match!
26 Object Recognition > SIFT Rotation Invariance What if the object is rotated? Needle Haystack
27 Object Recognition > SIFT Rotation Invariance What if the object is rotated? Needle Haystack Rotate to canonical orientation before comparison...
28 Object Recognition > SIFT Corner detection Corner detection: misnomer Finds locally unique regions What is NOT a corner?
29 Object Recognition > SIFT Corner detection Corner detection: misnomer Finds locally unique regions What is NOT a corner? No... Yes Yes Nope
30 Object Recognition > SIFT Background: corner detection Think of an image as a mountainous landscape White is high, black is low
31 Object Recognition > SIFT Visualizing gradients Images can be seen as heightmaps Where are corners?
32 Object Recognition > SIFT Visualizing gradients Images can be seen as heightmaps Where are corners? Yes No... No...
33 Object Recognition > SIFT Multiscale corners Different corners at different scales
34 Object Recognition > SIFT Multiscale corners Different corners at different scales
35 Object Recognition > SIFT Step 1 of 3: Get features In training image (needle) Find corners in different scales For each corner... Find local image patch Rotate it to canonical orientation In testing image (haystack) Do the same thing!!
36 Object Recognition > SIFT Step 2 of 3: match features What do we have now? Corner features in training Corner features in testing Now we just have to match them up We have M x N comparisons M is # training corners, N is # testing corners Compare image regions
37 Object Recognition > SIFT Step 3 of 3: match objects Find collections of matches that make sense Training images show spatial relationships Testing images should retain those for objects found
38 Object Recognition > SIFT SIFT corners found
39 Object Recognition > SIFT Applications: Blackjack Train dog on each card Look for features that match known spatial arrangement
40 Object Recognition > SIFT Applications: Panoramas AutoStitch (google it, it s fun and easy)
41 Object Recognition > SIFT SIFT at home SIFT 11/01/ :54 PM Home SIFT An open implementation of SIFT Research Publications Code VLFeat Bag of features MDoc Autorights VLPov Anaview SIFT++ SIFT Custom keypoints MSER VLUtil Code Snippets Restricted This is a MATLAB/C implementation of SIFT detector and descriptor. It is fairily customizable and features a decomposition of the algorithm in several reusable M and MEX files. This implementation produces interest points and descriptors which are very similar to David Lowe's implementation. Remark. This code is well suited to study, understand and modify SIFT, but it is not particularly fast. If you need to compute lots of features, you might be interested in this lightweight C++ version, which does not require MATLAB and comes with a flexible command line interface. Copyright This software program is Copyright 2006 The Regents of the University of California and can be freely used for academic purposes (see the included license file for details). Although this implementation is original (in particular, it is not derived from Lowe's implementation), the SIFT algorithm has been issued a patent. Thus you should note that:
42 Object Recognition > SIFT SIFT Drawbacks Rotation If object rotates around camera s z-axis, that s ok But if object rotates around other axes, that s a potential problem SIFT tolerates only 15% to 20% rotation Can be worked around by generating features at other rotations Floppy objects don t generate reproducible features
43 Object Recognition > SIFT Object Recognition Summary Why: because current robot perception is not good What: feature selection+classification How: SIFT (or Viola & Jones, or color-based segmentation)
44 Mobile Action Recognition Motivation Automation: for the 3 d s dirty dangerous dull Great! What s done already?
45 Mobile Action Recognition > Motivation What s automated today? Today s automation ATM s Automatic/assisted assembly cars electronics packaging Vending What is in common?
46 Mobile Action Recognition > Motivation Motivation Today s automation: ATM s, assembly, vending, automated phone assistance What s in common? They are immobile Communicating with them is unnatural Many dull, dangerous tasks need mobility!...and can benefit from natural communication!
47 Mobile Action Recognition > Motivation Immobility: why? Why don t robots truck around on thayer? Decision Making Actuators World Perception Sensors
48 Mobile Action Recognition > Motivation Immobility: why? Why don t robots truck around on thayer? Decision Making Actuators World Perception D Sensors
49 Mobile Action Recognition > Motivation Immobility: why? Danger to us, and/or themselves Can t see things or people
50 Mobile Action Recognition > Motivation Natural communication Hard to communicate when you can t see people Speech recognition works alright when trained on one person, or on simple words Hard to hear when it s noisy, different accents are being used, etc I will focus on Action Recog.
51 Mobile Action Recognition > Tactical Teams Goals Goals Person detection Gesture-based communication A 3d view of the world Means Improve sensors Decide Actuate World Improve perception Perceive Sense
52 Mobile Action Recognition > Tactical Teams Sensors S CSEM SwissRanger Emits nonvisible light Recovers a depth image Expensive
53 Mobile Action Recognition > Tactical Teams Perception P Hidden Markov Model Gesture 2 No Gesture Gesture 1
54 Mobile Action Recognition > Tactical Teams Perception P Hidden Markov Model Gesture 2 No Gesture 25% Gesture 1 50% 25%
55 Mobile Action Recognition > Tactical Teams Perception P Hidden Markov Model Gesture 2 No Gesture Gesture 1 25% 25% 50%
56 Mobile Action Recognition > Tactical Teams Getting features P I use a cylindrical body model (ok, not this one) And I use guess and check, aka Bayesian reasoning Gives me poses, which give me features
57 Mobile Action Recognition > Tactical Teams Demo
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