FinalPoster

Jan 21, 2016Download as ppt, pdf

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This document proposes a real-time system for designing and modeling using only a webcam. The system uses probability masks and optical flow to track hand movements in real-time and use them to draw on video chat. It outlines an approach using skin color probability masks, optical flow probability masks, and Lucas-Kanade optical flow to track regions of interest frame-by-frame. Future work includes improving performance, converting 2D drawings to 3D models in real-time, augmenting the 3D model onto the video in real-time, and sharing the model over video chat.

Pratheba Selvaraju
( 4 Homework + 1 Project )
(University of Washington)
Real-Time Designing and Modeling
Conclusions
Idea:
Introduction
Real Time Designing and Modelling:
Everyday engagement in
Conversation over video chat
? It will be useful to present
a design idea on the spot without
Use of any special software.
? Fun to play games where you
Model your own character.
? Show the person in other end
of video conference a new
Idea for a car design.
Approach- Phase I
Probability Mask and Bounding Box:
? Skin-Color-Probability mask
?
?
?
~ 20 frames
? Optical-flow-Probability mask
?
? Region of Interest
Centroid and BoundingBox
Results :
?Due to high number of processing,
frame rate is low, which results in
not a very smooth drawing.
?Very high speed movements
could not be tracked.
? To design withoutadditional software
? No additional hardware
? Must work with lowresolution camera
? Must be real-time withlittle to no-preprocessing
? Use of Hand / FingerTip to draw
and design.
? Use of motion to detect which object
To track for drawing
? Get the drawing to create 3D model
? Augment it to the video
Requirement
System Overview
Use case
Meeting Presentation
Gaming Application
? Face is detected from each frame
? ROI of face region is averaged
? HSV range of skin color is obtained, (bin
size = range)
? Hue Range is Obtained from HSV
.
.
.
Averaged face
HSV range
Haar Cascade classifier
Viola-Jones
(Frontal face)
Hue Mask
References
?Araki, R. ; Waseda Univ., Tokyo, Japan ;
Gohshi, S. ; Ikenaga, T. Real-time both hands
tracking using CAMshift with motion mask and
probability reduction by motion prediction,
Signal & Information Processing Association
Annual Summit and Conference (APSIPA ASC),
2012 Asia-Pacific
?Dorin Comaniciu,, Mean Shift : A Robust
approach towards feature space, IEEE
transactions on Pattern Analysis and Machine
intelligence
?Jean-yves Bouguet, Pyramidal implementation
of the Lucas Kanade feature tracker, Intel
Corporation, Microprocessor Research Labs,
2000.
Previous
Frame
Current
Frame
Grid
Features
Where is Probability mask
and is the x and y flow
vector
Pyramidal Lucas Kanade
Dense Optical
Flow
FlowmaskDense Optical
Flow
Normalize to range
between 0 – 255
= threshold = 255
= threshold = 0
? Optical flow image is smoothed, filtered and dilated
(~ 3 iteration) to remove noise.
? Extract image from FlowMask with range 200-255
? Normalize the extracted image to 0-255
? Obtain Contours and convex hull
Three coin algorithm
? Ability to draw in real-time with
Only webcam (low resolution).
? Can be used to track any moving
With better accuracy than traditional
Camshift.
ContourImage Convex Hull
FlowmaskHue Mask
? Regular camshift uses only the color
probability, which might result in incorrect
tracking due to noise or object with same
color range
? With addition of flow mask, it correctly
identifies the object to be tracked.
? Depending upon speed of object,
threshold level is adjusted.
Bounding box andcentroid used for initial
setting initial region ofinterest for tracking
where, 0 ≤ α ≤ 1 and 0 ≤ β ≤ 1
? When object start moving, camshaft tracks the
object
? Previous and current tracking box positions are
obtained.
? Line is drawn between center of previous and
current box position
Future Tasks:
?Improve performance processing.
?Convert 2D drawing into 3D model
real-time
(either via classification of shapes
or by rendering exact 3D shape of
2D drawing)
?Augment the 3D model in video in
real-time
?Send it via network to be viewed
by other person in chat

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Computer-aided design (CAD) involves using computer systems to assist in the creation, modification, analysis, or optimization of a design. Computer-aided manufacturing (CAM) provides computer support for manufacturing a given product. CAD/CAM integrates design and manufacturing, which were traditionally separate functions, and will ultimately provide the technology base for computer-integrated factories of the future. CAD/CAM software is used to both design and manufacture products through processes like computer numerical control (CNC) machining. Geometric modeling, a key part of CAD/CAM, mathematically describes shapes using methods like wireframe, surface, and solid modeling.

Introduction to CAD/CAMNafis Ahmad

The document outlines an introduction to CAD/CAM, detailing its design process, role of modeling, and various applications in engineering and manufacturing. It discusses the complexities involved, such as those encountered during the development of the Airbus A380, and highlights the importance of concurrent engineering to improve collaboration and efficiency. Additionally, it covers CAD system architecture and includes assignments related to CAD modeling and software comparison.

CIM- 1-120000000-UNIT-1 INTRODUCTION.pptdharma raja`

The document provides an overview of computer-aided design (CAD) and computer-aided manufacturing (CAM). It discusses the reasons for implementing CAD, including increasing productivity and improving quality. It describes the basic CAD modeling techniques of wireframe, solid modeling, and engineering analysis tools. The document also outlines common CAM applications in manufacturing planning, such as computer-aided process planning and computer-assisted NC part programming. Applications in manufacturing control discussed include process monitoring, quality control, and just-in-time production systems.

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Introduction to cadDeepak Antil

Introduction to CAD/CAMNafis Ahmad

CIM- 1-120000000-UNIT-1 INTRODUCTION.pptdharma raja`

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1. Pratheba Selvaraju ( 4 Homework + 1 Project ) (University of Washington) Real-Time Designing and Modeling Conclusions Idea: Introduction Real Time Designing and Modelling: Everyday engagement in Conversation over video chat ? It will be useful to present a design idea on the spot without Use of any special software. ? Fun to play games where you Model your own character. ? Show the person in other end of video conference a new Idea for a car design. Approach- Phase I Probability Mask and Bounding Box: ? Skin-Color-Probability mask ? ? ? ~ 20 frames ? Optical-flow-Probability mask ? ? Region of Interest Centroid and BoundingBox Results : ?Due to high number of processing, frame rate is low, which results in not a very smooth drawing. ?Very high speed movements could not be tracked. ? To design withoutadditional software ? No additional hardware ? Must work with lowresolution camera ? Must be real-time withlittle to no-preprocessing ? Use of Hand / FingerTip to draw and design. ? Use of motion to detect which object To track for drawing ? Get the drawing to create 3D model ? Augment it to the video Requirement System Overview Use case Meeting Presentation Gaming Application ? Face is detected from each frame ? ROI of face region is averaged ? HSV range of skin color is obtained, (bin size = range) ? Hue Range is Obtained from HSV . . . Averaged face HSV range Haar Cascade classifier Viola-Jones (Frontal face) Hue Mask References ?Araki, R. ; Waseda Univ., Tokyo, Japan ; Gohshi, S. ; Ikenaga, T. Real-time both hands tracking using CAMshift with motion mask and probability reduction by motion prediction, Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific ?Dorin Comaniciu,, Mean Shift : A Robust approach towards feature space, IEEE transactions on Pattern Analysis and Machine intelligence ?Jean-yves Bouguet, Pyramidal implementation of the Lucas Kanade feature tracker, Intel Corporation, Microprocessor Research Labs, 2000. Previous Frame Current Frame Grid Features Where is Probability mask and is the x and y flow vector Pyramidal Lucas Kanade Dense Optical Flow FlowmaskDense Optical Flow Normalize to range between 0 – 255 = threshold = 255 = threshold = 0 ? Optical flow image is smoothed, filtered and dilated (~ 3 iteration) to remove noise. ? Extract image from FlowMask with range 200-255 ? Normalize the extracted image to 0-255 ? Obtain Contours and convex hull Three coin algorithm ? Ability to draw in real-time with Only webcam (low resolution). ? Can be used to track any moving With better accuracy than traditional Camshift. ContourImage Convex Hull FlowmaskHue Mask ? Regular camshift uses only the color probability, which might result in incorrect tracking due to noise or object with same color range ? With addition of flow mask, it correctly identifies the object to be tracked. ? Depending upon speed of object, threshold level is adjusted. Bounding box andcentroid used for initial setting initial region ofinterest for tracking where, 0 ≤ α ≤ 1 and 0 ≤ β ≤ 1 ? When object start moving, camshaft tracks the object ? Previous and current tracking box positions are obtained. ? Line is drawn between center of previous and current box position Future Tasks: ?Improve performance processing. ?Convert 2D drawing into 3D model real-time (either via classification of shapes or by rendering exact 3D shape of 2D drawing) ?Augment the 3D model in video in real-time ?Send it via network to be viewed by other person in chat

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