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The document summarizes a wearable, real-time mobile gaze tracker called iShadow. It uses an eye-facing and world-facing camera along with an artificial neural network trained on data from 10 subjects to predict gaze. Key results found it had around 3 degrees of error. It aims to be a low-power, real-time gaze tracker but has limitations from user variability, glasses placement, and distinguishing gaze versus head motion.
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Eye Gaze Tracking With a Web Camera in a Desktop Environment
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
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1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
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Email id: 1croreprojects@gmail.com
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No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
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X36141145
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20110220 computer vision_eruhimov_lecture01
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Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images.
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Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
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Interactive full body motion capture using infrared sensor network
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
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This study examined the within-day temporal relationships between smoking frequency, craving, and withdrawal symptoms using data from 351 participants over 16 days. Lagged regression analyses showed that increases in craving temporarily preceded smoking episodes, and that smoking was associated with subsequent decreases in craving. Negative affect was also found to have a one-way relationship with smoking, such that smoking was associated with lower negative affect, but negative affect did not predict smoking episodes. This was the first study to examine these within-day temporal patterns between smoking and withdrawal symptoms.
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Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as
cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult
problem, especially in case of multiple moving objects. Object detection in the presence of camera noise
and with variable or unfavourable luminance conditions is still an active area of research. This paper
propose a framework which can effectively detect the moving objects and track them despite of occlusion
and a priori knowledge of objects in the scene. The segmentation step uses a robust threshold decision
algorithm which uses a multi-background model. The video object tracking is able to track multiple objects
along with their trajectories based on Continuous Energy Minimization. In this work, an effective
formulation of multi-target tracking as minimization of a continuous energy is combined with multibackground
registration. Apart from the recent approaches, it focus on making use of an energy that
corresponds to a more complete representation of the problem, rather than one that is amenable to global
optimization. Besides the image evidence, the energy function considers physical constraints, such as target
dynamics, mutual exclusion, and track persistence. The proposed tracking framework is able to track
multiple objects despite of occlusions under dynamic background conditions.
Augmented reality in spine surgery
1. Augmented reality (AR) supplements reality by combining virtual elements with the real world in real-time, unlike virtual reality which completely replaces the real world.
2. AR has applications in medical surgery by allowing surgeons to see "X-ray vision" through augmented reality head-mounted displays to help with visualization and navigation during minimally invasive surgeries.
3. Accurate tracking and registration of virtual elements with the real world is challenging in AR. Hybrid tracking systems that combine approaches may help cover weaknesses and allow for greater flexibility.
X36141145
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Effective Object Detection and Background Subtraction by using M.O.I
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Presentation slides from my talk on eye tracking and gaze-contingency for Virtual Reality applications.
In this talk I present the Eye Tracking Head-mounted Display proposed in the paper "An Affordable Solution for Binocular Eye Tracking and Calibration in Head-mounted Displays".
The paper won the "Best Student Paper Award" at ACM Multimedia 2015 in Brisbane, Australia.
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20110220 computer vision_eruhimov_lecture01
1) Computer vision techniques such as stereo vision and object detection can be used for applications in robotics.
2) Stereo vision involves calculating depth from two images using techniques like block matching and disparity maps. It can be implemented efficiently on GPUs.
3) Object detection methods include feature detection using SIFT or SURF descriptors followed by matching and geometry validation using techniques like RANSAC. These allow objects to be detected and their pose estimated.
11.0003www.iiste.org call for paper.survey on wireless intelligent video surv...
This document summarizes a survey on wireless intelligent video surveillance systems using moving object recognition technology. It describes the typical components and processes involved, including capturing video, creating a background template, detecting moving objects via background subtraction, and sending the separated moving object to a destination. It also discusses techniques for motion detection, such as frame differencing, background subtraction, and optical flow. The system architecture involves a camera capturing video, subtracting frames to identify moving objects, and transmitting just the moving objects.
3.survey on wireless intelligent video surveillance system using moving objec...
This document summarizes a survey on wireless intelligent video surveillance systems using moving object recognition technology. It describes the typical components and processing steps of such systems, including motion detection using background subtraction, object tracking, and transmitting detected objects wirelessly. The document also reviews the evolution of video surveillance technologies from analog CCTV to modern wireless intelligent systems and discusses design considerations like video quality and compression.
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Linkedin
The document summarizes the development and validation of a hyperspectral imaging microscopy system called Xanoscope for pathology applications. Key points:
1) Xanoscope allows for fast, automated acquisition of hyperspectral images over contiguous wavelength bands with high spatial resolution, reducing scan times significantly compared to previous methods.
2) Validation experiments showed Xanoscope produces quantitative measurements and improves signal-to-noise ratio through optimized camera settings and image accumulation.
3) Xanoscope was used to scan 10 multiplexed fluorophores in cell lines, and linear unmixing was able to measure the individual contribution of each fluorophore at pixel-level for advanced pathological analysis.
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This document discusses generating three-dimensional panoramic images from a stereo pair of panoramic views of a scene. It begins by introducing binocular vision and how humans perceive depth and 3D effects. It then discusses various methods for generating synthetic 3D effects, including anaglyph techniques which superimpose stereo image pairs. The document outlines methods for capturing stereo image pairs, computing disparity maps, and generating color and grayscale anaglyphs. It proposes using these stereoscopic techniques to create panoramic images with 3D depth perception for applications like panoramic television.
VIRTUAL VIEWPOINT THREE-DIMENSIONAL PANORAMA
This document discusses generating a three-dimensional panoramic image from a stereo pair of panoramic views of a scene. It begins by introducing binocular vision and how humans perceive depth and 3D using two eyes. It then discusses various methods for generating synthetic 3D effects, including anaglyph images which superimpose stereo image pairs in different colors viewed through colored glasses. The document outlines the process for generating anaglyph images from stereo panoramic views, including computing image disparity and depth from camera parameters. It concludes that generating a 3D panoramic image could enable new applications like panoramic television with depth perception.
VIRTUAL VIEWPOINT THREE-DIMENSIONAL PANORAMA
Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images.
VIRTUAL VIEWPOINT THREE-DIMENSIONAL PANORAMA
Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images.
Interactive full body motion capture using infrared sensor network
Traditional motion capture (mocap) has been
well
-
stud
ied in visual science for
the last decades
. However
the fie
ld is mostly about capturing
precise animation to be used in
specific
application
s
after
intensive
post
processing such as studying biomechanics or rigging models in movies. These data set
s are normally
captured in complex laboratory environments with
sophisticated
equipment thus making motion capture a
field that is mostly exclusive to professional animators.
In
addition
, obtrusive sensors must be attached to
actors and calibrated within t
he capturing system, resulting in limited and unnatural motion.
In recent year
the rise of computer vision and interactive entertainment opened the gate for a different type of motion
capture which focuses on producing
optical
marker
less
or mechanical sens
orless
motion capture.
Furtherm
ore a wide array of low
-
cost
device are released that are easy to use
for less mission critical
applications
.
This paper
describe
s
a new technique of using multiple infrared devices to process data from
multiple infrared sensors to enhance the flexibility and accuracy of the markerless mocap
using commodity
devices such as Kinect
. The method involves analyzing each individual sensor
data, decompose and rebuild
them into a uniformed skeleton across all sensors. We then assign criteria to define the confidence level of
captured signal from
sensor. Each sensor operates on its own process and communicates through MPI.
Our method emphasize
s on the need of minimum calculation overhead for better real time performance
while being able to maintain good scalability
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Presentation yamin
- 1. iShadow: Design of a wearable, real-time mobile gaze tracker Presenter: Yamin Tun Addison Mayberry, Pan Hu, Benjamin Marlin, Christopher Salthouse, Deepak Ganesan (UMass)
- 2. Introduction
- 3. Gaze Tracking Criteria Efficient (power, memory) Fast Accurate
- 7. Methodology: Prediction Model Artificial Neural Network (ANN): Sparse sampling Learn best mask of a typical eye image Predict Gaze from subsampled pixels
- 9. Data Collection, Processing 10 subjects ~3000 images at 10 Hz fps (~5 mins)
- 10. Key Results ~3 degree of error
- 11. Summary/Contribution Low-power Real-time Gaze Tracker
- 12. Limitations/Critique Varying performance for different users Different depth of view for different faces Visibility of the entire eye Glasses placement Obstructing user’s view Imager + motion sensor Gaze on same object with head movement
Editor's Notes
- What is Gaze Tracking? Gaze Tracking is measuring gaze points- estimating where a person is looking at in the physical world using eye movement information Applications- Continuous real-time gaze tracking can be valuable/useful in a lot of applications such as detection of unsafe behavior- such as tracking driver’s gaze and detect whether driver has attention on the road- prevent accidents- pedestrian and cars Detecting psychological behaviors- Pupil dilation- excitement Pupil micromovements-detect fatigue Power hungry, computation intensive device -> bulky wearable devices
- Easy Comp Vision problem- detect circle or the darkest patches- probably using some powerful MCU -real-time -power - Google glasses- power consuming, as a wearable device- not realistic to run out of battery all the time - but to do it in real-time with limited computation power- challenge
- -right now, glasses use eye-facing camera at the back
- STM32 Arm cortex- MCU Stonyman- grayscale imager Storage
- How the eyeglasses work? -feed in Eye images from back camera-> Output=gaze estimate -Neural network to estimate gaze ->to build it, offline-training ~3 degree of error on average= gaze estimate error