Hci presentation v2.2

CSE Template

Touchless, touch-based and Augmented Reality-based interactions with bacterial biofilm images Mohammadreza Hosseini, Arcot Sowmyamhosseini, [email protected] Bednarz [email protected]

Click to edit Presents Name

1

IntroductionImage review, manipulation within sterile environments, maintaining boundaries between sterile and non-sterile areas of work environment, are essential in biology studiesRemote control and visualization of biomedical images can reduce direct exposure of researchers to viruses and bacteria Investigation on human user interfaces, via touchless touch- based and Augmented Reality interfacesAlso study their usability through series of user experiments

School of Computer Science and EngineeringPage #

2

Touchless system design and image visualization





Touch based system design



User Experience Design 10 participants selected randomly from among scientists and employees in CSIRO to participate in the experiments People from different backgrounds, nationalities and gendersAt the beginning of the experiments every participant introduced individually to the system by an expert. They could observe the expert interacting with two systems They get enough time to fill the survey formsThey are also requested to indicate which interface felt more natural


Survey form1: SUSSystem usability scale (SUS) is a ten-item Linkert scale) with a weighted scoring range of 0-100, giving a global view of system usability.To calculate the SUS score, the score contributions from each item are summed. Each item score contribution will range from 0 to 4. For items 1, 3, 5, 7, and 9, the score contribution is the scale position minus 1. For items 2, 4, 6, 8 and 10, the contribution is five minus the scale position. Multipling the sum of the scores by 2.5 provides the overall value of SUS.The scores are then converted to a percentile rank using a process called normalization. The SUS score percentile rank is usually referred as school grade scale



Survey form2: Self assessment manikinSelf Assessment Manikin (SAM) is a graphical figure to measure feelings of pleasure, arousal and dominanceSAM displays each dimension with a graphical character array along a continuous nine-point centre movement scale For pleasure, SAM shows characters from smiley happy faces to unhappy and sad faces For arousal, SAM displays figures that are very excited with eye open down to sleepy and bored facesFor dominance, a very large figure presenting feelings of strength and being in control, and a small figure showing the feeling of being controlled or submissive




majority of user initiate touch based interaction as an excellent (A) choice for interaction


all participants gave higher scores to touch-based system in comparison to tuchless interaction systems.



people feel happier and more in control when they are interacting with a touchbased interactive system, but the excitement of using touchless is higher


ConclusionParticipant observations during user experiments reveal that moving the entire hand is not an ideal way to communicate with the system. The feeling of tiredness that users experience when using hand gesture explain why the touchless system is less pleasurable compared with a touch-based system.working with large images where information content is very high is the major cause of tiredness.the feeling of not being in control of the touchless interface is due to the delay between the hand movement and pointer positioning on the screen. Every participant also thought that an iPad is a more natural screen than a 3-meter hemispherical dome.


Augmented reality as a new form of interactionTo benefit from the capacities of both touchless and touch-based unimodal interaction. AR is introducedAR interaction can enhance understanding of physical objects by addition of digital information to captured video streams.


Tracking in AR Major challenge for every AR application is trackingTracking is about locating position of an object in the video frame and aligning the virtual information with the user field of viewTo detect the users field of view, it is assumed that the handheld device camera direction is the same as the user field of view By detecting features in images received through the camera and matching them with images stored in database, the field of view can be computed


System Design


Proposed Tracking

Feature matching between image from camera and stored image in database is used to find the corresponding tapped bacterium in the image stored in databaseCorresponding bacterium


Information Retrieval

Information from the bacteria in database is extracted and translated back to the position of corresponding bacterium on the image from cameraCorresponding bacterium


Dimension: Length, WidthThe selected bacterium highlighted and information from database displayed on the screen of handheld device



Feature matching for tracking: Comparing different feature detectors and descriptors on accuracy and real-time performanceFeature DetectorFeature DescriptorFast : Using a window of 16 Pixels to classify whether a pixel is actually a cornerBrief : A simple comparison of pixel pairs around feature points.ORB : A FAST or Harris detector ORB: A BRIEF descriptor with some hints about the key point orientation SIFT : Using Scale-Space Pyramid and DoG to detect feature pointsSIFT: A 128 vector of orientation histogram of pixels around the feature pointSURF: Using a simplified version of Laplacian of GaussianSURF: Wavelet response in horizontal and vertical direction


Real-time PerformanceDifferent combination of feature detector and descriptor used for designing the tracking algorithm.The application runs for 30 seconds and frame frame rates was recorded for each different combination.


Real-Time PerformanceFast BriefFast SiftFast SurfOrb ORBSIFT SIFTSURF SURF


Real-Time performanceDetectorDescriptorFrame rateFASTBRIEF30FASTSIFT2-5FASTSURF4ORBORB8-10SIFTSIFT1SURFSURF1

WINNER


AccuracyThe application accuracy is estimated by measuring the acceptable range of device rotation.The acceptable range is the maximum rotation in every direction before the application loses the bacterium position between two consecutive tapsThis is carried out by comparing the positions extracted from inverse homography of different matching methods with results from SURF matching inverse homography method in different device orientations. The reason for selecting SURF as the base model is because of its rotation and scale invariant properties.


AccuracyFeature DetectorFeature DescriptorAccuracyFASTBRIEF15 degree in each directionFASTSIFT45 degree in each directionFASTSURF90 degree in each directionORBORB85 degree in each direction

WINNER


ConclusionThe AR application can run in 30 frame per second using FAST/BRIEF feature detector and descriptorThe FAST/BRIEF combination has limited acceptable device rotation, which drop usability of the application. There is a trade-off between accuracy and real-time performance for AR application in high-dense environment Further research is necessary for developing more accurate feature with real-time capability


Pause the movieControl the pointerExtract and zoomed in Changing video frameResume video play

Pause the movieControl the pointerZooming in and out

Strongly DisagreeStrongly Agree

1I am familiar with this kind of system

12345

2I found the system is complex

12345

3I thought the system was easy to use

12345

4I think that I would need some help to be able to use this system

12345

5I found the various functions in this system were well integrated

12345

6I thought there was too much inconsistency in this system

12345

7I imagine that most people would learn to use this system very quickly

12345

8I found the system very cumbersome to use

12345

9I felt very confident using the system

12345

10I needed to learn a lot of things before using this system

12345

11Which screen felt more naturaldomeiPad

12Other Comments and recommendations:

Hci presentation v2.2

Technology

Transcript of Hci presentation v2.2