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Mobility Aid For The Blind By Amir Gonnen. The oldest aids Walking Cane Guide dog Problems: – Skills and Training phase – Range – Very little information.

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Presentation on theme: "Mobility Aid For The Blind By Amir Gonnen. The oldest aids Walking Cane Guide dog Problems: – Skills and Training phase – Range – Very little information."— Presentation transcript:

1 Mobility Aid For The Blind By Amir Gonnen

2 The oldest aids Walking Cane Guide dog Problems: – Skills and Training phase – Range – Very little information conveyed

3 A Technological Solution Information about the environment Digital Processing Perceptual input perceived by the blind SensorComputerOutput Device This is where I’m going to focus

4 Recent Developments Soundscapes from “The vOICe” “Stereo vision system for the Visually Impaired”, University of Guelph, Canada The “NavBelt” The “GuideCane”

5 “ The vOICe ” “ The vOICe ”

6 Stereo Vision

7 The “ NavBelt ”

8 The “ GuideCane ”

9 Problems Too much information Missing sub-conscious vision processes – Interpretation of a continuous 2D signal into a set of recognizable objects in a 3D space – Movement velocity and direction

10 Solution Emulating some of the sub- conscious processes in a computer Describe the world as a set of objects in a 3D space

11 Intro to the Simulation

12 Do We Get A Better Perception? The Simulation: – A 3D room with several objects – Each object has its own sound The sound is located at the object – Stereo sound  the direction of the object – Volume  How far is the object The beat scanner – divides the field view into sectors – More accurate perceptual direction

13 More About the Simulation The simulation solves several problems: – How to separate sounds of different objects, so that not all objects will be heard at once ? – How to give a good indication of object’s type, direction and distance at the same time ? Tradeoff between Resolution in Time and in Space.

14 Experiment Let the subject close her eyes and guess which object is visible, its direction and its distance. Results: – Recognition was correct in 80%-90% of the cases. – Notable improvement over time.

15 Experiment Video

16 Conclusion Still a lot of work to be done – Computer vision: how do we recognize objects on 3D space using a raw 2D signal ? – How do we assign sounds to different objects ? – How much information can be perceived using sound in a given time frame ? Real progress could be achieved when we understand and able to emulate the cognitive processes of human vision


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