1. Assistive System Progress Report 1
Bishwo Gurung
Shifali Kumar
James Chou

2. Overview of the System
Objective: To help individuals who are visually impaired and provide them independence.
Object recognition system
SIFT algorithm
Speech recognition feature
MATLAB (primary language and interactive environment)

3. SIFT - Scale-invariant feature transform
Scale-space extrema detection
Keypoint localization
Orientation assignment
Keypoint descriptor

4. The First Steps of Feature Detection
Begin by constructing a scale space
Replicate a natural concept of perception in digital imaging
The scale space represents the image in multiple sizes to produce invariance.

5. Applying Gaussian Blur
Intentionally eliminate unwanted detail from an image (such as textures or artifacts)
Removes detail without introducing false ones by smoothing the image
An important step in extracting keypoints

7. Combining with Octaves
An octave contains a set of one specific image in the same scale but progressively blurs over each one.
The next octave has the levels of blurring but is scaled half the size of the previous octave.
This step is repeated over and over.

8. David Lowe’s ideal numbers (4 octaves, 5 blur levels)

9. Gaussian Blur Expression
σ - scale parameter

10. Generate Difference of Gaussians
new set of images for finding interest points.

11. Gaussian Filter blur_1 = imgaussfilt(I, sigma);
blur_2 = imgaussfilt(blur_1, sigma);
blur_3 = imgaussfilt(blur_2, sigma);
blur_4 = imgaussfilt(blur_3, sigma);

12. Difference of Gaussians(Simplified)
DoG_1 = I-blur_1;
subplot(2,2,1); imshow(DoG_1)
DoG_2 = blur_1-blur_2;
subplot(2,2,2); imshow(DoG_1)
DoG_3 = blur_2-blur_3;
subplot(2,2,3); imshow(DoG_1)
DoG_4 = blur_3-blur_4;
subplot(2,2,4); imshow(DoG_1)

13. Sample Image

15. Next Step Key-point localization Orientation assignment
Scale-space extrema detection
Key-point localization
Orientation assignment
Keypoint descriptor
Locate maxima/minima in DoG images

16. Speech Recognition There are 2 types of systems:
isolated (having a brief pause between the words)
continuous (talking fluently)
Analyzing:
speaker independent system (multiple people)
speaker dependent system (single person)

17. Speech Recognition 3 stages main stages speech acquisition
speech analysis
user interface development
We will be using the microphone built in the computer.

18. Speech Acquisition Matlab tool: Simulink library browser
Training (part 1)
This part is being done offline since we are training the system.
First step, is to find a way to input in our data (word acoustic).
Matlab tool: Simulink library browser
Next, we need to know how the computer can differentiate between each of our inputs.
A solution for this is creating our own acoustic model which will convert each sounds into numerical digits so it will be efficient for the system analyze.

19. Speech Acquisition Matlab tool: Data acquisition toolbox
Testing (part 2)
This part will be done will be live.
We will need to pull the saved data and compare it with the new data while processing new speech.
Matlab tool: Data acquisition toolbox
Processing the new speech will be done frame by frame for isolated system.

20. Thank You!