Using Viola-Jones algorithmwith two-point features

for searching for man-made objects in the grass

Kornilov F.A., Kostousov K.V., Perevalov D.S.

Institute of Mathematics and Mechanics, Ural Branch of RAS, Ekaterinburg

TZSU-2011http://tvcs2011.technicalvision.ru

1. ProblemRequired to find the images of objects of interest – “camomile-like” objects.

Objects of interest are partially occluded, and are located on a complex

background - the grass.

Object of interest Object of interest in the grass

2. Available methods1. A special algorithm

2. Neural networks

3. Geometric matching

4. Viola-Jones algorithm

5. LBP (Local Binary Pattern)

6. HOG (Histogram of gradients)

7. Generalized Hough transform

8. Bayesian model (Markov random field)

2. Ways to address1. A special algorithm (???)

2. Neural networks (???)

3. Geometric comparison ("as separate circuits)

4. Viola-Jones algorithm (? How it works with occlutions)

5. LBP

6. HOG

7. Generalized Hough transform

8. Bayesian model (? "Slow")

3. Viola-Jones algorithmToday it is one of the most often used algorithms to quickly find the front faces.

Algorithm is training on the base of labeled examples.

At the core of work is a mix of Haar-like features used in cascades of classifiers,

constructed by boosting.

The algorithm is "universal" – potentially it can be trained to recognize not only

faces, but any other class of objects which have “almost” fixed shape.

4. Viola-Jones algorithmin the case of partial obstruction

In Barczak, Andre L.C. «Evaluation of a Boosted Cascade of Haar-Like Features in the Presence of Partial Occlusions and Shadows for Real Time Face Detection» (2004) shown that Viola-Jones algorithm works good with occluded faces, if training was made on a partially occludedexamples.

5. "Camomile" - is not a faceThe outer contour of the face and its internal features (eyes, nose, mouth) are

almost convex.

“Camomile" – has a non-convex outer contour, and has no significant internal

features.

So it makes sense to investigate the issue of search for “camomiles" some

other non-Haar features.

6. Two-point featuresConsider a set of features, which consists of the difference in

brightness of two pixels (x1,y

1) and (x

2,y

2)

for all possible pairs of pixels:

feature(x1,y1,x2,y2 ) = image(x1,y1) - image(x2,y2).

This set of features are significantly nonlocal.

This feature set is already used in several other works.

For example, in work on the recognition of gender with the faces:

Baluja, S., Rowley, H.A., Boosting sex identification performance.

Internat. J. Comput. Vision 71 (1), 111-119, 2007.

7. Which feature set is better?

Haar features Two-point features

or

7. Which feature set is better?

Training on 600 images

(«+»: 16x16 pixels, a small rotation of -2 .. 2 degrees;

«-»: 128x128 pixels).

Testing on 2000 of synthesized images (300x300 pixels, a several passes with

the rotation of images, by 5 degrees).

Synthesis of images: a layer of grass, an

object circles the grass and structural

noise (twigs and leaves), Gaussian

noise.

Examples of positive examples for

training

(Blue channel, 16x16 pixels).

7. Which feature set is better?

Correct detection

False alarm

Haar features 84.2% 4.9%

Two-pointfeatures

85.7% 7.3%

Output

Performance of both sets of features are similar,

just the two-point set is more sensitive.

8. Examples

Haar feature set Two-point set of features

8. Examples

Haar feature set Two-point set of features

8. Examples

Haar feature set Two-point set of features

8. Further research

1. What is performance of two-point features on frontal

faces?

2. How 3-and 4-point features will work?

perevalovds @ gmail.com

Thank you for your attention