US Patent Patent # 9.317.779 was awarded on 04/19/2016.

    Object recognition is a well studied but extremely challenging  field. We developed a novel approach to feature construction for object detection called Evolution-COnstructed Features (ECO features). Most current approaches rely on human experts to construct features for object recognition. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, no limitations to certain types of image sources, and ability to find both global and local feature types. We show in our experiments that the ECO features compete well against state-of-the-art object recognition algorithms.

Graduate Students:

Kirt Lillywhite, Beau Tippetts, Meng Zhang

1. M. Zhang and D.J. Lee, “Global ECO-Feature for Object Classification,” Lecture Notes in Computer Science (LNCS), International Symposium on Visual Computing (ISVC), Part II, LNCS 10073, pp. 281–290, Las Vegas, NV, U.S.A, December 12-14, 2016. 
2. M. Zhang and D.J. Lee, “Efficient Training of Evolution-COnstructed Features,” Lecture Notes in Computer Science (LNCS), International Symposium on Visual Computing (ISVC), Part II, LNCS 9475, p. 646-654, Las Vegas, NV, U.S.A., December 14-16, 2015.   
3. K.D. Lillywhite, D.J. Lee, B.J. Tippetts, and J.K Archibald, “A Feature Construction Method for General Object Recognition,” Pattern Recognition, vol. 46/12, p. 3300-3314, December 2013.
4. K.D. Lillywhite, B.J. Tippetts, and D.J. Lee, “Self-Tuned Evolution-COnstructed Features for General Object Recognition,” Pattern Recognition, vol. 45/1, p. 241-251, January 2012.
5. K.D. Lillywhite, D.J. Lee, and B.J. Tippetts, “Improving Evolution-COnstructed Features Using Speciation,” IEEE Workshop on Applications of Computer Vision (WACV), 6 pages, Breckenridge, Colorado, USA, January 9-11, 2012.