Template-Based Object Detection through Partial Shape Matching and Boundary Verification
This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.
[1] Y. Amit and A. Kong. Graphical templates for model registration. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 18(3):225-
236, 1996.
[2] N. Ansari and E. J. Delp. Partial shape recognition: A landmark-based
approach. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 12(5):470-483, 1990.
[3] A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object
recognition using low distortion correspondence. In IEEE Conference on
Computer Vision and Pattern Recognition, 2005.
[4] G. Borgefors. Hierarchical chamfer matching: A parametric edge
matching algorithm. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 10(6):849-865, 1988.
[5] D. Clemens and D. Jacobs. Space and time bounds on indexing 3d
models from 2d images. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 13(10):1007-1017, 1991.
[6] P. David and D. DeMenthon. Object recognition in high clutter images
using line features. In International Conference on Computer Vision,
pages 1581-1588, 2005.
[7] P. F. Felzenszwalb. Representation and detection of deformable shapes.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
27(2):208-220, 2005.
[8] C. Harris and M. Stephens. A combined corner and edge detector. In
Proc. Fourth Alvey Vision Conference, pages 147-151, 1988.
[9] F. Jurie and C. Schmid. Scale-invariant shape features for recognition of
object categories. In IEEE Conference on Computer Vision and Pattern
Recognition, pages II-90-96, 2004.
[10] P. D. Kovesi. Matlab functions for computer vision and image analysis.
University of Western Australia. Available from:
http://www.csse.uwa.edu.au/~pk/research/matlabfns/.
[11] D. Lowe. "distinctive image features from scale-invariant keypoints".
International Journal of Computer Vision, 60(2):91-110, 2004.
[12] K. Mikolajczyk, A. Zisserman, and C. Schmid. Shape recognition with
edge-based features. In British Machine Vision Converence, 2003.
[13] C. F. Olson and D. P. Huttenlocher. Automatic target recognition by
matching oriented edge pixels. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 6(1):103-113, 1997.
[14] E. G. M. Petrakis, A. Diplaros, and E. Milios. Matching and retrieval of
distorted and occluded shapes using dynamic programming. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
24(11):1501-1516, 2002.
[15] X. Ren, C. Fowlkes, and J. Malik. Mid-level cues improve boundary
detection. Berkeley Technical Report 05-1382, CSD 2005.
[16] S. Sclaroff and L. Liu. Deformable shape detection and description via
model-based region grouping. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 23(5):475-489, 2001.
[17] S. Wang, T. Kubota, J.M.Siskind, and J.Wang. Salient closed boundary
extraction with ratio contour. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 27(4):546-561, 2005.
[1] Y. Amit and A. Kong. Graphical templates for model registration. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 18(3):225-
236, 1996.
[2] N. Ansari and E. J. Delp. Partial shape recognition: A landmark-based
approach. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 12(5):470-483, 1990.
[3] A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object
recognition using low distortion correspondence. In IEEE Conference on
Computer Vision and Pattern Recognition, 2005.
[4] G. Borgefors. Hierarchical chamfer matching: A parametric edge
matching algorithm. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 10(6):849-865, 1988.
[5] D. Clemens and D. Jacobs. Space and time bounds on indexing 3d
models from 2d images. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 13(10):1007-1017, 1991.
[6] P. David and D. DeMenthon. Object recognition in high clutter images
using line features. In International Conference on Computer Vision,
pages 1581-1588, 2005.
[7] P. F. Felzenszwalb. Representation and detection of deformable shapes.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
27(2):208-220, 2005.
[8] C. Harris and M. Stephens. A combined corner and edge detector. In
Proc. Fourth Alvey Vision Conference, pages 147-151, 1988.
[9] F. Jurie and C. Schmid. Scale-invariant shape features for recognition of
object categories. In IEEE Conference on Computer Vision and Pattern
Recognition, pages II-90-96, 2004.
[10] P. D. Kovesi. Matlab functions for computer vision and image analysis.
University of Western Australia. Available from:
http://www.csse.uwa.edu.au/~pk/research/matlabfns/.
[11] D. Lowe. "distinctive image features from scale-invariant keypoints".
International Journal of Computer Vision, 60(2):91-110, 2004.
[12] K. Mikolajczyk, A. Zisserman, and C. Schmid. Shape recognition with
edge-based features. In British Machine Vision Converence, 2003.
[13] C. F. Olson and D. P. Huttenlocher. Automatic target recognition by
matching oriented edge pixels. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 6(1):103-113, 1997.
[14] E. G. M. Petrakis, A. Diplaros, and E. Milios. Matching and retrieval of
distorted and occluded shapes using dynamic programming. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
24(11):1501-1516, 2002.
[15] X. Ren, C. Fowlkes, and J. Malik. Mid-level cues improve boundary
detection. Berkeley Technical Report 05-1382, CSD 2005.
[16] S. Sclaroff and L. Liu. Deformable shape detection and description via
model-based region grouping. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 23(5):475-489, 2001.
[17] S. Wang, T. Kubota, J.M.Siskind, and J.Wang. Salient closed boundary
extraction with ratio contour. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 27(4):546-561, 2005.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54420", author = "Feng Ge and Tiecheng Liu and Song Wang and Joachim Stahl", title = "Template-Based Object Detection through Partial Shape Matching and Boundary Verification", abstract = "This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.", keywords = "Object detection, shape matching, contour grouping.", volume = "2", number = "11", pages = "2528-10", }