Home Articles Initialization for image registration using feature matching

Initialization for image registration using feature matching

ACRS 1999

Poster Session 1

Initialization for Image Registration using Feature Matching

Liang-Chien Chen and Jeng-Daw
Lee

Center for Space and Remote Sensing Research
National Central University, Chung-Li
Tel: (886)-3-4257232 , Fax: (886)-3-4254908
E-mail: [email protected]
China Taipei

Keywords: Image Registration, Segmentation, Feature Matching


Abstract

We present here a strategy to initialize the registration using feature matching.
The work includes extraction for feature polygons, description for region boundaries, and
similarity assessment. We combine three descriptors for feature polygons, namely, Shape-Matrix,
Fourier Descriptors, and Invariant Moments in the matching scheme. Three descriptors are both
scale and rotation invariant. Matching conjugate polygons with Shape Matrix approach is
reliable only when the principal axis is unambiguous. Fourier Descriptor is good at detail
descriptions. Invariant Moments are suitable to correspond the polygons with smooth
boundaries. Combining those complementary descriptors in similarity assessment, we propose a
selecting scheme to generate reliable matching pairs. Experimental results indicate that the
matching between an airborne scanner image and an aerial photo is reliable.

Introduction
The registration between a reference image and its counterpart, a second remotely sensed image,
is a necessity in many image analysis tasks such as change detection, feature or color
enhancement, map revision, and data fusion.

Two approaches are possible. The first is rigorous orthorectification [Mayr & Heipke, 1988].
Through orthorectification for each image, multi-temporal and multi-source images are
co-registered in the ground coordinate system. The approach is rigorous and robust. However, it
needs orientation parameters for the sensor in addition to a digital terrain model (DTM). The
second approach, on the other hand, performs image-to-image registration [Goshtasby et al.,
1986]. This approach does not require orientation parameters or DTMs. However, a reference
image is needed. Considering the advantages of the second approach, we will focus our
investigation on the image-to-image registration.

The procedure of image registration may be divided into two steps. The first is to select enough
registration control points (RCPs) then to measure the corresponding image coordinates. The
second step is to choose a mapping function after which a coordinate transformation is
performed. The first step is essentially the key work in automated registration. Several
approaches for automating the procedure have been proposed [Goshtasby et al., 1986; Nevatia &Medioni, 1984]. Those approaches suffer from the following limitations: (1) the number of
RCPs is often not sufficient, (2) the distribution of RCPs is not always uniform, and (3) the
point-to-point correspondence is not always sufficiently accurate. To cope with the weaknesses,
Chen & Lee [1992] proposed a scheme to densitify the control frameworks. The method was
also successfully implemented in registering an airborne scanner image on a digitized aerial
photo [Chen & Rau, 1993]. One weak point of the method is that at least 3 RCPs are needed to
provide the initial registration. We, thus, propose here a scheme to perform feature matching for
initializing the registration. The proposed scheme includes three major components: (1) feature
extraction, (2) feature description, and (3) similarity assessment and image matching.

Feature Extraction
Points, lines, and polygons are the three types of image features. Considering the content of
shape information, which is crucial in feature matching, we select shape polygons for
processing.

Image segmentation is an essential procedure to extract feature polygons from images. In the
segmentation, we use “Energy” [Pratt, 1991] of gray values as feature index to segment an
image. To improve the results of segmentation, a smoothing preprocess is preferable. In order to
preserve edge information in a smoothing procedure, we combine two methods to achieve that.
Those methods include (1) Adaptive Smoothing (AS) [Saint-Marc et al., 1991] and (2)
Symmetric Nearest Neighbor filter (SNN) [Harwood, et al., 1987]. The combination of the
methods achieves a goal that each segmented block is more homogeneous while the edges are
still preserved.

To further enhance the edges, we consider the Multi-resolution Edge Detection (MEDT) [Deok,
1995] method to strengthen the edge effect. After calculation the edge strengths, which are
normalized from 0 to 1, we multiply the grey values by the strength values to enhance the block
boundaries. Finally, “Energy” value is computed as a segmentation index.

Feature Description
Three feature measurements are considered namely, Shape Matrix (SM) [Flusser, 1992], Fourier
Descriptor (FD) [Pratt, 1991], and Invariant Moments (IM) [Pratt, 1991]. Three descriptors are
both scale and rotation invariant. Matching conjugate polygons with SM is reliable only when
the principal axis in unambiguous. FD is good at detail descriptions. IM is suitable to
correspond those polygons with smooth boundaries. Considering the complementary
characteristics, three measurements are combines in further matching procedure.

Similarity Assessment and Matching
We describe the three indices for three descriptors for measuring the shape similarity. Then a
matching strategy for combining three indicators will be provided.

ACRS 1999

Poster Session 1

Initialization for Image Registration using Feature Matching

Similarity Assessment
We use root mean square difference (RMSD) to measure the similarity for FD and IM. For a
sensed image and its counterpart, i.e., reference image, the RMSD for FD is defined as

br>

Two shapes are with higher similarity when smaller RMSD is observed.

The RMSD for seven IMs is defined as

For SM, referring to figure 1, the similarity between shape A and B is calculated as


Figure 1. Illustration of SimilarityAssessment for SM

The index P(A,B) reflects higher similarity when higher value is calculated.

Matching
Considering three indices, i.e., RMSDFD, RMSDIM, and P(A,B), a successful matching should
fulfill following criteria:

(1) RMSDFD<=dFD
(2) RMSDIM<=dIM
(3) Min(RMSDFDxRMSDIM
(4)P(A,B)<=dP(AB),Max(P(A,B))
(5)A subset of the intersection from the three sets that fulfills the four criteria is selected as the potential matching pair.]

Experimental Results
The test area is located in north Taiwan. The reference image is an orthorectified aerial photo
with 2000×2000 pixels at 1.12m pixel spacing as shown in figure 2(a). The sensed image is an
airborne scanner image with 512×512 pixels at 4.5 nominal ground resolution as shown in figure
2(b). The sensed image was resampled to 2000×2000 pixels for processing convenience. The
preprocessed images, including the procedure of AS, SNN and edge enhancement, are shown in
figure 3. The segmented blocks using Energy method are shown in figure 4.

(a)
(b)
Figure 2. Test Images (a) Reference Image (b) Sensed Image

(a)(b)

Figure 3.Preprocessed Images (a) Reference Image (b) Sensed Image

By visual inspection, the corresponding polygon pairs are shown in table 1. Considering the
combined results of FD and IM, table 2 shows the correspondence. The correspondence using
SM only is illustrated in table 3. Combining FD, IM, and SM, the matching results are shown in
table 4. The final results, with robust estimation, are shown in table 5.

ACRS 1999

Poster Session 1

Initialization for Image Registration using Feature Matching

(a)
(b)
Figure 7. Segmented Polygons with IDs (a) Reference Image (b) Sensed Image

Table 1. Corresponded
Polygons

SensedReference
s1r1
s3r3
s4r4
s5r5
s6r6
s8r7
s10r8
s11r9
s12r10
s13r13
s14r14
s15r16
s16r15
s17r17
s18r18
s20r20


Table 2. Combined
Correspondence for FD & IM

SensedReference
s1r1
s4r2
s5r4
s6r12
s8r7
s10r15
s11r9
s12r10
s13r13
s15r16
s17r17
s20r20

Table 3. Correspondence
by SM

SensedReference
s1r1
s3r3
s5r5
s6r6
s8r7
s10r8
s12r10
s15r16
s16r15
s17r17
s18r18
s19r14
s20r20

Table 4. Matching Results

Table 5. Robust Estimated Results

SensedReference
s1r1
s8r7
s12r10
s15r16
s17r17
s20r20

SensedReference
s1r1
s8r7
s12r10
s15r16
s17r17
s20r20

It is observed that if only FD and IM are used, more polygon pairs will be selected. While some
erroneous correspondence (S4, S5, S6, and S10) are remained. On the other hand, if we use SM,
the situation is similar. The difference is that the remained erroneous corresponding polygon (S19) is different. When three descriptors are combined, the selected polygon pairs are less
while with highest reliability. It is the purpose of this investigation that we only need small
amount, 3 for instance, of RCPs for initialization of image registration. Thus, the proposed
scheme is validated for the time being.

Concluding Remarks
The experimental results indicate that the proposed scheme may select RCP pairs with very high
reliability. Although the number of selected pairs is less, the reliability is our concerned. Thus,
the test is successful. However, we still need further tests to ascertain the applicability. It should
be pointed out that the proposed scheme might only be used for the area of rolling terrain. For
those images with rugged terrain or large scale images with high-rises the scheme may result
unreliably.

References

  • Chen, L.C., and Lee, L. H., 1992, Progressive Generation of Control Frameworks for Image
    Registration, Photogrammetric Engineering and Remote Sensing, 58 (9), pp.1321-1328.
  • Chen, L. C., and Rau, J. Y., 1993. “Geometric Correction of Airborne Scanner Imagery
    Using Orthophotos and Triangulated Feature Points”, International Journal of Remote
    Sensing. 14 (16), pp.3041-3059.
  • Deok, J. P., Nam, K. M., and Park, R. H., 1995, Multiresoultion edge detection techniques,
    Pattern Recognition, 28 (2), pp. 211-229.
  • Flusser, J., 1992. Invariant shape description and measure of object similarity, Proc. 4th
    Internat. Conf. Image Processing, Maastricht, The Netherlands, pp.139-142.
  • Goshtasby, A., Stockman, G., and Page, C., 1986. A region-based approach to digital image
    registration with subpixel accuracy, IEEE Trans. On Geoscience and Remote Sensing, 24
    (3), pp. 390-399.
  • Harwood, D., Subbarao, M., Hakalahti, H., and Davis, L. S., 1987, A new class of
    edge-preserving smoothing filters, Pattern Recognition Letters, 6, pp. 155-162.
  • Mayr, W. and Heipke, C., 1988, A contribution to digital orthophoto generation,
    International Archives of Photogrammetry and Remote Sensing, Kyoto, Vol. XXVII, Part
    B11, pp. 430-439.
  • Nevatia, R., and Medioni, G., 1984, Matching images using linear features, IEEE Trans. on
    PAMI, 6 (6), pp. 675-685.
  • Saint-Marc, P., Chen, J. S., and Medioni, G., 1991, Adaptive smoothing: a general tool for
    early vision, IEEE Pattern Analysis and Machine Intelligence, 13 (6), pp.514-529.
  • Pratt, W. K., 1991, Digital Image Processing, 2nd edition, John Wiley & Sons, New York, pp.
    563-648.