Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes

Chang, Shuenn‐Yih; Cheng, Fang‐Hsuan; Hsu, Wen‐Hsing; Wu, Guoxing

doi:10.1016/s0031-3203(96)00076-3

Cited by 147 publications

(75 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Except for naturally occurring features on wood surfaces, there may also be other features induced by handling, such as dirt or dents. Point pattern matching is a problem that consists of finding correct point matches between two images or scenes (Chang et al 1997). This problem has previously been addressed in, for example, star and constellation recognition (Spratling and Mortari 2009), biometrics (Jain et al 2007, Chap.…”

Section: Point Pattern Matchingmentioning

confidence: 99%

See 1 more Smart Citation

Wood fingerprint recognition using knot neighborhood K-plet descriptors

et al. 2014

View full text Add to dashboard Cite

In the wood industry there is a wish to recognize and track wood products through production chains. Traceability would facilitate improved process control and extraction of quality measures of various production steps. In this paper, a novel wood surface recognition system that uses scale-and rotationally invariant feature descriptors called K-plets is described and evaluated. The idea behind these descriptors is to use information of how knots are positioned in relation to each other. The performance and robustness of the proposed system is tested on 212 wood panel images with varying levels of normally distributed errors applied to the knot positions. The results showed that the proposed method is able to successfully identify 99-100% of all panel images with knot positional error levels that can be expected in practical applications.

show abstract

Section: Point Pattern Matchingmentioning

confidence: 99%

“…According to Chang et al (1997) there are a few problems associated with point pattern matching in general:…”

Section: Point Pattern Matchingmentioning

confidence: 99%

Wood fingerprint recognition using knot neighborhood K-plet descriptors

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Note that since there have been a copious of studies investigating the problem of measuring the similarities between fingerprints, this work does not intend to develop a brand new solution to this problem, but only to provide an example from numerous possibilities. All the three components for the inter-fingerprint similarity computation are derived from some prior work [24]- [30]. Figure 4 shows the overall flowchart of the preprocessing for each fingerprint.…”

Section: Inter-fingerprint Similarity Computationmentioning

confidence: 99%

“…In this study, the features between two fingerprints are aligned using the method in [30]. Accordingly, the number of consistent features between F (i) and F ( j) can be measured Fig.…”

Section: Feature Matchingmentioning

confidence: 99%

Unsupervised Fingerprint Recognition

Tsai

Lin

Tseng

2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis study extends conventional fingerprint recognition from a supervised to an unsupervised framework. Instead of enrolling fingerprints from known persons to identify unknown fingerprints, our aim is to partition a collection of unknown fingerprints into clusters, so that each cluster consists of fingerprints from the same finger and the number of generated clusters equals the number of distinct fingers involved in the collection. Such an unsupervised framework is helpful to handle the situation where a collection of captured fingerprints are not from the enrolled people. The task of fingerprint clustering is formulated as a problem of minimizing the clustering errors characterized by the Rand index. We estimate the Rand index by computing the similarities between fingerprints and then apply a genetic algorithm to minimize the Rand index. Experiments conducted using the FVC2002 database show that the proposed fingerprint clustering method outperforms an intuitive method based on hierarchical agglomerative clustering. The experiments also show that the number of clusters determined by our system is close to the true number of distinct fingers involved in the collection.

show abstract

“…That is, close neighbors in the model must map to close neighbors in the scene. We are looking for a telephone in a room, not a constellation in the Vinod and Ghose [4] Asymmetric neural networks Translation, rotation, distortion and noise but not scaling 1996 Irani and Raghavan [5] Randomized alignment Translation, rotation and scaling O(n 2 m log n) 1997 Chang et al [6] 2-D Cluster approach Translation, rotation, scale changes, local distortion, extra and or missing points O(n 4 )…”

Section: Introductionmentioning

confidence: 99%