Efficient NCC-Based Image Matching in Walsh-Hadamard Domain

Pan, Wei-Hau; Wei, Shou-Der; Lai, Shang-Hong

doi:10.1007/978-3-540-88690-7_35

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…For FOHT, since one image is preprocessed only once for every group, the time proportion of preprocessing is reduced greatly in comparison with computing of HPVs, making it faster than OHT N in all the results that Figs. 11,12,13,14 show. Moreover, in general the advantage of FOHT over OHT N is more obvious about speed-ups for bigger patterns.…”

Section: Experiments For Matching Multiple Patternsmentioning

confidence: 97%

“…In order to reduce the computational complexity and time cost, a lot of fast algorithms are proposed as an alternative to the full search algorithm, some of them using normalized correlation as similarity [9], [10], [11], [12], [13], [14], and some of them using the sum of absolute differences (SAD), the sum of squared differences (SSD) or L p distance as dissimilarity. Certainly, general pattern matching may use the Hamming distance [15], or even use the dissimilarity between pattern descriptors and candidate window descriptors [16], [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

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Fast Orthogonal Haar Transform PatternMatching via Image Square Sum

Cai

2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Although using image strip sum, an orthogonal Haar transform (OHT) pattern matching algorithm may have good performance, it requires three subtractions to calculate each Haar projection value on the sliding windows. By establishing a solid mathematical foundation for OHT, this paper based on the concept of image square sum, proposes a novel fast orthogonal Haar transform (FOHT) pattern matching algorithm, from which a Haar projection value can be obtained by only one subtraction. Thus, higher speed-ups can be achieved, while producing the same results with the full search pattern matching. A large number of experiments show that the speed-ups of FOHT are very competitive with OHT in most cases of matching one single pattern, and generally higher than OHT in all cases of matching multiple patterns, exceeding other high-level full search equivalent algorithms.

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Section: Experiments For Matching Multiple Patternsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Fast Orthogonal Haar Transform PatternMatching via Image Square Sum

Cai

2014

IEEE Trans. Pattern Anal. Mach. Intell.

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“…The similarity score plays a key role in measuring confidence and distinguishing the target object from the background. The most widely used off-the-shelf techniques are pixel-wise methods such as SSD, SAD, and normalised crosscorrelation (NCC) [7,8], owing to their simplicity and efficiency. These methods have been combined with tone mapping [9] for handling illumination change, with asymmetric correlation [10] to deal with noise.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, the result on illumination-change-only dataset significantly degrades when the appearance term or the rank term is not included, which is consistent with our previous discussion. Furthermore, we show the affect of verifying the proportion between the appearance term and the rank term by varying the parameter 𝜆 3 in Equation (8). Results are summarised in Table 3.…”

Section: Ablation Experimentsmentioning

confidence: 99%

DS‐SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching

Zhang

Akashi

2022

IET Image Processing

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This paper presents a novel multi-scale template matching method that can be applied in unconstrained environments. The key component behind this is a general similarity measure is referred to as the diversity similarity measure against scaling, rotation, and illumination (DS-SRI). Specifically, DS-SRI exploits bidirectional diversity calculated from the nearest neighbour matches between two sets of points. Scaling and rotation changes are taken into consideration by introducing normalisation term on the scale change, and geometric consistency term with respect to the polar coordinate system. Moreover, in order to deal with the illumination change and further deformation, illumination-corrected local appearance and rank information are jointly exploited during the nearest neighbour search. All the features of DS-SRI are statistically assessed, and the extensive visual and quantitative results on both synthetic and real-world data show that DS-SRI can significantly outperform state-of-the-art methods.

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“…There are two factors influencing computation overheads. One is what similarity measures, including cross-correlation (CC) [5] [6], normalized crosscorrelation (NCC) [7] [8], and sum of squared differences (SSD) [9], are used. Similarity measure influences performances as well.…”

Section: Introductionmentioning

confidence: 99%

Fast Template Matching by Subsampled Circulant Matrix

Hsieh,

Lu,

Pei

2015

Preprint

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Template matching is widely used for many applications in image and signal processing and usually is time-critical. Traditional methods usually focus on how to reduce the search locations by coarse-to-fine strategy or full search combined with pruning strategy. However, the computation cost of those methods is easily dominated by the size of signal N instead of that of template K. This paper proposes a probabilistic and fast matching scheme, which computation costs requires O(N ) additions and O(K log K) multiplications, based on cross-correlation. The nuclear idea is to first downsample signal, which size becomes O(K), and then subsequent operations only involves downsampled signals. The probability of successful match depends on cross-correlation between signal and the template. We show the sufficient condition for successful match and prove that the probability is high for binary signals with K 2 log K ≥ O(N ). The experiments shows this proposed scheme is fast and efficient and supports the theoretical results.

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Efficient NCC-Based Image Matching in Walsh-Hadamard Domain

Cited by 11 publications

References 15 publications

Fast Orthogonal Haar Transform PatternMatching via Image Square Sum

Fast Orthogonal Haar Transform PatternMatching via Image Square Sum

DS‐SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching

Fast Template Matching by Subsampled Circulant Matrix

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