This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast pre-screening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively. Extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match.
A great amount of research effort has been dedicated to designing efficient and effective template matching techniques. Based on conventional full search matching, Alkhansari proposed a technique that reduces search space by pruning unmatched regions using a downsampled reference image @cite_26 . Pele and Werman developed a method to determine the optimal step size of sliding windows for full search matching @cite_13 . On the topic of latest full search equivalent techniques, Ouyang provided a comprehensive survey and compared the performances of several popular methods @cite_9 .
@cite_9 @cite_26 @cite_13
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We present a new method for the robust detection and matching of multiple planes in pairs of images. Such planes can serve as stable landmarks for vision-based urban navigation. Our approach starts from SIFT matches and generates multiple local homography hypotheses using the recent J-linkage technique by Toldo and Fusiello, a robust randomized multi-model estimation algorithm. These hypotheses are then globally merged, spatially analyzed, robustly fitted, and checked for stability. When tested on more than 30,000 image pairs taken from panoramic views of a college campus, our method yields no false positives and recovers 72 of the matchable building walls identified by a human, despite significant occlusions and viewpoint changes. One of the important tasks of an autonomous mobile vehicle is the reliable and fast estimation of its position over time. This paper presents the development of an adaptive technique to hasten and improve the quality of correlation-based template matching for monocular visual odometry systems that estimate the relative motion of ground vehicles in low-textured environments. Moreover, the factors that can affect the maximum permissible vehicle driving speed were determined and the related equations were derived. The developed system uses a single downward-facing monocular camera installed at an optimum location to avoid the negative effect of directional sunlight and shadows which can disturb the correlation. In addition, the normalized cross-correlation method is implemented to calculate the pixel displacement between image frames. Although this method is highly effective for template matching because of its invariance to linear brightness and contrast variations, it incurs high computational cost. Thus, the optimal sizes of image template and matching search area are selected and their locations are dynamically changed according to vehicle acceleration, in order to achieve a compromise between the performance and the computational cost of correlation. The proposed technique increases the allowable vehicle driving speed and reduces the probability of template false-matching. Moreover, compared to traditional full search matching techniques, the adaptive technique demonstrates high efficiency and accuracy and improves the quality and speed of the correlation with more than 87 of reduction in computational cost. This paper proposes a new template matching method that is robust to outliers and fast enough for real-time operation. The template and image are densely transformed in binary code form by projecting and quantizing histograms of oriented gradients. The binary codes are matched by a generic method of robust similarity applicable to additive match measures, such as L p - and Hamming distances. The robust similarity map is computed efficiently via a proposed Inverted Location Index structure that stores pixel locations indexed by their values. The method is experimentally justified in large image patch datasets. Challenging applications, such as intra-category object detection, object tracking, and multimodal image matching are demonstrated. We propose a novel measure for template matching named Deformable Diversity Similarity &#x2013; based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.