Adaptive Normalized Cross Correlation Research Articles

Unmanned aerial vehicle oblique image registration using an ASIFT-based matching method

Low-altitude unmanned aerial vehicles (UAV) are widely used to acquire aerial photographs, some of which are oblique and have a large angle of view. Precise, automatic registration of such images is a challenge for conventional image processing methods. We present an affine scale-invariant feature transform (ASIFT)-based method that can register UAV oblique images at a subpixel level. First, we used the ASIFT algorithm to collect initial feature points. Positions of the feature points on corresponding local images were then corrected using the weighted least square matching (WLSM) method. Mismatching points were discarded and a local transform model was estimated using the adaptive normalized cross correlation algorithm, which also provides initial parameters for WLSM. Experiments show that sufficient feature points are collected to successfully register, to the subpixel level, UAV and other images with large angle-of-view variations and strong affine distortions. The proposed method improves the matching accuracy of previous UAV image registration methods.

Intensity guided cost metric for fast stereo matching under radiometric variations.

Reliable and efficient stereo matching is a challenging task due to the presence of multiple radiometric variations. In stereo matching, correspondence between left and right images can become hard owing to low correlation between radiometric changes in left and right images. Previously presented cost metrics are not robust enough against intensive radiometric variations and/or are computationally expensive. In this work, we propose a new similarity metric coined as Intensity Guided Cost Metric (IGCM). IGCM turns out to significantly contribute to the depth accuracy by rejecting outliers and reducing the edge-fattening effect in object boundaries. IGCM is further combined explicitly with a color formation model to handle various radiometric changes that occur between stereo images. Experimental results on Middlebury dataset show 13.8%, 22.8%, 20.9%, 19.5 % and 9.1% decrease in average error rate compared to Adaptive Normalized Cross-Correlation (ANCC), Dense Adaptive Self-Correlation (DASC), Adaptive Descriptor(AD), Fast Cost Volume Filtering (FCVF) and Iterative Guided Filter (IGF)-based methods, respectively. Moreover, using integral images IGCM can achieve a speedup of 20x, 6x, 41x, 25x and 45x compared to the aforementioned methods.

Robust Adaptive Normalized Cross-Correlation for Stereo Matching Cost Computation

Stereo matching is a challenging task because stereo images are affected by many factors, such as radiometric distortion, sun and rain flare, flying snow, occlusion, textureless and noisy image regions, and object boundaries. However, most of the existing methods for stereo matching aim to solve only one specific problem. As a result, their performance is degraded significantly when operating with stereo images captured under a variety of scenes and conditions. In this paper, we propose a novel matching cost function based on adaptive normalized cross-correlation (ANCC). We demonstrate several weaknesses of ANCC and propose techniques to resolve them. In addition, we employ available information, such as intensity mean, intensity variance, and support window radius, to estimate the parameters of the proposed matching cost function. Compared with ANCC, the proposed matching cost function reduces the error rates from 24.1% to 17.8% in the Middlebury data set and from 64.1% to 26.4% in the KITTI data set. In addition, for noisy stereo pairs, the proposed function reduces the error rate from 73.6% to 37.3%. The qualitative and quantitative experimental results based on stereo images in different data sets under various conditions show that our proposed matching cost function outperforms state-of-the-art matching cost functions in indoor and outdoor stereo images having various radiometric distortions.

Robust Stereo Data Cost With a Learning Strategy

The performance of stereo matching algorithms strongly depends on the quality of the stereo data/matching cost. Most state-of-the-art data costs require expert knowledge for the design of a transformation function, such as census for handling gray-level changes monotonically, adaptive normalized cross correlation for handling Lambertian cases, guided filtering for preserving edge information, and local density encoding for handling illumination differences. However, it is difficult to design a complex transformation function to handle unknown factors that often occur in driving conditions such as snow, rain, and sun. Therefore, this paper has investigated the deep learning strategy to develop a novel stereo matching cost model without using much expert knowledge. Experimental results show that the proposed deep learning model obtains better results than the state-of-the-art stereo matching cost as judged by the standard KITTI benchmark, Middlebury, and HCI datasets.

A robust stereo matching algorithm based on improved epipolar distance transformation

Aim to the problem of initial epipolar distance transformation algorithm is sensitive to illumination and depth discontinuous, an improved stereo matching algorithm is proposed. In Delta-Gama log-chromaticity space, Gamma correction coefficient is computed firstly according to raw disparity map which is used to Modified the similarity function bandwidth. Secondly, by employing image structure and color features, the segment of epipolar is obtained adaptively. At last, final disparity map is acquired by belief propagation method. Experimental results demonstrate the effectiveness of the proposed transform in illumination change regions and discontinuities regions. The matching rate of proposed algorithm is improved about 60%, comparing with the initial epipolar distance transformation algorithm and state-of-art algorithm such as ANCC(Adaptive Normalized Cross Correlation).

Fuzzy Encoding Pattern for Stereo Matching Cost

We propose a novel fuzzy encoding pattern that fuzzily encodes the relative orders between pixel pairs. An image window is divided into disjoint neighboring pixel sets for the window’s center pixel, and the relative order is established not only between the center pixel and its neighbors but also between the pixel pairs in each neighboring pixel set. The relative orders are fuzzily encoded to extract more detailed information from a local structure. We successfully apply the pattern as a matching cost function for stereo correspondence under severe radiometric variations. We conduct experiments using the proposed matching cost function and compare it with functions employing the census transform, supporting local binary pattern, and adaptive normalized cross correlation, as well as a mutual information-based matching cost function, using different stereo data sets. Compared with the census transform, the proposed function reduces the error from 33.1% to 16.9% in the Middlebury data set and from 17.6% to 9.5% in the Kitti data set. The experimental results indicate that the proposed function is superior to the state-of-the-art functions under radiometric variations. In addition, the proposed function is faster than recently developed functions, such as the adaptive normalized cross correlation, a mutual information-based function, and support local binary pattern.

Robust Matching Cost Function for Stereo Correspondence Using Matching by Tone Mapping and Adaptive Orthogonal Integral Image.

Real-world stereo images are inevitably affected by radiometric differences, including variations in exposure, vignetting, lighting, and noise. Stereo images with severe radiometric distortion can have large radiometric differences and include locally nonlinear changes. In this paper, we first introduce an adaptive orthogonal integral image, which is an improved version of an orthogonal integral image. After that, based on matching by tone mapping and the adaptive orthogonal integral image, we propose a robust and accurate matching cost function that can tolerate locally nonlinear intensity distortion. By using the adaptive orthogonal integral image, the proposed matching cost function can adaptively construct different support regions of arbitrary shapes and sizes for different pixels in the reference image, so it can operate robustly within object boundaries. Furthermore, we develop techniques to automatically estimate the values of the parameters of our proposed function. We conduct experiments using the proposed matching cost function and compare it with functions employing the census transform, supporting local binary pattern, and adaptive normalized cross correlation, as well as a mutual information-based matching cost function using different stereo data sets. By using the adaptive orthogonal integral image, the proposed matching cost function reduces the error from 21.51% to 15.73% in the Middlebury data set, and from 15.9% to 10.85% in the Kitti data set, as compared with using the orthogonal integral image. The experimental results indicate that the proposed matching cost function is superior to the state-of-the-art matching cost functions under radiometric variation.

Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain

This paper proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT) features in Steerable-Domain, which can deal with the large variations of scale, rotation and illumination between images. First, a new robust SIFT descriptor is presented, which is invariant under affine transformation. Then, an adaptive similarity measure is developed according to the robust SIFT descriptor and the adaptive normalized cross correlation of feature point’s neighborhood. Finally, the corresponding feature points can be determined by the adaptive similarity measure in Steerable-Domain of the two input images, and the final refined transformation parameters determined by using gradual optimization are adopted to achieve the registration results. Quantitative comparisons of our algorithm with the related methods show a significant improvement in the presence of large scale, rotation changes, and illumination contrast. The effectiveness of the proposed method is demonstrated by the experimental results.

Robust Stereo Matching Using Adaptive Normalized Cross-Correlation

A majority of the existing stereo matching algorithms assume that the corresponding color values are similar to each other. However, it is not so in practice as image color values are often affected by various radiometric factors such as illumination direction, illuminant color, and imaging device changes. For this reason, the raw color recorded by a camera should not be relied on completely, and the assumption of color consistency does not hold good between stereo images in real scenes. Therefore, the performance of most conventional stereo matching algorithms can be severely degraded under the radiometric variations. In this paper, we present a new stereo matching measure that is insensitive to radiometric variations between left and right images. Unlike most stereo matching measures, we use the color formation model explicitly in our framework and propose a new measure, called the Adaptive Normalized Cross-Correlation (ANCC), for a robust and accurate correspondence measure. The advantage of our method is that it is robust to lighting geometry, illuminant color, and camera parameter changes between left and right images, and does not suffer from the fattening effect unlike conventional Normalized Cross-Correlation (NCC). Experimental results show that our method outperforms other state-of-the-art stereo methods under severely different radiometric conditions between stereo images.