Census Cost Research Articles

Fusing Census and MC-CNN Cost Volumes for Stereo Matching

Fusing Census and MC-CNN Cost Volumes for Stereo Matching

High-Resolution Satellite Stereo Matching by Object-Based Semiglobal Matching and Iterative Guided Edge-Preserving Filter

This letter presents an object-based stereo matching and content-based guided disparity map refinement for high-resolution satellite stereo images. The proposed method uses the superpixels for object-based cost filtering and then object-based semiglobal matching. In the proposed object-based stereo matching strategy, two improvements including homogeneity weight for cost filtering, and weighted cost aggregation by image objects are considered. After cost aggregation, the generated disparity map is computed using the winner takes all and then refined with a new iterative guided edge-preserving filter. The proposed method with Census cost function has been implemented on high-resolution satellite stereo images and then is compared with the LiDAR ground truth. Moreover, the proposed method is compared with five state-of-the-art stereo matching methods. The experimental results on along-track satellite stereo images from Pleiades and IKONOS images and cross-track multidate images from WV-III demonstrate that the proposed method significantly improves the result of stereo matching.

Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.

A Robust 3-D Abrasion Diagnosis Method of Pantograph Slipper Based on Stereo Vision

The pantograph is an essential component in modern electrified trains. Due to the characters of contactless and high precision, the 3-D stereo vision, which can effectively monitor the abrasion, is of great significance for ensuring train safety. However, illumination conditions, such as overbrightness and insufficient light, have a great impact on semiglobal matching (SGM) stereo matching, inevitably affecting the integrity and accuracy of the abrasion measurement of the pantograph slipper. In this regard, a robust SGM stereo matching is proposed in this article for the 3-D abrasion measurement. First, the combination of the neighborhood information-aware census cost and multiscale fusion for cost aggregation is put forward. While insensitive to illumination condition changes, the improved census cost can quantify the texture information of the neighborhood. Moreover, multiscale cost aggregation further fuses the neighborhood information of different scales. As a result, the performance of SGM in weakly textured areas is greatly improved, especially in complex illumination conditions. Eventually, with the help of the subpixel disparity refinement and local points fitting, an accurate 3-D abrasion calculation is achieved. Practical experiments show the robustness of stereo matching. The precision verification and computation time analysis confirm the measurement accuracy and applicability.

EVALUATION OF SELECTED COST AGGREGATION METHODS ON HIGH RESOLUTION SATELLITE STEREO IMAGES

Abstract. Dense stereo processing requires a critical step that called cost aggregation or cost optimization. Most of the cost aggregation methods are evaluated on close range stereo images from Middlebury or KITTI datasets. While the effect of cost aggregation on high resolution satellite stereo processing has not yet been sufficiently evaluated. In this paper, three typical cost aggregation methods together with another approach which is a combination of these methods are evaluated on high resolution satellite stereo images and then are compared with LiDAR ground truth. These methods including Semi-Global Matching (SGM), Guided Filtering (GF), iterative GF (IGF), and SGM followed by GF (SGM-GF) with Census and Zero Normalized Cross Correlation (ZNCC) cost functions. Although the Census cost function has a good performance on the border of the objects and low blurring effects, the results of both cost functions, i.e. Census and ZNCC, have same treatment on all stereo methods. Also, in order to make an impartial assessment, for all stereo methods, we do not perform any disparity map refinement. The bad-pixel criteria with an absolute difference height error greater than 2 meters for SGM, GF, IGF, and SGM-GF methods is 36.7%, 34.8%, 33.8%, and 28.6% respectively. Also, the Normalized Median Absolute Difference (NMAD) error for SGM, GF, IGF, and SGM-GF is 1.29, 1.15, 1.06, and 0.94 meters, respectively. Overall, the experimental results on WV III stereo images demonstrate that the SGM method has lower accuracy and SGM-GF method is accurate than other methods.

A critical analysis of satellite stereo pairs for digital surface model generation and a matching quality prediction model

The geometric analysis and data acquisition of satellite photogrammetric images are often regarded as a direct extension of traditional aerial photogrammetry, with the only difference being the sensor model (linear array vs. central perspective). The intersection angle (or base-height ratio) between two images is seen as the most important metadata of stereo pairs, which directly relates to the base-high ratio and texture distortion in the parallax direction, thus both affecting the horizontal and vertical accuracy. State-of-the-art DIM algorithms were reported to work best for narrow baseline stereos (small intersection angle), e.g. Semi-Global Matching empirically takes 15–25° as “good” intersection angles. However, our experiments found that the intersection angle is not the only determining factor, as the same DIM algorithm applied to stereo pairs of the same area with similar and good intersection angle may produce point clouds with dramatically different accuracy (demonstrated in the graphical abstract). This raises a very practical and often asked question: what factors constitute a good satellite stereo pair for DIM algorithms? In this paper, we provide a comprehensive analysis on this matter by performing stereo matching using the very typical and widely-used Semi-Global Matching (SGM) with a Census cost over 1000 satellite stereo pairs of the same region with different meta-parameters including their intersection, off-nadir, sun elevation & azimuth angles, completeness and time differences, thus to offer a thorough answer to this question. Our conclusion has specifically outlined an important yet often ignored factor – the Sun-angle difference to be one decisive in determining good stereo pair. Based on the analytical results, we propose a simple idea by training a support vector machine model for predicting potential stereo matching quality (i.e. potential level of accuracy and completeness given a stereo pair). Experiments have shown that the model is well-suited and generalized for multi-stereo 3D reconstruction, evidenced by a comparative analysis against three other strategies: (1) pair selection based on an example patch where partial ground-truth data is available for computing a priori ranking (2) based on intersection angles and (3) based on a recent algorithm using intersection angle, off-nadir angle and time intervals. This work will potentially provide a valuable reference to researchers working on multi-view satellite image reconstruction, as well as for practitioners minimizing costs for high-quality large-scale mapping. The trained model is made available to the academic community upon request.

Stereo matching using census cost over cross window and segmentation-based disparity refinement

Stereo matching is a vital requirement for many applications, such as three-dimensional (3-D) reconstruction, robot navigation, object detection, and industrial measurement. To improve the practicability of stereo matching, a method using census cost over cross window and segmentation-based disparity refinement is proposed. First, a cross window is obtained using distance difference and intensity similarity in binocular images. Census cost over the cross window and color cost are combined as the matching cost, which is aggregated by the guided filter. Then, winner-takes-all strategy is used to calculate the initial disparities. Second, a graph-based segmentation method is combined with color and edge information to achieve moderate under-segmentation. The segmented regions are classified into reliable regions and unreliable regions by consistency checking. Finally, the two regions are optimized by plane fitting and propagation, respectively, to match the ambiguous pixels. The experimental results are on Middlebury Stereo Datasets, which show that the proposed method has good performance in occluded and discontinuous regions, and it obtains smoother disparity maps with a lower average matching error rate compared with other algorithms.

Evaluation of statistical precision and design of efficient sampling for the population estimation based on frequency of occurrence

SummaryThe binomial sampling to estimate population density of an organism based simply upon the frequency of its occurrence among sampled quadrats is a labour‐saving technique which is potentially useful for small animals like insects and has actually been applied occasionally to studies of their populations. The present study provides a theoretical basis for this convenient technique, which makes it statistically reliable and tolerable for consistent use in intensive as well as preliminary population censuses. Firs, the magnitude of sampling error in relation to sample size is formulated mathematically for the estimate to be obtained by this indirect method of census, using either of the two popular models relating frequency of occurrence (p) to mean density (m), i.e. the negative binomial model, p=1−(1+m/k)−k, and the empirical model, p=1−exp(−amb). Then, the equations to calculate sample size and census cost that are necessary to attain a given desired level of precision in the estimation are derived for both models. A notable feature of the relationship of necessary sample size (or census cost) to mean density in the frequency method, in constrast to that in the ordinary census, is that it shows a concave curve which tends to rise sharply not only towards lower but also towards higher levels of density. These theoretical results make it also possible to design sequential estimation procedures based on this convenient census technique, which may enable us with the least necessary cost to get a series of population estimates with the desired precision level. Examples are presented to explain how to apply these programs to acutal censuses in the field.