Accuracy-Efficiency Evaluation of Adaptive Support Weight Techniques for Local Stereo Matching

This paper presents a new local binocular stereo matching algorithm. A local window with adaptive support weights is used to aggregate matching costs. A more robust matching score is set up combining sum of absolute intensity differences and a gradient based measure. Each pixel has a different disparity search range according to disparity gradient analysis and computational complexity decreases rapidly. Experimental results using the Middlebury stereo test bed demonstrate the validity of our presented approach.

Local stereo matching could deliver accurate disparity maps by the associated method, like adaptive support-weight, but suffers from the high computational complexity, O(NL), where N is pixel count in spatial domain, and L is search range in disparity domain. This paper proposes a fast algorithm that groups similar pixels into super-pixels for spatial reduction, and predicts their search range by simple matching for disparity reduction. The proposed algorithm could be directly applied to other local stereo matching, and reduce its computational complexity to only 8.2%-17.4% with slight 1.5%-3.2% of accuracy degradation.

Stereo matching is the key problem in many stereo vison based 3D applications. One of the factors make local stereo matching time-consuming is that every pixel has the same disparity range as the pre-set one, which should be larger than all possible disparities. An improper pre-set disparity range may lead to redundant computation for some pixels and inadequate computation for some others. In this paper, we propose an accurate and fast local stereo matching method, which employs fine disparity estimation and adaptive cost aggregation. The main contributions of our work include two parts. Firstly, we use phase-based correlation to estimate an initial disparity range for block center pixels. Secondly, we estimate a more limited disparity range for every in-block pixel according to its support weight to the block center pixel. Our disparity estimation and block matching techniques can not only reduce the disparity searching range for every pixel, but also can eliminate some pseudo match pairs. Four standard Middlebury stereo image pairs are tested to evaluate the performance of the proposed algorithm. Experimental results show that the proposed algorithm can reduce the matching time by 37.4% on average with relatively higher accuracy compared to traditional method.

In this study, novel techniques are presented addressing the challenges of stereo matching algorithms for surveillance and vehicle control. For this purpose, one of the most efficient local stereo matching techniques, namely permeability filter, is modified in terms of road plane geometry and temporal consistency in order to take the major challenges of such a scenario into account. Relaxing smoothness assumption of the permeability filter along vertical axis enables extraction of road geometry with high accuracy, even for the cases where ground plane does not contain sufficient textural information. On the other hand, temporal smoothness is enforced by transferring reliable depth assignments against illumination changes, reflections and instant occlusions. According to the extensive experiments on a recent challenging stereo video dataset, the proposed modifications provide reliable disparity maps under severe challenges and low texture distribution, improving scene analyses for surveillance related applications. Although improvements are illustrated for a specific local stereo matching algorithm, the presented specifications and modifications can be applied for the other similar stereo algorithms as well.

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.

In this paper, a novel local stereo matching algorithm is proposed. As is known, finding the adaptively appropriate support region is the key for accurate disparity map. In this algorithm edges are first detected by Canny edge detector and then are used to construct support windows. With left and right consistency check, reliable disparity seeds are detected. And then the disparities of these seeds are propagated to the neighbor pixels with color difference and occlusion constraint. Finally the false matches in the disparity map will be eliminated by the disparity refinement process. Experiments show that accurate disparity maps are obtained from Middlebury data sets and the algorithm is among the best local stereo algorithms. To accelerate the speed of cost aggregation, an applied method is proposed.

基于颜色内相关和自适应支撑权重的立体匹配算法

Video sensor networking technologies have developed very rapidly in the last ten years. In this paper, a cross-based framework strategy for cost aggregation is presented for the depth map estimation based on video sensor networks. We formulate the process as a local regression problem consisting of two main steps with a pair of video sensors. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4%, and 8.3%, when compared with adaptive support weight (ASW) algorithm. Comparing to cross-based algorithm, the proposed algorithm gives 2.0%, 1.3%, and 1.0% in terms of nonocclusion, all, and discontinuities, respectively.

A cross-based framework strategy for performance of gradient-based weight cost aggregation strategy is presented. We formulate the process as a local regression problem consisting of two main steps. The first step is to calculate estimates for a set of points within a shape-adaptive local support region. The second step is to aggregate the matching cost for the gradient-based weight of the support region at the outmost pixel. The proposed algorithm achieves strong results in an efficient manner using the two main steps. We have achieved improvement of up to 6.9%, 8.4% and 8.3%, when compared with Adaptive Support weight (ASW) algorithm. Comparing to Cross-based algorithm, the proposed algorithm gives 2.0%, 1.3% and 1.0% in terms of non-occlusion, all and discontinuities, respectively.

GPU-friendly multi-view stereo reconstruction using surfel representation and graph cuts

Stereo matching is one of the research directions of binocular stereo vision system. In this paper, a novel adaptive local stereo matching algorithm is proposed. Firstly, the cost function calculation is redefined according to the gray similarity and the spatial similarity. Then, the data smoothing term is introduced and the penalty coefficient is determined by the local entropy of the image. Finally, the initial disparity map of the cost aggregation is analyzed by using the left-right consistency check and weight median filter to perform disparity refinement. The algorithm uses four international standard stereo image pairs provided by Middlebury website. The experiment shows that the proposed algorithm can get more accurate disparity map.

The misinformation is diffused extensively via Internet forms big barriers to primary-retaining information credibility and productive public communication. We propose a new dual component approach which is innovative combines neural architecture based on BERT with customized rule-based analysis. None of the above is complete, as this paper aims to solve the fake news identification problem using a driven analysis. There are 47 deception markers with our framework, as well 15 authenticity indications using a weighted system: Pfinal=0.3 ×Prules+ 0.7 ×PBERTwhich is 91% classification precision and yet processing speeds of less than 400 milliseconds. The design has specialized domain enhancement, real-time web-based assessment, reactivity and real-time user interaction-based learning. Testing across a critically collected collection of 100 various samples indicates improved performance relative to the existing methodologies, and including Athletic content 96 percent precision and 7.8 percent calibration deviation in confidence assessment. Our strategy addresses basic weaknesses in current detection models through providing explainable results, real-time processing speeds and self-bettering algorithms, determining its practicability in the real world information validation applications.

The objective of foreground segmentation is to extract the desired foreground object from input videos. Over the years, there have been significant amount of efforts on this topic. Nevertheless, there still lacks a simple yet effective algorithm that can process live videos of objects with fuzzy boundaries (e.g., hair) captured by freely moving cameras. This paper presents an algorithm toward this goal. The key idea is to train and maintain two competing one-class support vector machines at each pixel location, which model local color distributions for both foreground and background, respectively. The usage of two competing local classifiers, as we have advocated, provides higher discriminative power while allowing better handling of ambiguities. By exploiting this proposed machine learning technique, and by addressing both foreground segmentation and boundary matting problems in an integrated manner, our algorithm is shown to be particularly competent at processing a wide range of videos with complex backgrounds from freely moving cameras. This is usually achieved with minimum user interactions. Furthermore, by introducing novel acceleration techniques and by exploiting the parallel structure of the algorithm, near real-time processing speed (14 frames/s without matting and 8 frames/s with matting on a midrange PC & GPU) is achieved for VGA-sized videos.

A comparison of real-time acousto-optic processors for synthetic aperture radar (SAR) image formation has been performed. These processors take advantage of the high processing speed and large time bandwidth product of acousto-optic devices (AOD's) in combination with the multichannel correlation capability of charge coupled devices (CCD) to form the SAR image in real time. They offer significant size, weight and power consumption advantages compared to conventional optical or digital processors. The required two dimensional matched filtering operation is performed as a series of one dimensional operations. First, the matched filtering is performed in range using an AOD. Then the azimuth correlation is performed using a reference mask and a CCD operating as time delay and integrate correlator. The first operation is performed coherently on each radar pulse return while the second is an incoherent correlation performed over the several pulses required to form the synthetic aperture. Two features common to this type of architecture which might limit their applicability are the presence of unwanted signal-dependent bias terms and the inability to perform true complex processing. Architectures utilizing both spatial carriers and subtraction schemes for elimiminating the unwanted bias terms have been analyzed. Also, multichannel architectures for complex (quadrature) processing have been addressed. In addition to imaging performance, the impact of these approaches on system complexity, real-time processing speed and required component capabilities will also be discussed. Results from both our analysis and the experimental implementation of a selected group of these architectures will be presented.

Changes in blood cell morphology are a primary indicator of serious diseases such as leukemia. In this study, an automated blood cell diagnostic approach is proposed by integrating the latest object detection algorithm, YOLOv11, with the statistical Neyman–Pearson hypothesis. The study utilizes an extended BCCD dataset consisting of 2,000 images (80% training, 10% testing, and 10% validation). The results demonstrate that the YOLOv11 model significantly outperforms the conventional Faster R-CNN algorithm in terms of higher accuracy (95.8% mAP) and real-time processing speed, thereby enhancing the efficiency of clinical diagnostics.