Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Yuan, Weimin; Cai, Meng; Tong, Xiaoyan; Li, Zhaoxi

doi:10.1016/j.image.2021.116280

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…Almost 70% of noise can be detached and for sure the accuracy can be increased. As a result, the segment-tree [18] is selected to be used at this stage. The segment-tree is a type of image segmentations and this method is efficient to increase the accuracy.…”

Section: Cost Aggregationmentioning

confidence: 99%

Stereo matching algorithm using census transform and segment tree for depth estimation

Azali

Hamzah²,

Noh³

et al. 2023

TELKOMNIKA

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This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively. The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available methods from the benchmark system.

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Section: Cost Aggregationmentioning

confidence: 99%

Stereo matching algorithm using census transform and segment tree for depth estimation

Azali

Hamzah²,

Noh³

et al. 2023

TELKOMNIKA

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“…To be more precise, it's an area of 3840 Â 2880 pixels on the iPhone 13 Pro. Then, we use a process called binning [88,89]. It works as follows: Binning takes a region of 2Â2 pixels, averages the pixel values, and writes back a single pixel.…”

Section: Data Collection Principles and Quality Requirementsmentioning

confidence: 99%

“…The image matching in stereovision [89,[91][92][93][94] is the process of identifying the corresponding points in two images that are cast by the same physical point in the tridimensional space. This can be carried out pixel by pixel or identifying significant features in the images, such as edges, regions or interest points.…”

Section: Image Matching In Stereovision Within Fcm Frameworkmentioning

confidence: 99%

Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Agbossou¹

2023

Advances in Fuzzy Logic Systems

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Cities are increasingly looking to become smarter and more resilient. Also, the use of computer vision takes a considerable place in the panoply of techniques and algorithms necessary for the 3D reconstruction of urban built environments. The models thus obtained make it possible to feed the logic of decision support and urban services thanks to the integration of augmented reality. This chapter describes and uses Fuzzy Cognitive Maps (FCM) as computing framework of visual features matching in augmented urban built environment modeling process. It is a combination of the achievements of the theory of fuzzy subsets and photogrammetry according to an algorithmic approach associated with the ARKit renderer. In this experimental research work, part of which is published in this chapter, the study area was confined to a portion of a housing estate and the data acquisition tools are in the domain of the public. The aim is the deployment of the algorithmic process to capture urban environments built in an augmented reality model and compute visual feature in stereovision within FCM framework. The comparison of the results obtained with our approach to two other well-known ones in the field, denotes the increased precision gain with a scalability factor.

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“…When capturing images for augmented reality, a specific region of the image sensor is utilized, specifically an area of 3840x2880 pixels on the iPhone 13 Pro. To optimize image processing and memory usage, a technique called binning is applied [38,39]. Binning involves averaging the pixel values within a 2x2 pixel region and replacing them with a single pixel.…”

Section: Enhanced Data Collection For Uarm Approachmentioning

confidence: 99%

Urban Augmented Reality for 3D Geosimulation and Prospective Analysis

Agbossou

2023

Applications of Augmented Reality - Current State of the Art

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The advent of augmented reality (AR) has introduced a new era of real-time geosimulation and analysis, particularly in urban planning, spatial design, and architecture. In this chapter, we propose a framework for using urban augmented reality model (UARM) to implement 3D geosimulation and prospective analysis of urban built environments. Our framework leverages advanced technologies, such as computer vision, 3D modeling, and machine learning, to provide a realistic and interactive representation of urban built environments. Using UARM, stakeholders can visualize and analyze the impact of proposed changes to the built environment in real-time. This paper presents the technical specifications and implementation details of our proposed framework and provides case studies demonstrating its effectiveness in urban planning and design. This paper will serve as a guideline for future research in implementation tools for virtual geographic environments (VGE).

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Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Cited by 11 publications

References 25 publications

Stereo matching algorithm using census transform and segment tree for depth estimation

Stereo matching algorithm using census transform and segment tree for depth estimation

Fuzzy Photogrammetric Algorithm for City Built Environment Capturing into Urban Augmented Reality Model

Urban Augmented Reality for 3D Geosimulation and Prospective Analysis

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