A Quantitative Evaluation of Confidence Measures for Stereo Vision

Hu, Xiaoyan; Mordohai, Philippos

doi:10.1109/tpami.2012.46

Cited by 209 publications

(25 citation statements)

References 46 publications

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“…This R-measure is based on the Kolmogorov-Smirnov statistic and exploits the Gaussian model of the focus function. A more general R-measure, compatible with any model of the focus function, is proposed in (13). The experiments conducted in section 4.1 illustrate the working principle behind the concept of the R-measure.…”

Section: Discussionmentioning

confidence: 99%

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Reliability measure for shape-from-focus

Pertuz

Puig

García

2013

Image and Vision Computing

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This is the author’s version of a work that was accepted for publication in Journal Image and Vision Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Image and Vision Computing , 31, 10 (2013) DOI: 10.1016/j.imavis.2013.07.005Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyses the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented

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Section: Discussionmentioning

confidence: 99%

“…The problem of estimating the reliability of the focus measure is analogous to confidence estimation in stereo and optical flow [12,13]. In that scope, the aim is to rank depth estimates in stereo vision or flow fields in optical flow according to the likelihood for being correct.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Reliability measure for shape-from-focus

Pertuz

Puig

García

2013

Image and Vision Computing

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“…Both stereoscopic camera calibration and correspondence matching between two rectified images are critical for accuracy in iSV surface reconstruction [39], [40]. Techniques for camera calibration are well studied [39].…”

Section: Methodsmentioning

confidence: 99%

“…2a and 2b). Most algorithms are based on optimization of cross-correlation or its variants (e.g., sum of squared differences) between two windowed sub-images [40]. Here, we employed an optical-flow-based technique to treat the correspondence matching as an unconstrained nonrigid registration to obtain a pixel-level disparity map [41].…”

Section: Methodsmentioning

confidence: 99%

Patient Registration Using Intraoperative Stereovision in Image-guided Open Spinal Surgery

Ji¹,

Fan²,

Paulsen³

et al. 2015

IEEE Trans. Biomed. Eng.

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Despite its widespread availability and success in open cranial neurosurgery, image-guidance technology remains more limited in use in open spinal procedures, in large part because of patient registration challenges. In this study, we evaluated the feasibility of using intraoperative stereovision (iSV) for accurate, efficient and robust patient registration in open spinal fusion surgery. Geometrical surfaces of exposed vertebrae were first reconstructed from iSV. A classical multi-start registration was then executed between point clouds generated from iSV and preoperative CT (pCT) images of the spine. With two pairs of feature points manually identified to facilitate the registration, an average registration accuracy of 1.43 mm in terms of surface-to-surface distance error was achieved in 8 patient cases using a single iSV image pair sampling 2–3 vertebral segments. The iSV registration error was consistently smaller than the conventional landmark approach for every case (average of 2.02 mm with the same error metric). The large capture ranges (average of 23.8 mm in translation and 46.0 deg in rotation) found in the iSV patient registration suggest the technique may offer sufficient robustness for practical application in the operating room. Although some manual effort was still necessary, the manually-derived inputs for iSV registration only needed to be approximate as opposed to be precise and accurate for the manual efforts required in landmark registration. The total computational cost of the iSV registration was 1.5 min on average, significantly less than the typical ~30 min required for the landmark approach. These findings support the clinical feasibility of iSV to offer accurate, efficient and robust patient registration in open spinal surgery, and therefore, its potential to further increase the adoption of image-guidance in this surgical specialty.

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“…It can be computed by several methods such as the sum of absolute differences and census transforms [16,17]. Several comprehensive studies have compared the matching accuracy of various types of matching cost computation methods, and several studies that have found that rank and census transforms are inherently robust to radiometric distortions of images have observed that the census transform outperforms other window-based stereo matching methods [18,19]. Thus, we adopt the census transform, which presents the characteristic feature of a window as a sequence of bit streams [20,21].…”

Section: Stereo Matching Processormentioning

confidence: 99%

The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors

Lee

Shim

et al. 2017

Sensors

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Abstract:Recently, stereo matching processors have been adopted in real-time embedded systems such as intelligent robots and autonomous vehicles, which require minimal hardware resources and low power consumption. Meanwhile, thanks to the through-silicon via (TSV), three-dimensional (3D) stacking technology has emerged as a practical solution to achieving the desired requirements of a high-performance circuit. In this paper, we present the benefits of 3D stacking and process technology scaling on stereo matching processors. We implemented 2-tier 3D-stacked stereo matching processors with GlobalFoundries 130-nm and Nangate 45-nm process design kits and compare them with their two-dimensional (2D) counterparts to identify comprehensive design benefits. In addition, we examine the findings from various analyses to identify the power benefits of 3D-stacked integrated circuit (IC) and device technology advancements. From experiments, we observe that the proposed 3D-stacked ICs, compared to their 2D IC counterparts, obtain 43% area, 13% power, and 14% wire length reductions. In addition, we present a logic partitioning method suitable for a pipeline-based hardware architecture that minimizes the use of TSVs.

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A Quantitative Evaluation of Confidence Measures for Stereo Vision

Cited by 209 publications

References 46 publications

Reliability measure for shape-from-focus

Reliability measure for shape-from-focus

Patient Registration Using Intraoperative Stereovision in Image-guided Open Spinal Surgery

The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors

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