Advances in computational stereo

Brown, Myron; Burschka, Darius; Hager, Gregory D.

doi:10.1109/tpami.2003.1217603

Cited by 964 publications

(516 citation statements)

References 61 publications

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“…The main remaining challenge is to build robust systems that perform reliably in practical applications where changes in the environment or imaging setup do not influence the quality of reconstruction. For a detailed perspective on the state-of-the art in computational stereo techniques outside surgery, the reader is referred to two comprehensive reviews of the field up to 2003 (Scharstein and Szeliski, 2002;Brown et al, 2003). For the most recent advances we refer the reader to the Middlebury Stereo Vision 2 repository of data with ground truth and evaluation metrics which has served the community as a baseline for algorithm performance over the past decade.…”

Section: State-of-the-artmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

234

129

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One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilites by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

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Section: State-of-the-artmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

234

129

View full text Add to dashboard Cite

show abstract

“…In the process, there is an important assumption that the corresponding pixels should have similar color values [25]. In other words, it is assumed that the object surface is a Lambertian surface [1,3,36]. In the Lambertian, the color of each 3D point acquired from different cameras will be constant.…”

Section: : Refines Disparity B : Aggregates Cost a : Computes Matchmentioning

confidence: 99%

Visual stereo matching combined with intuitive transition of pixel values

Shin

Kim

Chung

2015

Multimed Tools Appl

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The objective of stereo matching is to find the corresponding pixels from similar two or more images. However, it is difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric factors such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose a novel stereo matching approach that is robust in controlling various radiometric variations such as local and global radiometric variations. We designed a hybrid stereo matching approach using transition of pixel values and data fitting. Transition of pixel values is utilized for the coarse stereo matching stage, and polynomial curve fitting is used for the fine stereo matching stage. Experimental results show that the proposed method has better performances compared to the stereo matching algorithms of comparison group under severely different radiometric conditions between stereo images. Consequentially, we demonstrate that the proposed method is less sensitive to various radiometric variations, and shows an outstanding performance in computational complexity.

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“…If the camera axes are parallel and the images have been rectified and corrected for geometric distortions (Brown, 2003), the disparity can be calculated as the difference between the horizontal coordinates of the point in the left and right image:…”

Section: D Scene Sonification Conceptmentioning

confidence: 99%