Neural adaptive stereo matching

Binaghi, Elisabetta; Gallo, Ignazio; Marino, G.; Raspanti, Mario

doi:10.1016/j.patrec.2004.07.001

Cited by 41 publications

(42 citation statements)

References 19 publications

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“…The issue of stereo matching has recruited a variation of computation tools. Advanced computational intelligence techniques are not uncommon and present interesting and promiscuous results (Binaghi et al 2004;.…”

Section: Energy Function I Imentioning

confidence: 99%

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Review of Stereo Vision Algorithms: From Software to Hardware

Nalpantidis

Sirakoulis

Γαστεράτος

2008

International Journal of Optomechatronics

346

159

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Stereo vision, resulting in the knowledge of deep information in a scene, is of great importance in the field of machine vision, robotics and image analysis. In this article, an explicit analysis of the existing stereo matching methods, up to date, is presented. The presented algorithms are discussed in terms of speed, accuracy, coverage, time consumption, and disparity range. Towards the direction of real-time operation, the development of stereo matching algorithms, suitable for efficient hardware implementation is highly desirable. Implementations of stereo matching algorithms in hardware for real-time applications are also discussed in details.

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Section: Energy Function I Imentioning

confidence: 99%

“…Binaghi (Binaghi et al 2004) on the other hand, have chosen to use the zero mean normalized cross correlation (ZNCC) as matching cost. This method integrates a neural network (NN) model, which uses the least-mean-square delta rule for training.…”

Section: Dense Disparity Algorithmsmentioning

confidence: 99%

Review of Stereo Vision Algorithms: From Software to Hardware

Nalpantidis

Sirakoulis

Γαστεράτος

2008

International Journal of Optomechatronics

346

159

View full text Add to dashboard Cite

show abstract

“…3c). In fact, other approaches to measure the displacements could be applied instead of DIC (edge detection, for instance; Binaghi et al 2004). This variation would allow applying this 3D reconstruction method in samples with low general contrast and only appearance of lines indicative of abrupt height changes between relatively flat and contrastless regions.…”

Section: Accuracy and Drawbacks Of The Methodsmentioning

confidence: 99%

On the 3D reconstruction of diatom frustules: a novel method, applications, and limitations

et al. 2015

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Because of the importance of diatoms and the lack of information about their third dimension, a new method for the 3D reconstruction is explored, based on digital image correlation of several scanning electron microscope images. The accuracy of the method to reconstruct both centric and pennate (symmetrical and asymmetrical) diatoms was shown, independently of valve size and shape, and considering not only the general frustule morphology but also the intricate ornamentation. Several measurements were obtained, such as of the surface and projected areas and the valve volume. These results were validated by focused ion beam transverse cross section of one valve, and the quantitative results were compared with geometric models commonly applied. Furthermore, direct volume calculations based on 3D reconstructions have several advantages, such as it has higher taxonomic accuracy than the methods based on light microscopy; it solves the problems of the light halos; it allows the precise measurement of all linear dimensions, including the often neglected third dimension; natural samples can be measured directly; and it provides an exact estimate of cell volume, independently of its shape or alterations due to life cycle stage. This approach provides therefore a simple way to measure the morphological features of diatoms, even at a nanoscale, and can be applied to other microorganisms commonly illustrated by means of scanning electron microscopy.

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“…Binaghi et al raised an advanced method which utilizes zero mean normalized cross correlation i.e. ZNCC metric which combined with the techniques in the field of neural network [13]. The neural network in this method is applied for each support region, depending on the shape and size of each window.…”

Section: The Learning Modelmentioning

confidence: 99%

A Survey of Learning Approaches and Application for 3D Vision

2018

MATEC Web Conf.

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Abstract. Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the twodimensional (2D) images is the most effective topic in computer vision and video surveillance. Threedimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.

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Neural adaptive stereo matching

Cited by 41 publications

References 19 publications

Review of Stereo Vision Algorithms: From Software to Hardware

Review of Stereo Vision Algorithms: From Software to Hardware

On the 3D reconstruction of diatom frustules: a novel method, applications, and limitations

A Survey of Learning Approaches and Application for 3D Vision

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