Depth extraction, refinement and confidence estimation from image data
Başlık çevirisi mevcut değil.
- Tez No: 402674
- Danışmanlar: PROF. DR. M. J. T. REINDERS, DR. E. A. HENDRIKS
- Tez Türü: Doktora
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Elektrik ve Elektronik Mühendisliği, Computer Engineering and Computer Science and Control, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2015
- Dil: İngilizce
- Üniversite: Technısche Unıversıteıt Delft (delft Unıversıty Of Technology)
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Özet yok.
Özet (Çeviri)
Depth extraction is one of the important steps of 3D computer vision (CV). Although, it has been researched for many decades and there are variety of methods already that addresses depth extraction, there is no perfect solution that satisfies the needs of all CV algorithms. Stereo vision is implemented in CV as a matching algorithm where an image region in one image is matched to another region in the other image and the disparity between the matches indicates the depth of the region. One of the fundamental issues in stereo matching is the repetition of pattern problem. When there are patterns that are repeated along the search path, stereo algorithms can wronglymatch the searched pattern with its repetition in the other image. We showed that a solution to this problem is to use enhanced features that will distinguish the correct pattern from its repetitions. Therefore, the search space is limited to the pattern itself rather than its repetitions and correct disparities can be found. Although stereo algorithms require many computations, these computations are independent from each other which make effective parallelization of these algorithms on a GPU possible. However, their parallelization efficiency relies highly on their architecture. In order to optimize the performance of stereo algorithms, it is important to consider both their accuracy and their parallelization performance. We showed that certain architectures of stereo matching provide better parallelization capability while providing similar accuracies with other architectures. Another way of measuring the depth of a scene is to use depth sensors. After the release of the Microsoft Kinect, depth sensors have been increasingly used in CV applications. Kinect provides dense and real-time depth measurements of indoor scenes which has sufficient quality for many CV applications. However, its quality is not enough for accurate 3D reconstructions especially on the boundaries of objects. Since there is a mismatch between the RGB and depth map of the Kinect, depth refinement algorithms that consider all of their input depth information as correct, fail to refine depth maps accurately. We showed that, to accurately refine regions around boundaries, refinement algorithms should mark outliers and do the refinement based on the trustworthy part of the depth map. Another fundamental problem of depth sensors including the Kinect is transparent surfaces. On transparent regions, Kinect fail to estimate any depth measurements. Since depth refinement algorithms require sparse depth estimations on a surface in order to estimate the unknown depth, they fail to refine the depth on the transparent surfaces correctly. To fully recover transparent objects on the depth map, we propose to use stereo matching between IR and RGB views of the Kinect in a fully connected energy minimization framework. Our refinement strategy can fully recover transparent objects and it can correct the errors fromKinect measurements and stereo matching estimations. Stereo matching requires distinctive similarity measures to match pixels between two images. Different similarity measures perform differently depending on the noise and texture of the regions. It is important to combine their advantages to increase the accuracy of the matching. To measure which similarity measure performs better than others on a local region, we used stereo confidences. According to the confidence of each measure, multiple measures are adaptively fused. The result of fusion provides more robust and accurate matching compared to any of the fused similarities and any fusion of them with static weights. Finally, we proposed a novel confidence measure for medical image registration based on similar measures from stereo matching confidences. The proposed confidence measure is shown to be correlated with error from expert control points. Besides, our confidencemeasure can indicate the error as a continuous score, on any region of the image.
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