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1.
Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape From Focus (SFF) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in the image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we develop Optimal Composite Depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is constructed by optimally combining the primary information extracted using one/or more focus measures. The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences. Experimental results demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing SFF methods. Moreover, it is found that the heterogeneous function is more effective than homogeneous function.  相似文献   

2.
Shape from focus (SFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. In this paper, the SFF is presented as a combinatorial optimization problem. The proposed algorithm tries to find the combination of pixel frames which produces maximum focus measure computed over pixels lying on those frames. To reduce the high computational complexity, a local search method is proposed. After the estimate of the initial depth map solution of an object, the neighborhood is defined, and an intermediate image volume is generated from the neighborhood. The updated depth map solution is found from the intermediate image volume. This update process of the depth map solution continues until the amount of improvement is negligible. The results of the proposed SFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing SFF methods.  相似文献   

3.
Shape-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) analyzes 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.  相似文献   

4.
A novel technique for three-dimensional depth recovery based on two coaxial defocused images of an object with added pattern illumination is presented. The approach integrates object segmentation with depth estimation. Firstly segmentation is performed by a multiresolution based approach to isolate object regions from the background given the presence of blur and pattern illumination. The segmentation has three sub-procedures: image pyramid formation; linkage adaptation; and unsupervised clustering. These maximise the object recognition capability while ensuring accurate position information. For depth estimation, lower resolution information with a strong correlation to depth is fed into a three-layered neural network as input feature vectors and processed using a Back-Propagation algorithm. The resulting depth model of object recovery is then used with higher resolution data to obtain high accuracy depth measurements. Experimental results are presented that show low error rates and the robustness of the model with respect to pattern variation and inaccuracy in optical settings.  相似文献   

5.
Shape from focus   总被引:14,自引:0,他引:14  
The shape from focus method presented here uses different focus levels to obtain a sequence of object images. The sum-modified-Laplacian (SML) operator is developed to provide local measures of the quality of image focus. The operator is applied to the image sequence to determine a set of focus measures at each image point. A depth estimation algorithm interpolates a small number of focus measure values to obtain accurate depth estimates. A fully automated shape from focus system has been implemented using an optical microscope and tested on a variety of industrial samples. Experimental results are presented that demonstrate the accuracy and robustness of the proposed method. These results suggest shape from focus to be an effective approach for a variety of challenging visual inspection tasks  相似文献   

6.
Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.  相似文献   

7.
在机器视觉疲劳裂纹扩展试验中,为了能够满足对裂纹宏观观察裂纹和准确定位裂纹尖端的要求,需要采用变焦镜头放大和缩小采集图像范围,针对于摄像头变焦后的聚焦问题,提出了摄像头的自动聚焦方法。首先建立图像采集系统,采集裂纹图像至计算机,通过中值滤波对图像预处理,去除噪声干扰,选取裂纹区域作为聚焦窗口,采用Laplace算子法作为清晰度评价函数,并提出一种变步长穷举法进行聚焦搜索。最后设计了以ARM7为核心的摄像头运动控制器。实验表明,所提出的方法能够实现疲劳裂纹扩展试验中摄像头在各种情况下的准确自动聚焦,为下一步精确测量疲劳裂纹扩展参数奠定基础。  相似文献   

8.
Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new Focus Measure for the estimation of a depth map using image focus. This depth map can subsequently be used in techniques and algorithms leading to the recovery of a three-dimensional structure of the object, a requirement of a number of high level vision applications. The proposed Focus Measure has shown robustness in the presence of noise as compared to the earlier Focus Measures. This new Focus Measure is based on an optical transfer function implemented in the Fourier domain. The results of the proposed Focus Measure have shown drastic improvements in estimation of a depth map, with respect to the earlier Focus Measures, in the presence of various types of noise including Gaussian, Shot, and Speckle noises. The results of a range of Focus Measures are compared using root mean square error and correlation metric measures.  相似文献   

9.
深度学习单目深度估计研究进展   总被引:1,自引:0,他引:1       下载免费PDF全文
单目深度估计是从单幅图像中获取场景深度信息的重要技术,在智能汽车和机器人定位等领域应用广泛,具有重要的研究价值。随着深度学习技术的发展,涌现出许多基于深度学习的单目深度估计研究,单目深度估计性能也取得了很大进展。本文按照单目深度估计模型采用的训练数据的类型,从3个方面综述了近年来基于深度学习的单目深度估计方法:基于单图像训练的模型、基于多图像训练的模型和基于辅助信息优化训练的单目深度估计模型。同时,本文在综述了单目深度估计研究常用数据集和性能指标基础上,对经典的单目深度估计模型进行了性能比较分析。以单幅图像作为训练数据的模型具有网络结构简单的特点,但泛化性能较差。采用多图像训练的深度估计网络有更强的泛化性,但网络的参数量大、网络收敛速度慢、训练耗时长。引入辅助信息的深度估计网络的深度估计精度得到了进一步提升,但辅助信息的引入会造成网络结构复杂、收敛速度慢等问题。单目深度估计研究还存在许多的难题和挑战。利用多图像输入中包含的潜在信息和特定领域的约束信息,来提高单目深度估计的性能,逐渐成为了单目深度估计研究的趋势。  相似文献   

10.
Shape from focus (SFF) is one of the optical passive methods for three dimensional (3D) shape recovery of an object from its two dimensional (2D) images. The focus measure plays important role in SFF algorithms. Mostly, conventional focus measures are based on gradient, so their performance is restricted under noisy conditions. Moreover, SFF methods also suffer from loss of focus information due to discreteness. This paper introduces a new SFF method based on principal component analysis (PCA) and kernel regression. The focus values are computed through PCA by considering a sequence of small 3D neighborhood for each object point. We apply unsupervised regression through Nadaraya and Watson Estimate (NWE) on depth values to get a refined 3D shape of the object. It reduces the effect of noise within a small surface area as well as approximates the accurate 3D shape by exploiting the depth dependencies in the neighborhood. Performance of the proposed scheme is investigated in the presence of different types of noises and textured areas. Experimental results demonstrate effectiveness of the proposed approach.  相似文献   

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