Shape from focus (SFF) is a passive optical method for 3D shape recovery, which has numerous applications in
machine vision, range segmentation, and video microscopy. This paper introduces a new algorithm for shape from
focus (SFF) based on multidimensional scaling (MDS) analysis. In contrast to the conventional focus measures
operators, a three dimensional neighborhood, enabling to capture the effect of pixels from previous as well as
next frames on focus value, is considered for each pixel in the image volume. A similarity matrix is computed
using Euclidean metric for the sequence of these 3D neighborhoods corresponding to each object point. This
matrix is then provided as input to MDS algorithm. The monotonic regression is applied which computes the
fitness of the approximated configuration by using stress function as the criterion for the fitness. The energy of
the components in lower dimensions is employed to compute the best focused point and its corresponding depth.
The proposed method is experimented using synthetic and real image sequences. The evaluation is gauged on
the basis of unimodality and monotonicity of the focus curve. Experimental results have demonstrated the
effectiveness of the new method.
We introduce a new approach for 3-D shape recovery based on discrete wavelet transform (DWT) and principal component analysis (PCA). A small 3-D neighborhood is considered to incorporate the effect of pixels from previous as well as next frames. The intensity values of the pixels in the neighborhood are then arranged into a vector. DWT is applied on each vector to decompose it into approximation and wavelet coefficients. PCA is then applied on modified energies of wavelet components. The first feature in the eigenspace, as it contains maximum variation, is employed to compute the depth. The performance of the proposed approach is tested and is compared with existing methods by using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality and monotonicity of the focus curve. Resolution, accuracy, root mean square error (RMSE), and correlation metrics have been applied to evaluate the performance. Experimental results and comparative analysis demonstrate the effectiveness of the proposed method.
This paper introduces a new approach for 3D shape recovery based on Discrete Wavelet Transform (DWT) and Principal
Component Analysis (PCA). Contrary to computing focus quality locally by summing all values in a 2D or 3D window
obtained after applying a focus measure, a vector consisting of seven neighboring pixels is populated for each pixel in
the image volume. Each vector in the sequence is decomposed by using DWT and then PCA is applied on the energies of
detailed coefficients to transform the data into eigenspace. The first feature, as it contains maximum variation, is
employed to compute the depth. Though DWT and PCA are both computationally expensive transformations, the
reduced data elements and algorithm iterations have made the proposed method efficient. The new approach was
experimented and its performance was compared with other methods by using synthetic and real image sequences. The
evaluation is gauged on the basis of unimodality, monotonicity and resolution of the focus curve. Two other global
statistical metrics Root Mean Square Error (RMSE) and correlation have also been applied for synthetic image sequence.
Experimental results demonstrate the effectiveness and the robustness of the new method.
The objective of 3D shape recovery using focus is to estimate depth map of the scene or object based on best focus points
from camera lens. In Shape From Focus (SFF), the measure of
focus - sharpness - is the crucial part for final 3D shape
estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of
the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for
curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a
new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that
have the same initial depth as of the center pixel and then it
re-adjusts the center value accordingly. Experimental results
show that the proposed technique generates better shape and takes less computation time in comparison to previous SFF
methods based on Focused Image Surface (FIS) and dynamic programming.
This paper presents the use of Genetic Algorithm as a search method for focus measure in Shape From Focus (SFF). Previous methods compute focus value for each pixel locally by summing all values within a small window. This summation is a good approximation of focus quality, but is not optimal one. The Genetic Algorithm is used
as a fine tuning process in which a measure of best focus is used as the fitness function corresponding to motion parameter values which make up each gene. The experimental results show that the proposed method performs better than previous algorithms such as Sum of the Modified Laplacian(SML), Grey Level Variance(GLV) and
Tenenbaum Focus Measure. The results are compared using root mean square error(RMSE) and correlation. The experiments are conducted using objects simulated cone, real cone and TFT-LCD color filter<sup>1</sup> to evaluate performance of the proposed algorithm.