The multi-frequency heterodyne is a temporal phase unwrapping algorithm that is commonly used in phase-shifting methods. The main principle is that two phase maps with similar periods are differenced to obtain a reference map with a longer period and no ambiguity. However, this method poses a noise problem: the heterodyne procedure leads to the accumulation and amplification of errors in the absolute phase maps, which may result in fringe order jumps. In this paper, a novel multi-phase fusion strategy is proposed, which incorporates correlation constraints in the phase decoding process to reduce the effect of noise. First, a priori constraint on the background illumination and modulation intensity of the multi-frequency fringe pattern is proposed, and a phase optimization function is established by correlating the phase signal models of different frequencies. Since the optimal value of the optimization function is localized, the optimization process is divided into two parts: phase order correction and local phase optimization; then, anomalous phases are identified and denoised according to phase anomalies and the priori condition failures. Finally, the effectiveness of the proposed strategy is verified quantitatively on a standard plane: the SNR of the optimized 4-step phase-shift method is close to that of the 16-step phase-shift method. Furthermore, the strategy also demonstrated a significant reduction in the phase jump problem and an improvement in the SNR of the phase map in 3D PCB measurement experiments.
Key-cap flatness detection after assembly is one of the basic quality control (QC) indexes in computer keyboard manufacturing. A modified machine vision system based on linear structured light imaging for measuring the key-cap flatness is proposed for keyboard QC automation. After a brief introduction of the system design and principle, the pipeline of light stripe image processing, especially the removal of printed letter interference, is studied. First, the staggered reprojection of dense multiline fringes is presented using the pattern editability of the digital light processing projector to replace the conventional three-dimensional (3-D) sensor mechanical scanning and avoid the movement and cumulative error. Second, an adaptive direction operator based on a Hough transform voting is proposed. This operator is used for directional morphological filtering to remove letter noise and solve the issue of printed letter interference on the key-cap surface, thus improving the accuracy and stability of stripe centerline extraction. Finally, the nonlinear least square method is used to fit the 3-D surface of the key-cap and evaluate its flatness efficiently based on the discrete globally distributed 3-D point cloud data. The experimental result demonstrates that the proposed machine vision system can quickly detect keyboard key-cap flatness and shows superior performance to that of the previous work.
Pulmonary fissures are important landmarks for automated recognition of lung anatomy and need to be detected
as a pre-processing step. We propose a derivative of stick (DoS) filter for pulmonary fissures detection in
thoracic CT scans by considering their thin curvilinear shape across multiple transverse planes. Based on a stick
decomposition of a local rectangular neighborhood, a nonlinear derivative operator perpendicular to each stick
is defined. Then, combining with a standard deviation of the intensity along the stick, the composed likelihood
function will take a strong response to fissure-like bright lines, and tends to suppress undesired structures
including large vessels, step edges and blobs. Applying the 2D filter sequentially to the sagittal, coronal and
axial slices, an approximate 3D co-planar constraint is implicitly exerted through the cascaded pipeline, which
helps to further eliminate non-fissure tissues. To generate a clear fissure segmentation, we adopt a connected
component based post-processing scheme, combined with a branch-point finding algorithm to disconnect the
residual adjacent clutters from the fissures. The performance of our filter has been verified in experiments with
a 23 patients dataset, where pathologies to different extents are included. The DoS filter compared favorably
with prior algorithms.
We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures,
removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate
objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure
or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial
for good performance in patient data, containing incomplete and disease-affected fissures.
The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a
median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the
three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main
lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.
In this paper, a structured light system based on synchronous scanning technology is developed for meeting the need of body surface acquisition. The proposed system is composed of a
fixed CCD camera, a fixed structured light projector and a mirror scanner. While the mirror is reflecting light stripes and scene images, the camera acquires a series of body section images from the scanner. After extracting the trace of laser stripes and calculating the relative 3-D coordinate of the illuminated pixels on the series of CCD images, the system can acquire the spatial profile of the
inspected body surface. Moreover, a prototype is developed according to the results from geometrical analysis mentioned above. The experiment data obtained from the scanning system are shown. This synchronized scanning system can be widely applied in the custom design, surgery navigation and the other optical measurement field in the future.
This paper presents a novel method for speckle reduction in ultrasonic images. Firstly, a particular filtering kernel is defined by decomposing the local rectangular neighborhood into asymmetric sticks pointing outside with variable orientation from the investigated pixel. Then the local mean and variance along each stick are calculated using a template based convolution algorithm. Finally, a pseudo-diffusion model is derived to diffuse the intensity averages of sticks into the central pixel, and a variance sensitive conductance functions is designed to adaptively control the diffusion strength in varying directions. The proposed method is in essence an integration of the linear boundary detection operator, i.e. stick technique, and the nonlinear diffusion model. In homogeneous regions, our method will act as a Gaussian like low pass filter, since the sticks are partially overlapped near the center, which implicitly assigns distance dependent weights to neighboring pixels. In heterogeneous regions, the information is expressed as many structures, which often occur as line boundaries or tube shapes in ultrasonic images, then our approach can encourage smoothing along the sticks falling inside the structures, and penalize blurring along the sticks across edges. The performance of our method is verified in experiments of both synthetic and clinical ultrasonic images. The results show that our method outperforms the existed filtering techniques in term of smoothing homogeneous regions, preserving resolvable features, enhancing weak edges and linear structures.
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