KEYWORDS: Phase shifts, Gamma correction, Point clouds, Projection systems, 3D metrology, Cameras, 3D projection, Optical engineering, Error analysis, Complex systems
The fringe projection 3D measurement method is a widely used 3D measurement technology, which utilizes a digital projector as the structural light source. However, the nonlinear intensity response of projectors results in non-sinusoidal fringe images captured by cameras, introducing phase errors that undermine measurement accuracy. We propose a nonlinear correction method based on precise pre-coding for digital fringe projection 3D measurement systems. By encoding only two sets of four-step phase-shifting fringe images into the projector and modeling the relationship among gamma value, pre-coding value, and ideal phase, the algorithm estimates accurate pre-coding values. Experimental validation demonstrates significant reductions in phase error standard deviation post-gamma correction on standard whiteboards, leading to an ∼84% increase in 3D point cloud height accuracy. In addition, the proposed method effectively mitigates the periodic water ripple phenomenon induced by nonlinear gamma effects. Experimental investigations on plaster sculpture and printed circuit board further validate the method’s superiority, achieving higher phase accuracy and point cloud precision, with a measurement accuracy of 0.1 mm.
Polarimetric synthetic aperture radar (PolSAR) images are widely used in the fields of disaster detection and military reconnaissance. PolSAR image classification is one of the most important applications in PolSAR image interpretation. Existing image classification methods based on deep learning usually rely on a large number of labeled training data. Labeling large-scale data sets is expensive and time-consuming Therefore, semi-supervised learning is an important research direction in the field of image classification. Fixmath has achieved excellent semi-supervised classification performance for nature image, so it is introduced into the field of PolSAR image interpretation, and to improve the performance of semi-supervised PolSAR image classification. Fixmatch has the following advantages: (1) It extends the training data, using generated, labeled and unlabeled data to train the network. (2) By utilizing the information of unlabeled data, FixMatch can improve the generalization ability of the classification model, thus allowing the model to better predict on unseen data. Because of these two advantages, FixMatch has shown excellent results in semi-supervised PolSAR image classification. Experiments on two real PolSAR datasets show that the overall accuracy reaches 89.9 % when the number of labeled samples is set to 110, and reaches 98.6 % when the number of labeled samples is set to 1000.
In order to achieve high efficiency, automatic and accurate measurement, the paper takes the two-dimensional measurement of industrial glass under the experimental conditions. The main contents of this paper includes: Analyzing the structure and hardware performance parameters of the system, building a measuring platform including computer, Charge-coupled Device image sensor, lens, etc., using high-precision camera to take the image of glass, preprocessing of glass image data and acquiring edge information of glass. The system use second filtering method to filter the image and Canny operator to acquire the edge of the industry glass, transforming computer coordinate system into world coordinate system through coordinate transformation method, and finally calculate the two-dimensional size information of industrial glass. The system measures the two-dimensional length and width of polygonal glass, the experimental results show that the measurement method in this paper meet the accuracy requirements of general industrial measurement, and the detection system is feasible.
This paper analyzes the importance of real-time identification of key parts of non-cooperative targets in military applications, and proposes the shortcomings of current target detection algorithms, and a real-time localization algorithm for target points based on TMS320C6678 parallel processing. The algorithm firstly locates the approximate position of the target by down sampling and horizontal vertical projection of the original image, then locates the target key points by polar coordinate transformation and target edge curvature solution, and combines the multi-core of TMS320C6678 DSP to optimize the algorithm. Finally, the algorithm is parallel optimized on the TMS320C6678 multi-core DSP hardware platform, which can accurately locate the target parts within 5ms.
KEYWORDS: Clouds, Reconstruction algorithms, Principal component analysis, 3D modeling, 3D image reconstruction, 3D image processing, Detection and tracking algorithms, 3D displays, Data acquisition, Algorithm development
In order to meet the requirements of 3D reconstruction in accuracy, reconstruction speed and algorithm applicability, this paper proposes a Delaunay growth algorithm based on point cloud curvature smoothing, which firstly projects a 3D discrete point cloud into a 2D plane and passes a 2D Delaunay triangulation. The two-dimensional Delaunay triangulation is performed by the empty circle criterion and the maximum and minimum angle criterion in the score. The PCA principal component analysis is used to estimate the normal of the three-dimensional point cloud and locate the normal on the same side to avoid the disordered points. The cloud normal, combined with the curvature of the corresponding 3D point cloud, removes the invalid normal in the point cloud due to invalid points and preserves the larger part of the point cloud as much as possible, and finally passes the Delaunay constraint criterion and the evaluation function. Filter the set of alternate points to ensure that the reconstructed triangle approximates the Delaunay triangle. The experimental results show that the reconstruction algorithm proposed in this paper is much better than the traditional greedy triangle projection algorithm and Poisson algorithm and the reconstruction speed is increased by 20%.
This paper analyzes the causes of image noise in seawater and the influence of noise on the target image of UUV(unmanned underwater vehicle), and points out the shortcomings of existing methods of noise suppression. In view of the above problems, we propose a real-time noise suppression method for the target image of the UUV platform. The algorithm is divided into three steps: (1) Firstly, the image is binarized by finding an appropriate threshold based on the dispersion between classes. (2) Then, the binary image is subjected to rapid morphological processing to separate the sticky noise. (3) Finally, the target connected domain is calibrated by the four-neighbor method and the pixel values outside the target are gradually reduced based on the principle of human vision to achieve the purpose of noise suppression. Experiments and results show that the method is able to preserve the edge and details of the target well, suppress the noise, and the speed is fast, which satisfies the accuracy and timeliness required for underwater video processing.
The problem of restoring images degraded by an underwater environment is challenging, in part because light traveling underwater suffers from two combined degradations, known as scattering and absorption, which leads to inaccurate transmittance estimation. In this work, we propose that underwater image dehazing and color correction algorithm based on scene depth estimation. Through scene depth estimation, we get accurate transmittance to achieve better dehazing effect. The experimental results show that our approach obtains good-quality images, with a visibility enhancement comparable or better than other recent methods. As for color recovery, We recovere among different images, regardless of the different water conditions. In this work we not only achieves the effect of underwater image dehazing, but also guarantees accuracy and timeliness of recovery results.
Pantograph carbon slide is an important device in power supply system of electric locomotive, the pantograph location is greatly significant for the geometric parameter measurement of the pantograph-catenary system. In order to enhance the adaptability of pantograph detection algorithm to the scene, and to reduce the false rate and missing rate of pantograph detection, this paper proposes a novel method based on the pantograph template for fast matching and horizontal edge detection projection in monocular infrared pantograph images. Firstly, the prior knowledge of the position of the pantograph and the catenary is combined with the template matching method to realize the rough location of the pantograph, and then the precise location of the pantograph by horizontal edge detection and horizontal unilateral projection. The experimental results show that this novel adaptive method realizes the non-contact detection and location of the pantograph effectively, and improve the efficiency significantly.
In the field of glass thickness measurement, the traditional contact manual measurement usually tends to destroy the measured surface, which causes slow speed and low accuracy of measurement. Combining the optical properties of the laser through the glass surface, the paper proposed a novel glass thickness measurement method based on the laser triangulation method. Double line images of the line laser reflected by the up-down surfaces of the glass will form on the CCD through the camera lens. By analyzing the texture features of laser lines in images, gray-centre algorithm is used to extracted the two-dimensional coordinates of laser lines. And three-dimensional point cloud data are derived from laser triangulation formula. The spatial interpolation in specific part of point cloud to calculate the glass thickness. The experimental results show that the proposed method has repeatability and high-precision measurement of the glass thickness.
KEYWORDS: Clouds, Confocal microscopy, Coating, Light emitting diodes, Data acquisition, Distance measurement, Solids, Data conversion, 3D acquisition, 3D image processing
Traditional measuring equipments and methods cannot satisfy the requirements of micrometer-level accuracy and realtime measurement of LED tape coating, the paper proposes a three-dimensional measurement method to compute the thickness of LED tape coating based on linear array spectral confocal. Firstly, the distance data is collected by linear array spectral confocal scanning and converted into 3D point cloud data, then the point cloud is materialized and smoothed to make the 3D object more realistic. Finally, the 3D entity is interacted in the Point Cloud Library to perform manual measurement of the tiny parts of the object. The subsequent automatic measurements are used to control the grating ruler for the specified position moving of measurement based on the previous manual measurement processes and the procedure file. The experimental results indicate that the accuracy of the proposed measurement method is less than 3um, and automatic measurement costs the processing time within 2.5s. In addition, the measurement accuracy is as high as 99.9%, which indicates that the proposed method performs a competitive result.
Blind image deblurring is a challenging problem which has drawn a lot of attention in recent years. Previous work states shows that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. In this paper, we focus on how to extract the suitable salient structure for kernel estimation from a single blurred image. A fast method for estimating the salient structure of an image is proposed in the paper. The image is divided into two layers with different smoothness, and the local relative smoothness layer eliminates the image structure that adversely affects the kernel estimation. Further kernel estimation using the layer can obtain more accurate results. Substantial experiment shows that our method is effective on some challenging examples.
Fixed-point attack on the key parts of small aerial vehicles is an important means of UAV (Unmanned Aerial Vehicle) countermeasure. Because of the fast speed and flexible attitude of fixed-wing aircraft, the detection accuracy of key points of fixed-wing aircraft in infrared images is low and the speed is slow. This paper presents an improved detection and tracking algorithm based on SVM. Firstly, the detection module extracts the fixed-wing aircraft area by image segmentation, then extracts the characteristics of the fixed-wing aircraft, then uses SVM to judge the flight direction of the fixed-wing aircraft, and then locates the key points according to the direction. The experimental results show that the proposed detection algorithm can process 30 frames per second on the platform of DSP (TSM320C6678), and still achieve a high detection rate (<93%) with very high practical value.
Stripe is a common degradation phenomenon in remote sensing images. The variation-based de-striping method, due to the defect of the model itself, always has an unnecessary influence on the stripe-free area while correcting the stripe, and cannot satisfy some requirements in high-precision quantitative applications or sensitive data processing of remote sensing images. This paper proposes a high-precision stripe correction method, which first detects the position of the stripes, and then uses the interpolation idea to correct the stripe to solve the fidelity problem of the stripe-free area in the de-striping process. We use the rational assumption that the derivative of the real signal in the stripe region (to be repaired) is consistent with the derivative of the observed signal, and then selects cubic Hermite spline interpolation method for de-striping, which can uses the derivative information of the region to be repaired (ie, the derivative information of the stripe region) to overcoming the difficulty of the existing interpolation de-stripe method not being able to work well when the stripes is too wide. The experimental results show that our method can effectively remove the stripes and maintain the stripe-free area intact.
The extraction of Region of Interest (ROI) is an important information guarantee in the application of imaging matching guidance, which directly affects the acquisition probability and matching accuracy of the target. Image segmentation is an important method to extract the Region of Interest of the target. Based on image segmentation algorithm, histogram equalization and morphological filtering, this paper proposes an effective image processing method to extract the Region of Interest of the target. (1) A variety of image threshold segmentation methods are applied to the actual processing flow, and their segmentation performance is compared and analyzed. Some image segmentation methods are obtained, which are suitable for target region extraction in template image preparation and target potential region location in matching recognition. (2) Preliminary localization of visible remote sensing images is carried, using color information, to obtain local regions, then enhance the image using histogram equalization method, finally morphological filtering is used to remove the edge noise. (3) The Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused, and the evaluation indexes such as area constraint, similarity and contrast are filtered to obtain the target region .Tests have been done with visible image and infrared image in this paper. The result indicates that the effectiveness of the morphological filter is more obvious after histogram equalization for the original image. Besides, the Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused to get a more precise Region of Interest, thus ensuring the accuracy and timeliness of imaging matching guidance.
Canny edge detection[1] is a technique to extract useful structural information from different vision objects and dramatically reduce the amount of data to be processed. It has been widely applied in various computer vision systems. There are two thresholds have to be settled before the edge is segregated from background. Usually, by the experience of developers, two static values are set as the thresholds[2]. In this paper, a novel automatic thresholding method is proposed. The relation between the thresholds and Cross-zero Points is analyzed, and an interpolation function is deduced to determine the thresholds. Comprehensive experimental results demonstrate the effectiveness of proposed method and advantageous for stable edge detection at changing illumination.
As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.
In order to detect the invisible leaking gas that is usually dangerous and easily leads to fire or explosion in time, many new technologies have arisen in the recent years, among which the infrared video based gas leak detection is widely recognized as a viable tool. However, all the moving regions of a video frame can be detected as leaking gas regions by the existing infrared video based gas leak detection methods, without discriminating the property of each detected region, e.g., a walking person in a video frame may be also detected as gas by the current gas leak detection methods.To solve this problem, we propose a novel infrared video based gas leak detection method in this paper, which is able to effectively suppress strong motion disturbances.Firstly, the Gaussian mixture model(GMM) is used to establish the background model.Then due to the observation that the shapes of gas regions are different from most rigid moving objects, we modify the Features From Accelerated Segment Test (FAST) algorithm and use the modified FAST (mFAST) features to describe each connected component. In view of the fact that the statistical property of the mFAST features extracted from gas regions is different from that of other motion regions, we propose the Pixel-Per-Points (PPP) condition to further select candidate connected components.Experimental results show that the algorithm is able to effectively suppress most strong motion disturbances and achieve real-time leaking gas detection.
Aiming at the problem that the computational process in the method for image haze removal based on the dark channel prior is too complicated and too time-consuming, we propose a fast method for single image haze removal based on multiscale dark channel prior. In order to solve the problem that it takes too much time on a high resolution image, we choose to optimize the image haze removal based on dark channel prior algorithm by the method of multiple scales. We deal with rough resolution images and use fast minimum filtering and fast guided filtering to speed up the haze removal algorithm. Therefore, the speed of calculation is accelerated while maintaining the good image effect after haze removal.
Slant correction for billet characters is primary and critical step of the recognition of steel billet characters. Character positioning is not accurate when the billet characters are inclined. To solve this problem, this paper presents an algorithm of slant correction for billet characters using height feature of characters. Characters are linearly arranged, using this feature, the angle between the horizontal direction and the base line can be calculated, then the sloping billet characters can be corrected. Experimental results show that the proposed method can correct sloping characters accurately. Compared with the traditional algorithm of slant correction for billet characters, the proposed method can obtain better results.
Embedded steel billet character is low contrast. The uneven illumination distribution and oxidation would affect embedded character detection in images correctly. A novel method based on structured light for embedded character acquisition and extraction is proposed. First the embedded character is irradiated by structured light, embedded character would bend the structured light. The processing algorithm based on Fourier transform, picks up the carrier wave from the reflected image. The reflected image is demodulated and filtered to extract the embedded character. Experimental results show that the algorithm is conciseness and effective. The algorithm has the integrity, is not easy to be interfered by noise. The proposed method has reliability and application.
In the process of steel billets recognition on the production line, the key problem is how to determine the position of the billet from complex scenes. To solve this problem, this paper presents a positioning algorithm based on the feature variance of billet character. Using the largest intra-cluster variance recursive method based on multilevel filtering, the billet characters are segmented completely from the complex scenes. There are three rows of characters on each steel billet, we are able to determine whether the connected regions, which satisfy the condition of the feature variance, are on a straight line. Then we can accurately locate the steel billet. The experimental results demonstrated that the proposed method in this paper is competitive to other methods in positioning the characters and it also reduce the running time. The algorithm can provide a better basis for the character recognition.
Pavement crack detection is affected by much interference in the realistic situation, such as the shadow, road sign, oil stain, salt and pepper noise etc. Due to these unfavorable factors, the exist crack detection methods are difficult to distinguish the crack from background correctly. How to extract crack information effectively is the key problem to the road crack detection system. To solve this problem, a novel method for pavement crack detection based on combining non-negative feature with fast LoG is proposed. The two key novelties and benefits of this new approach are that 1) using image pixel gray value compensation to acquisit uniform image, and 2) combining non-negative feature with fast LoG to extract crack information. The image preprocessing results demonstrate that the method is indeed able to homogenize the crack image with more accurately compared to existing methods. A large number of experimental results demonstrate the proposed approach can detect the crack regions more correctly compared with traditional methods.
KEYWORDS: Point spread functions, Cameras, 3D modeling, 3D image reconstruction, Image restoration, Motion models, 3D acquisition, 3D image processing, Image processing, Motion estimation
With improving of intelligent and automation in modern industrial production area, the detection and reconstruction of the 3D surface of the product has become an important technology, but the image which acquire on the actual production line has motion blur and this problem will affect the later reconstruction work. In order to solve this problem, a deblurring method which based on double view moving target image is proposed in this paper. We can deduce the relationship of the point spread function(PSF) path between the double view image through the epipolar geometry and the camera model. The experimental results show that deblurring with the PSF path solved by the geometric relationship achieves good results.
Blind image deblurring is an important issue. In this paper, we focus on solving this issue by constrained regularization method. Motivated by the importance of edges to visual perception, the edge-enhancing indicator is introduced to constrain the total variation regularization, and the bilateral filter is used for edge-preserving smoothing. The proposed edge enhancing regularization method aims to smooth preferably within each region and preserve edges. Experiments on simulated and real motion blurred images show that the proposed method is competitive with recent state-of-the-art total variation methods.
We present a method to extract edges using zero-crossing feature and contour measure. This method differs markedly from previous ones, since it provided a means of quantitative analysis to detect zero-crossing. There are two main steps in this method. Firstly, the edge intensity was obtained through the value of contour measure. Secondly, the actual edges are identified according to the edges intensity. A series of experiments are performed to test the algorithm proposed, which show that the edges is extracted more accurately and completely.
KEYWORDS: Image segmentation, 3D image reconstruction, 3D acquisition, Reconstruction algorithms, Image processing, Cameras, 3D image processing, Detection and tracking algorithms, Imaging systems, 3D modeling
During the process of three-dimensional vision inspection for products, the target objects under the complex background
are usually immovable. So the desired three-dimensional reconstruction results can not be able to be obtained because of
achieving the targets, which is difficult to be extracted from the images under the complicated and diverse background.
Aiming at the problem, a method of three-dimensional reconstruction based on the graph theoretic segmentation and
multiple views is proposed in this paper. Firstly, the target objects are segmented from obtained multi-view images by
the method based on graph theoretic segmentation and the parameters of all cameras arranged in a linear way are gained
by the method of Zhengyou Zhang calibration. Then, combined with Harris corner detection and Difference of Gaussian
detection algorithm, the feature points of the images are detected. At last, after matching feature points by the triangle
method, the surface of the object is reconstructed by the method of Poisson surface reconstruction. The reconstruction
experimental results show that the proposed algorithm segments the target objects in the complex scene accurately and
steadily. What’s more, the algorithm based on the graph theoretic segmentation solves the problem of object extraction
in the complex scene, and the static object surface is reconstructed precisely. The proposed algorithm also provides the
crucial technology for the three-dimensional vision inspection and other practical applications.
Road crack detection is seriously affected by many factors in actual applications, such as some shadows, road signs, oil
stains, high frequency noise and so on. Due to these factors, the current crack detection methods can not distinguish the
cracks in complex scenes. In order to solve this problem, a novel method based on infrared laser pavement imaging is
proposed. Firstly, single sensor laser pavement imaging system is adopted to obtain pavement images, high power laser
line projector is well used to resist various shadows. Secondly, the crack extraction algorithm which has merged multiple
features intelligently is proposed to extract crack information. In this step, the non-negative feature and contrast feature
are used to extract the basic crack information, and circular projection based on linearity feature is applied to enhance the
crack area and eliminate noise. A series of experiments have been performed to test the proposed method, which shows
that the proposed automatic extraction method is effective and advanced.
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