False Edges Research Articles

Application of machine vision technology in geometric dimension measurement of small parts

In this paper, the on-line detection of small parts’ dimension measurement based on machine vision is designed, and the key technologies, such as image processing, image registration and stitching, edge detection, sub-pixel location analysis, image feature recognition and clustering, and scale measurement based on image involved in the detection of small part dimension are studied. Firstly, based on the actual usage and the characteristics of the algorithm, the feature-based SIFT algorithm was selected to complete the image registration, and the image edge detection algorithm and data processing method were explored. The histogram equalization improves the grayscale distribution of the stitched image and improves the contrast of the image. The median noise filtering algorithm was used to complete the image noise reduction. The false edge was filtered to get the single pixel edge, and the least square method was used to compensate the missing edge pixels and reduce the measurement error. An accurate image registration transformation matrix was obtained. Then, the weighted average fusion algorithm was used to complete the image fusion. The experimental results show that the image stitching algorithm is accurate and effective, and the measurement accuracy of the system meets the performance requirements.

Mapping channel edges in seismic data using curvelet transform and morphological filter

Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors.

A Spectral–Temporal Patch-Based Missing Area Reconstruction for Time-Series Images

Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the similar pixel is independently selected, the improper reference pixel or various accuracies obtained by different land covers causes the problem of salt-and-pepper noise in the reconstructed part of an image. To overcome these problems, this paper presents a spectral–temporal patch (STP)-based missing area reconstruction method for time-series images. First, the STP, the pixels of which have similar spectral and temporal evolution characteristics, is extracted using multi-temporal image segmentation. However, some STP have Missing Observations (STPMO) in the time series, which should be reconstructed. Next, for an STPMO, the most similar STP is selected as the reference STP; then, the mean and standard deviation of the STPMO is predicted using a linear regression method with the reference STP. Finally, the textural information, which is denoted by the spatial configuration of color or intensities of neighboring pixels, is extracted from the clear temporal-adjacent STP and “injected” into the missing area to obtain synthetic cloud-free images. We performed an STP-based missing area reconstruction experiment in Jiangzhou, Chongzuo, Guangxi with time-series images acquired by wide field view (WFV) onboard Chinese Gao Fen 1 on 12 different dates. The results indicate that the proposed method can effectively recover the missing information without salt-and-pepper noise in the reconstructed area; also, the reconstructed part of the image is consistent with the clear part without a false edge. The results confirm that the spectral information from the remaining clear part of the same image and textural information from the temporal-adjacent image can create seamless time-series images.

Finding Maternal Siblings in Birth Registration Data to form a Pregnancy Spine – Data Linkage & Graph Based Methods for Unknown Cluster Sizes

IntroductionWe have developed an innovative methodology to link maternal siblings within 2000 – 2005 England and Wales Birth Registration data, to form a Pregnancy Spine, a unification of all births to each unique mother.
 Key challenges in this many-many linkage scenario:
 
 Blocking (reduction of record pair comparisons)
 Cluster resolution
 
 Objectives and ApproachProbabilistic data linkage (Python) was followed by generation of clusters (using igraph in R) and graph theory community detection techniques.
 To optimise geographical blocking and increase accuracy, we incorporated Internal Migration data to map the likely geographic movement of mothers between births.
 Maternal sibling clusters were modelled as a graph and the structure of clusters was optimised using community detection methods to link, split and evaluate sibling groups.
 Additionally, we incorporated additional childhood statistics data relating to child date of birth to evaluate likely accuracy of sibling pairs and remove false edges (links).
 ResultsOur development has resulted in a new blocking method and cluster resolution method. In addition, we developed new ways to assess and measure the accuracy of sibling groups, beyond traditional classifier metrics, and infer error rates.
 We applied our method to Registration Data used in earlier studies for QA of our methods.
 Using this, and by comparing against other statistics on maternal sibling composition we will present results which show that a high degree of accuracy (precision / recall and new checks) was obtained for precision, recall, and other evaluation metrics.
 Conclusion/ImplicationsThese methods will improve other linkage projects with unknown clusters sizes; for de-duplicating datasets, linkage of multiple datasets, or incorporation of data from a longer time-period through longitudinal linkage.
 To this Spine, researchers can now append and link other data sources to answer questions about maternal and child health outcomes.

Optimal image compression via block-based adaptive colour reduction with minimal contour effect

Current image acquisition devices require tremendous amounts of storage for saving the data returned. This paper overcomes the latter drawback through proposing a colour reduction technique which first subdivides the image into patches, and then makes use of fuzzy c-means and fuzzy-logic-based inference systems, in order to cluster and reduce the number of the unique colours present in each patch, iteratively. The colours available in each patch are quantised, and the emergence of false edges is checked for, by means of the Sobel edge detection algorithm, so as to minimise the contour effect. At the compression stage, a methodology taking advantage of block-based singular value decomposition and wavelet difference reduction is adopted. Considering 35000 sample images from various databases, the proposed method outperforms centre cut, moment-preserving threshold, inter-colour correlation, generic K-means and quantisation by dimensionality reduction.

Investigation on the Geometrical Characteristics of Secondary Arc by Image Edge Detection

The image of secondary arc, which is a common phenomenon in ultrahigh voltage power system, contains a wealthy of information. To determine its geometrical characteristics, the secondary arc image is systematically processed involving gray scale, noise filtering, and image segmentation. To overcome the discontinuous and false edge caused by conventional operators, an improved algorithm is employed; the unrelated edges are fully removed, and the interrupted endpoints are dilated. Based on the results, the component and evolution of secondary arc are discussed; the correlation of the arc luminance and arc current is revealed. Occasionally, the arc roots and arc column have irregular motion behavior. Using the edge-detection technique, the detailed jumping location and velocity of anode arc root are tracked; the life expectancy and stability of coexistence of multicathode arc roots are determined; the morphology of short circuit of arc column is ascertained. Furthermore, the secondary arc dimension is estimated in this paper, including the arc radius and arc length. A number of formulas have been presented to predict the dimension. The obtained results are more accuracy than previous works.

Dissociating the effect of disruptive colouration on localisation and identification of camouflaged targets

Disruptive camouflage features contrasting areas of pigmentation across the animals’ surface that form false edges which disguise the shape of the body and impede detection. In many taxa these false edges feature local contrast enhancement or edge enhancement, light areas have lighter edges and dark areas have darker edges. This additional quality is often overlooked in existing research. Here we ask whether disruptive camouflage can have benefits above and beyond concealing location. Using a novel paradigm, we dissociate the time courses of localisation and identification of a target in a single experiment. We measured the display times required for a stimulus to be located or identified (the critical duration). Targets featured either uniform, disruptive or edge enhanced disruptive colouration. Critical durations were longer for identifying targets with edge enhanced disruptive colouration camouflage even when presented against a contrasting background, such that all target types were located equally quickly. For the first time, we establish empirically that disruptive camouflage not only conceals location, but also disguises identity. This shows that this form of camouflage can be useful even when animals are not hidden. Our findings offer insights into how edge enhanced disruptive colouration undermines visual perception by disrupting object recognition.

An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking

In this paper, we propose an effective directional Bayer color filter array (CFA) demosaicking algorithm based on residual interpolation (RI). The proposed directional interpolation algorithm aims to reduce computational complexity and get more accurate interpolated pixel values in the complex edge areas. We use the horizontal and vertical weights to combine and smooth color difference estimations. Compared with four directional weights in minimized Laplacian residual interpolation, the proposed algorithm not only guarantees the quality of color images but also reduces the computational complexity. Generally, the directional estimations may be inaccurately calculated because of the false edge information in irregular edges. We alleviate it by using a new method to calculate the directional color difference estimations. Experimental results show that the proposed algorithm provides outstanding performance compared with some previous algorithms, especially in the complex edge areas. In addition, it has lower computational complexity and better visual effect.

A quality evaluation model for calculating block and blur effects generated by H.264 and MPEG2 codecs

Many factors can affect and/or compromise the quality of visual media. Most video compression standards use motion compensation and block-based encoding scheme for compression. As a result, the decoded video is altered by one or more compression artifacts, such as block effect, blur, color bleeds, buzzing, false edges, jagged motion and flickering chrominance. In this framework, we present in this paper a quality prediction model for video encoders. Our model is designed to calculate the distortions of blocking and blurring effects, which appeared from the coding operation. We also give an overall score of the two distortions. The validation of our model is done using the subjective video database “LIVE” to correlate with the judgment of the observers. Correlation indicators such as the Spearman rank correlation coefficient (SROCC) and the Pearson linear correlation coefficient (PLCC) indicate that our model gives good performance.

Non-regular homogenous block representation for low bit rate depth coding in 3D video

In multi-view video plus depth (MVD) format based 3D video (3DV) systems, the quality of the rendered virtual views depends highly on the quality of the compressed depth maps. Therefore, depth coding technology is vital for 3DV applications. This paper proposed a superpixel based non-regular homogenous block (NRHB) representation for low bit rate depth map coding. At the encoder side, with the aid of the superpixel segmentation of the associated texture images and the synthesis distortion estimation of the virtual views, the original depth maps are partitioned into several NRHBs and represented with only a small amount of pixels. Then, the NRHB represented depth maps are compressed by using the standard video coding approach afterward. At the decoder side, the depth maps are reconstructed through position mapping of the superpixel segmentation of the decoded texture images, and enhanced by a false edge filter. The experimental results show that the proposed method could impressively increase both the coding efficiency of the depth maps and the rendering quality of the virtual views. It also can be seen that the proposed method significantly reduced the encoding computational complexity for depth maps, which makes it particularly useful for the low bandwidth mobile consumer devices with limited computational resources.

Ship infrared image edge detection based on an improved adaptive Canny algorithm

Influenced by light reflection and water fog interference, ship infrared images are mostly blurred and have low signal-to-noise ratio. In this paper, an improved adaptive Canny edge detection algorithm for infrared image of ship is proposed, which aims to solve the threshold of the traditional Canny cannot be adjusted automatically and the shortcomings of sensitivity to noise. The contrast limited adaptive histogram equalization algorithm is adopted to enhance the infrared image, the morphological filter replaces the Gauss filter to smooth the image, and the OTSU algorithm is utilized to adjust the high and low thresholds dynamically. The experimental results show that the improved Canny algorithm, which can not only improve the contrast of the image and automatically adjust the threshold but also reduce the background sea clutter and false edges, is an effective edge detection method.

Effects of data quality in an animal trade network and their impact on centrality parameters

Dealing with the analysis of animal trade networks always faces the challenge of imperfect data sets mainly due to country borders or different producer communities. In the present study, the network robustness, i.e. the point at which false positive nodes or edges may influence the network structure and the results of the centrality parameters, were analysed for a pork supply chain of a producer community in Northern Germany. The analysis of animal trade networks mainly focusses on disease transmission and the development and implementation of targeted prevention and intervention strategies based on centrality parameters. Here, the inclusion criteria may impact the prediction of disease transmission as well as the outcome of the applied control measures. Thus, four different removal scenarios all based on the boundary specification problem (removal of arcs according to their frequency of appearance, removal of nodes according to their general frequency of appearance and according to their frequency of appearance as supplier or purchaser) were established to analyse the network robustness. In order to evaluate the changes in the rank order of the nodes a Spearman Rank Correlation Coefficient (rs) was calculated between the original network and each removal step. The removal of nodes according to their frequency of appearance showed the most robust results. The values of rs stayed above the threshold of 0.70 for at least a fraction of 80% removed arcs. For the other removal scenarios the centrality parameters under investigation showed various robust results concerning the ranking of the nodes.Therefore, the exclusion of farms that trade infrequently in the network would not be associated with significant change in network structure and centrality parameters. For targeted disease prevention and intervention strategies based on centrality parameters, it is of great relevance to be able to evaluate the influence of inclusion criteria on the network structure and thus on the speed and the extent of possible disease transmission.

Speckle Removal Using Diffusion Potential for Optical Coherence Tomography Images.

We propose a fast and accurate solution to speckle reduction targeted specifically at optical coherence tomography images. The proposed speckle removing filter is designed using a novel potential function based on the gradient of the local variance of intensity. After filtering, the spatially neighboring pixels with close values of intensities converge to uniform gray values, while the edges remain intact. This filtering process results in removal of speckle without destroying the edges of the desired object. The proposed filter also prevents the generation of any false edges. Detailed experimental analysis shows at least 1-dB improvement in the peak signal-to-noise ratio for spectral domain optical coherence tomography images. The method also shows superior edge preservation, contrast, and speed compared to the state of the art in speckle removing filters.

A robust anisotropic edge detection method for carotid ultrasound image processing

A new approach for robust edge detection on B-mode ultrasound images of the carotid artery is proposed in this paper. The proposed method uses anisotropic Gaussian derivative filters along with non-maximum suppression over the overall artery wall orientation in local regions. The anisotropic filters allow using a wider integration scale along the edges while preserving the edge location precision. They also perform edge continuation, resulting in the connection of isolated edge points along linear segments, which is a valuable feature for the segmentation of the artery wall layers. However, this usually results in false edges being detected near convex contours and isolated points. The use of non-maximum suppression over pooled local orientations is proposed to solve this issue. Experimental results are provided to demonstrate that the proposed edge detector outperforms other common methods in the detection of the lumen-intima and media-adventia layer interfaces of the carotid vessel walls. Additionally, the resulting edges are more continuous and precisely located.

Research on Improved Canny Edge Detection Algorithm

Aiming at the poor noise robustness of traditional Canny algorithm and the defect of false edge or edge loss, an edge detection algorithm using statistical algorithm for filtering denoising and using genetic algorithm to determine the optimal high and low threshold of image segmentation is proposed. Firstly, statistical filtering uses mean and variance to denoise, avoiding the problem of Gaussian denoising susceptible to interference in the traditional Canny algorithm, and ensuring the integrity of image edge information. Secondly, this article uses the genetic algorithm, design the crossover operator and genetic operator to modify the evolution of the population, and determine the optimal height threshold of the image edge connection to make the threshold more accurate. Finally, using MATLAB software to simulate, the results show that the improved Canny edge detection algorithm can further improve the anti-noise ability and robustness, and the edge location is more accurate.

Edge detection in Cassini astronomy image using Extreme Learning Machine

Edge detection is often performed on disc-like object in Cassini astronomy images to accurately obtain the object’s center position. The existing edge extraction methods usually produce lots of false edge pixels because of noise and the interior details in disc-like objects. In the paper, an edge detection algorithm based on Extreme Learning Machine (ELM) is proposed for Cassini astronomy images. In the ELM model, a 28-D feature vector of a pixel in Cassini image is constructed as input, which consists of first and second derivatives and some Haar-like features, and a binary classifier is obtained as output that tells if the pixel is in edge. The experimental result shows that its performance is much better than traditional operators. The detected edge is closer to the actual contour. Its average accuracy is 0.9379. The algorithm can be applied to edge detection of disc-like objects in astronomy images.

Multiple hot-deck imputation for network inference from RNA sequencing data

Network inference provides a global view of the relations existing between gene expression in a given transcriptomic experiment (often only for a restricted list of chosen genes). However, it is still a challenging problem: even if the cost of sequencing techniques has decreased over the last years, the number of samples in a given experiment is still (very) small compared to the number of genes. We propose a method to increase the reliability of the inference when RNA-seq expression data have been measured together with an auxiliary dataset that can provide external information on gene expression similarity between samples. Our statistical approach, hd-MI, is based on imputation for samples without available RNA-seq data that are considered as missing data but are observed on the secondary dataset. hd-MI can improve the reliability of the inference for missing rates up to 30% and provides more stable networks with a smaller number of false positive edges. On a biological point of view, hd-MI was also found relevant to infer networks from RNA-seq data acquired in adipose tissue during a nutritional intervention in obese individuals. In these networks, novel links between genes were highlighted, as well as an improved comparability between the two steps of the nutritional intervention. Software and sample data are available as an R package, RNAseqNet, that can be downloaded from the Comprehensive R Archive Network (CRAN). alyssa.imbert@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Probabilistic thresholding of functional connectomes: Application to schizophrenia

Functional connectomes are commonly analysed as sparse graphs, constructed by thresholding cross-correlations between regional neurophysiological signals. Thresholding generally retains the strongest edges (correlations), either by retaining edges surpassing a given absolute weight, or by constraining the edge density. The latter (more widely used) method risks inclusion of false positive edges at high edge densities and exclusion of true positive edges at low edge densities. Here we apply new wavelet-based methods, which enable construction of probabilistically-thresholded graphs controlled for type I error, to a dataset of resting-state fMRI scans of 56 patients with schizophrenia and 71 healthy controls. By thresholding connectomes to fixed edge-specific P value, we found that functional connectomes of patients with schizophrenia were more dysconnected than those of healthy controls, exhibiting a lower edge density and a higher number of (dis)connected components. Furthermore, many participants' connectomes could not be built up to the fixed edge densities commonly studied in the literature (∼5–30%), while controlling for type I error. Additionally, we showed that the topological randomisation previously reported in the schizophrenia literature is likely attributable to “non-significant” edges added when thresholding connectomes to fixed density based on correlation. Finally, by explicitly comparing connectomes thresholded by increasing P value and decreasing correlation, we showed that probabilistically thresholded connectomes show decreased randomness and increased consistency across participants. Our results have implications for future analysis of functional connectivity using graph theory, especially within datasets exhibiting heterogenous distributions of edge weights (correlations), between groups or across participants.

Global Canny algorithm based on Canny edge detector framework in magnetic resonance imaging

Magnetic resonance imaging is an important modality in the diagnosis and pathology detection. Edge detection is used for image segmentation and feature extraction as part of the medical image analysis. There is no ideal and universal algorithm which performs perfectly under all conditions. Conventional Canny edge detector is not suitable to be used in Magnetic resonance images that contaminated by Rician noise. In this paper, we propose the use of customized non-local means into the Canny edge detector instead of Gaussian smoothing in the conventional Canny edge detector to effectively remove Rician noise while preserving edges in Magnetic resonance image of an internal organ. The result shows that our method can yield better edge detection than conventional method, with minimal false edge detection. The proposed method undergoes several attempts of parameter adjustment to detect true edges successfully using optimal parameter setting.

A Dynamic Mode Decomposition Based Edge Detection Method for Art Images

Edge detection is a widely used feature extraction method in various fields, such as image processing, computer vision, machine vision, and so forth. However, it is still a challenging task to extract edges from art images, due to the false edge, shadow, and double lines of art images. In this paper, we propose a dynamic mode decomposition algorithm (DMD) based method for edge detection of art images. This is achieved by proposing a new color space based denoise method to deal with the shadow issue. Then, the false edge and double lines can be resolved by employing DMD method, which can be used to extract sparse features from the denoised images. Here, the sparse features have been enhanced by a new designed eight direction gradient operator. Finally, the effectiveness of our method will be demonstrated through detecting the edges of three classical types of art images (Comic, Oil Painting, and Printmaking).