Contour Detection Research Articles

Systematic measuring cortical thickness in tibiae for bio-mechanical analysis

Background and Objective:Measuring the thickness of cortical bone tissue helps diagnose bone diseases or monitor the progress of different treatments. This type of measurement can be performed visually from CAT images by a radiologist or by semi-automatic algorithms from Hounsfield values. This article proposes a mechanism capable of measuring thickness over the entire bone surface, aligning and orienting all the images in the same direction to have comparable references and reduce human intervention to a minimum. The objective is to batch process large numbers of patients’ CAT images obtaining thicknesses profiles of their cortical tissue to be used in many applications. Methods:Classical morphological and Deep Learning segmentation is used to extract the area of interest, filtering and interpolation to clean the bones and contour detection and Signed Distance Functions to measure the cortical Thickness. The alignment of the set of bones is achieved by detecting their longitudinal direction, and the orientation is performed by computing their principal component of the center of mass slice. Results:The method processed in an unattended manner 67% of the patients in the first run and 100% in the second run. The difference in the thickness values between the values provided by the algorithm and the measures done by a radiologist was, on average, 0.25 millimetres with a standard deviation of 0.2. Conclusion:Measuring the cortical thickness of a bone would allow us to prepare accurate traumatological surgeries or study their structural properties. Obtaining thickness profiles of an extensive set of patients opens the way for numerous studies to be carried out to find patterns between bone thickness and the patients’ medical, social or demographic variables.

Contour detection network for zero-shot sketch-based image retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) is a challenging task that involves searching natural images related to a given hand-drawn sketch under the zero-shot scene. The previous approach projected image and sketch features into a low-dimensional common space for retrieval, and used semantic features to transfer the knowledge of seen to unseen classes. However, it is not effective enough to align multimodal features when projecting them into a common space, since the styles and contents of sketches and natural images are different and they are not one-to-one correspondence. To solve this problem, we propose a novel three-branch joint training network with contour detection network (called CDNNet) for the ZS-SBIR task, which uses contour maps as a bridge to align sketches and natural images to alleviate the domain gap. Specifically, we use semantic metrics to constrain the relationship between contour images and natural images and between contour images and sketches, so that natural image and sketch features can be aligned in the common space. Meanwhile, we further employ second-order attention to capture target subject information to increase the performance of retrieval descriptors. In addition, we use a teacher model and word embedding method to transfer the knowledge of the seen to the unseen classes. Extensive experiments on two large-scale datasets demonstrate that our proposed approach outperforms state-of-the-art CNN-based models: it improves by 2.6% on the Sketchy and 1.2% on TU-Berlin datasets in terms of mAP.

A low cost IoT-based Arabic license plate recognition model for smart parking systems

License Plate Recognition is one of the significant enablers that can be utilized in wide range of applications in ITS and smart cities. The proposed design relies on three image processing stages to achieve license plate identification with high accuracy which are pre-processing, segmentation, and character recognition. The canny edge detection method with various thresholds, contour detection, and masking techniques are used to locate the car edges and license plate. In the experiment presented in this paper, 200 images were used to identify Egyptian car plates. The model successfully identified Arabic license plates with 93% accuracy. A prototype is implemented using ESP32 Cameras and Raspberry-Pi to test the system's performance. Moreover, a database and a website are hosted on the RPi to allow users to search for their car location in the parking lot using the car's full or partial license plate which was saved in database upon detection.

Fully Parallel Homological Region Adjacency Graph via Frontier Recognition

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is O(log(M×N)) , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field.

Anthracnose Disease Detection in Cashew Leaf Using Machine Learning Technique Based on Contour Detection and Principal Component Analysis

Anthracnose Disease Detection in Cashew Leaf Using Machine Learning Technique Based on Contour Detection and Principal Component Analysis

Contour detection with bicubic spline surfaces

ABSTRACT Contour detection has a rich history in multiple fields such as geography, engineering, and earth science. The predominant approach is based on piecewise planar tessellation and now being challenged concerning the extraction of contour objects for non-linear elevation functions, particularly with respect to bicubic spline functions. A storage-efficient method was developed in previous research, but the detection of the complete set of contour objects is yet to be realized. Although intractable, theoretical underpinnings pertinent to curvature resulted in an approach to realize the complete detection of objects. Given a digital elevation model dataset, in this study, a bicubic spline surface function was first determined. Thereafter, candidate initial points on the edges across the region of interest were identified, and the recursive disaggregation of rectangles was repeated if the non-existence of a solution could not be assured. A developed tracking method was then applied. During advancement, other initial points on the same contour curve were identified and eliminated to circumvent duplicate detection. The completeness of the outlets provides analytical tools for elevation and other geographical assessments. Demonstrative experiments included the development of a three-dimensional contour-based network and slope assessments. The latter application transforms the slope analysis type from raster-based to vector-based. Highlights Detection of a complete set of contour objects amenable to bicubic spline surfaces. Small closure inside a single patch is detectable if size exceeds the standard. Curvature & tolerances central to step length adjustment and tangent angle determination. Redundant initial points are identified and eliminated during the tracking process. Various potential applications in addition to geographical elevations.

INFORMATION TECHNOLOGY FOR DETECTING FOREST FIRE CONTOURS USING OPTICAL SATELLITE DATA

The number of forest fires has increased significantly over the past ten years. It indicates that forest area estimates fires are a very urgent task today. The use of satellite-based data simplifies the process of assessing forest fires. The aim is to develop an information tech-nology for automated forest fire contours detection on digital optical satellite datas in con-ditions of non-stationarity and uncertainty based on convolutional neural networks. The most popular tools for forest fire analysis are considered. This work proposed using hotspots to identify all fire and smoke pixels for automated forest fire contour detection. It made it possible to obtain contour polygons of the corresponding areas with various attrib-utes: position, size, etc. The results are tested on Sentinel 2 satellite images of the Ávila re-gion. The proposed method has an overall accuracy of 94.3% for the selection of forest fires.

Location and timestamp-based chip contour detection using LWMG-YOLOv5

In the fab, semiconductor manufacturers often use deep learning approaches for chip contour detection to shorten automated optical inspection to minimize the loss of production costs and lower power consumption in chip contour detection for realizing energy-efficient computing. However, YOLOv5 and GSEH-YOLOv5 models have sacrificed their accuracy to improve the operational speed. MobileNetv3-YOLOv5 model can enhance the accuracy but lacks high-speed operation. Therefore, this study presents a light version of MobileNetv3-YOLOv5 model with ghost convolution, abbreviated LWMG-YOLOv5, to speed up chip contour detection because this architecture can reduce the number of model parameters and computational burden at the same time. As a result, the proposed approach can outperform the other methods by getting a 3.62% speed-up in chip contour detection to gain a better manufacturing advantage in increasing the chip yields by 1.7% and reducing the loss of production costs by 1.83% significantly.

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities.

Background and Objectives: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. Materials and Methods: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. Discussion: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. Conclusions: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system.

An Approach for the Reduction of Unwanted Edges in Contour Detection Based on Local Filtering

In this paper, an approach for the reduction of unwanted edges in contour detection based on local filtering is presented. Our approach can be used as a preprocessing step before contour detection. Also our approach is useful for object recognition based on feature extraction tasks, because many contour detection methods can’t delete all unwanted edges carefully. Our method consists of a computational algorithm that has 7 steps. Including smoothing, edge detection, smoothing, decreasing of pixels, thresholding, local filtering, and mask creation respectively. We use smoothing for adhering neighbor edge pixels and weakening alone edge pixels. So we can amplify the correct edge pixels and attenuate unwanted edge pixels by smoothing the edge image. In local filtering, we use a proposed casual template that determines noisy regions and correct regions and therefore can create a mask matrix that its elements related to mentioned regions. Finally we can use the "mask matrix" for improving contours by using a “And” operator and we ensure final contour that has a few context effect.

Deep Edge Detection Methods for the Automatic Calculation of the Breast Contour.

Breast cancer conservative treatment (BCCT) is a form of treatment commonly used for patients with early breast cancer. This procedure consists of removing the cancer and a small margin of surrounding tissue, while leaving the healthy tissue intact. In recent years, this procedure has become increasingly common due to identical survival rates and better cosmetic outcomes than other alternatives. Although significant research has been conducted on BCCT, there is no gold-standard for evaluating the aesthetic results of the treatment. Recent works have proposed the automatic classification of cosmetic results based on breast features extracted from digital photographs. The computation of most of these features requires the representation of the breast contour, which becomes key to the aesthetic evaluation of BCCT. State-of-the-art methods use conventional image processing tools that automatically detect breast contours based on the shortest path applied to the Sobel filter result in a 2D digital photograph of the patient. However, because the Sobel filter is a general edge detector, it treats edges indistinguishably, i.e., it detects too many edges that are not relevant to breast contour detection and too few weak breast contours. In this paper, we propose an improvement to this method that replaces the Sobel filter with a novel neural network solution to improve breast contour detection based on the shortest path. The proposed solution learns effective representations for the edges between the breasts and the torso wall. We obtain state of the art results on a dataset that was used for developing previous models. Furthermore, we tested these models on a new dataset that contains more variable photographs and show that this new approach shows better generalization capabilities as the previously developed deep models do not perform so well when faced with a different dataset for testing. The main contribution of this paper is to further improve the capabilities of models that perform the objective classification of BCCT aesthetic results automatically by improving upon the current standard technique for detecting breast contours in digital photographs. To that end, the models introduced are simple to train and test on new datasets which makes this approach easily reproducible.

Artificial neural network for cervical abnormalities detection on computed tomography images

<span lang="EN-US">Cervical cancer is the second deadliest after breast cancer in Indonesia. Sundry diagnostic imaging modalities had been used to decide the location and severity of cervical cancer, one among those is computed tomography (CT) Scan. This study handles a CT image dataset consisting of two categories, abnormal cervical images of cervical cancer patients and normal cervix images of patients with other diseases. It focuses on the ability of segmentation and classification programs to localize cervical cancer areas and classify images into normal and abnormal categories based on the features contained in them. We conferred a novel methodology for the contour detection round the cervical organ classified with artificial neural network (ANN) which was employed to categorize the image data. The segmentation algorithm used was a region-based snake model. The texture features of the cervical image area were arranged in the form of gray level co-occurrence matrix (GLCM). Support vector machine (SVM) had been added to determine which algorithm was better for comparison. Experimental results show that ANN model has better receiver operating characteristic (ROC) parameter values than SVM model’s and existing approach’s regarding 96.2% of sensitivity, 95.32% of specificity, and 95.75% of accuracy. </span>

Non-contact clothing anthropometry based on two-dimensional image contour detection and feature point recognition

Developing the technology of estimating human body size from two-dimensional images is the key to realising more digitalization and artificial intelligence in the textile and garment industry. Therefore, this paper is an in-depth study of estimating body sizes from two-dimensional images in a self-collected database of human body samples. First, the artificial thresholds in the Canny edge operator were replaced by adaptive thresholds. The improved Canny edge operator was combined with mathematical morphology so that it could detect a clear and complete single human contour. Then a joint point detection algorithm based on a convolution neural network and human proportion is proposed. It can detect human feature points with different body proportions. Finally, front and side images and manual body measurements of 122 males aged 18–22 years were collected as the human sample database, calculating the length and fit of the girth size. Compared with manual body measurement data, the error of human length and girth size parameters within the national standard range of –1.5 ~ 1.5 cm can reach 91% on average. This study provides an accurate and convenient anthropometric method for digital garment engineering, which can be used for online shopping and garment customization, and has a certain practical value.

Plasma Expansion Characterization of a Vacuum Arc Thruster with Stereo Imaging

In order to support increasingly detailed simulation models in the field of plasma generation and expansion, more experimental data are needed to prove and verify these models. In addition to existing invasive probe methods and spectroscopy, optical diagnostics methods can be employed for this purpose. These can be used to obtain information about the transient behavior of the plasma plume itself. For this purpose, a pseudo stereo imaging system was assembled, which consisted of a biprism and one camera. The presented setup was used to observe the plasma plume expansion of a vacuum arc thruster and to detect how a magnetic nozzle changes the plume expansion behavior of the used thruster. For this, areas were calculated by means of contour detection from both view directions, and a 3D model was reconstructed by computing cross-sectional images. The results show that the magnetic nozzle provides a more uniform formation of the plasma plume, which can be seen by comparing the calculated areas from both directions of observation and is confirmed by the reconstructed 3D models. Thus, the used setup is suitable as an additional diagnostic tool in the future. Despite its simple design, it provides information about the spatial development of a plasma plume. It can serve as a fast and simple verification tool for simulation results, where otherwise complex tomographic setups and reconstructions would be necessary to obtain spatial information. Further measurements are recommended to improve and validate the recording and evaluation process.

Effects of Microwaves on Burning Velocity, UV–VIS-Spectra, and Exhaust Gas Composition of Premixed Propane Flames

Microwaves can interact with the flame’s combustion zone and increase the energy density. This induces changes to the combustion properties by influencing the radical formation and significantly expanding the flame thickness. It is possible to combust low calorific gas mixtures in a stable process without preheating or co-firing with microwave assistance. Furthermore, the hybrid heating of gaseous fuels and electrical energy offers a solution for the improved use of volatile energy sources and the further improvement of the efficiencies of thermal systems. Moreover, microwaves can be used to heat materials internally, and when coupled with external heating, contribute to a more homogeneous process. This can be particularly beneficial in the glass industry and metal processing. However, the literature on basic behaviour of this coupling such as dielectric properties for higher temperature is limited, which prevents a comprehensive evaluation of the application potential. Thus, investigations with a newly designed, externally cooled, and symmetric resonator were conducted. Furthermore, the optimized resonator was improved by installing ports for burner installation and filter elements for exhaust gas probing and optical access. An axisymmetric burner was designed to generate a steady conical laminar premixed flame, which is stabilized on the outlet of a contoured nozzle. Combustion regimes with propane/air were tested within a range of equivalence ratios from 0.9 to 1.5. Experiments were carried out with an initial temperature of 298 K under atmospheric pressure and with microwave inlet power with a range of 200 to 800 W. The optical techniques used in the current survey are based on flame contour detection using OH* chemiluminescence imaging. Additionally, spectrally resolved flame emission measurement techniques were used to monitor excited state species. Significantly increased production rates of OH*, C2*, and CH* radicals were measured when there was an increased microwave power. The amount of NOx and CO were increased by 20% and 55% respectively in the exhaust gas, which was determined experimentally using a probe method. Numerical simulations of the electromagnetic field and its influence on combustion were carried out to confirm these findings. This enhanced reactivity by 22%, resulted in higher burning velocity, and provided knowledge on the emission performance of the combustion process under microwave influence. The numerical electromagnetic simulations using FEKO produced results that are consistent with the experimental results.

The Detection of Yarn Roll's Margin in Complex Background.

Online detection of yarn roll's margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

An effective method for figures and tables detection in academic literature

Figures and tables in scientific articles serve as data sources for various academic data mining tasks. These tasks require input data to be in its entirety. However, existing studies measure the performance of algorithms using the same IoU (Intersection over Union) or IoU-based metrics that are used for natural situations. There is a gap between high IoU and detection entirety in scientific figures and tables detection tasks. In this paper, we demonstrate the existence of this gap and suggest that the leading cause is the detection error in the boundary area. We propose an effective detection method that cascades semantic segmentation and contour detection. The semantic segmentation model adopted a novel loss function to enhance the weights of boundary parts and a categorized dice metric to evaluate the imbalanced pixels in the segmentation result. Under rigorous testing criteria, the method proposed in this paper yielded a page-level F1 of 0.983 exceeding state-of-the-art academic figure and table detection methods. The research results in this paper can significantly improve the data quality and reduce data cleaning costs for downstream applications.

Weakly Supervised Salient Object Detection by Hierarchically Enhanced Scribbles

The performance of salient object detection (SOD) has been significantly advanced by using deep convolutional networks. However, it largely depends on the high cost of pixel-level annotations. To reduce human effort while improving the prediction accuracy, we propose a novel two-phase learning framework. The weakly supervised information in terms of scribbles is provided as initial labels. Then, as the first phase, high-quality pseudo-labels are generated by mapping scribbles onto object/object-part contours. These contour maps are predicted by the hierarchical contour detection algorithm, providing superior accuracy and smoothness. In the second phase, a deep neural network is alternately trained and predicted. The pseudo-labels are refined in an iterated process, where a conditional random field (CRF) model and a filter module are designed to promote the performance. Extensive experiments on five benchmarks show that our framework can achieve comparable results with the state-of-the-art fully and weakly supervised methods.

Water wave optimized nonsubsampled shearlet transformation technique for multimodal medical image fusion

SummaryMedical image fusion has advanced to the point that it is now possible to combine multiple medical images for accurate disease diagnosis and treatment. The state‐of‐art techniques based on spatial and transform domains suffer from different limitations such as low fused image quality, spectral degradation, contrast reduction, low edge information preserving, lack of shift‐invariance, high computational complexity, classification accuracy, and sensitivity to noise. The main motivation of this work is to generate a single image with excellent visual clarity that retains the features of the source images. This article proposes a water wave optimized nonsubsampled shearlet transformation technique (NSST) for multimodal medical image fusion, in which the water wave optimization (WWO) algorithm is used to allocate the weights of the NSST approach's high‐frequency subbands. The NSST approach is primarily used in this work due to its ability to withstand shift‐invariance and its potential to improve the visual clarity of the fused multimodal image by preserving the essential features present in the image's various directions and edges. We combined the NSST technique with the WWO algorithm, which processes the edges, details, and contourlets of medical images using a max selection strategy based on the fitness function, to improve image quality and computational costs. The WWO algorithm is mainly applied to the NSST to minimize the L1 distance between the fused and the source images. Hence to overcome this problem a condition CNN optimized with a hybrid tunicate swarm memetic (TSM) algorithm is used to incorporate both the benefits offered by the condition CNN–TSM algorithm and NSST. The TSM optimized condition CNN architecture is used to preserve the coefficients of the image and improve the perceiving capability of the high‐frequency sub‐bands. An inverse NSST is used for fused frequency sub‐band integration. Finally, the efficiency of the proposed methodology is evaluated in terms of enhanced visual feature quality, edge detection, contour detection, and computational performance.

Quadcopter Drone for Vision-Based Autonomous Target Following

Unmanned aerial vehicles (UAVs) are becoming popular in various applications. However, there are still challenging issues to be tackled, such as effective obstacle avoidance, target identification within a crowd, and specific target tracking. This paper focuses on dynamic target following and obstacle avoidance to realize a prototype of a quadcopter drone to serve as an autonomous object follower. An adaptive target identification system is proposed to recognize the specific target in the complicated background. For obstacle avoidance during flight, we introduce an idea of space detection and use it to develop a so-called contour and spiral convolution space detection (CASCSD) algorithm to evade obstacles. Thanks to the low architecture complexity, it is appropriate for implementation on onboard flight control systems. The target prediction is integrated with fuzzified flight control to fulfill an autonomous target tracker. When this series of technical research and development is completed, this system can be used for applications such as personal security guard and criminal detection systems.