Boundary Detection Technique Research Articles

Graphical User Interface-Based Detection of Kidney Stones Using Image Segmentation Techniques

The exponential increase in detrimental surroundings and unhealthy nourishments is causing various health issues in humans. The most destructive effect of such lifestyles is on the kidneys, which cause many kidney diseases, and the most common among them are kidney stones. Kidney stones are a regular but life-threatening disease as they mostly remain unrecognized at the initial stages, leading to an increased threat of end-stage kidney failure. Due to the high recurring rate, Medical Imaging technologies are of paramount importance in detecting this serious public health threat worldwide. This research paper provides a Graphical User Interface for the detection of kidney stones that enables ease of understanding and point-and-click control of the algorithm in MATLAB. The proposed work uses CT scan images to explore image processing techniques due to their reliability and regularity. The algorithm enhances kidney stone screening by improving image quality and focusing on the region of interest. This study highlights the best solutions of imaging techniques to resolve problems like grainy pixels, low-resolution images, and the inaccurate detection of kidney stones due to size and resemblance with nearby parts. All this begins with examining the medical imaging slices from the body area, which later undergoes preprocessing, segmentation, and boundary detection techniques. To check the accuracy of the algorithm, 12 features were extracted using GLCM. Finally, the obtained features were classified using the Classification Designer App in MATLAB. An accuracy of 99.2% was acquired using Ensemble Classifiers. Also, for further progress in the detection of kidney stones in the future, an AI model can be trained so that it can deal with images having different thresholds for better management of the disease.

Optimizing CNN based model for thyroid nodule classification using data augmentation, segmentation and boundary detection techniques

Optimizing CNN based model for thyroid nodule classification using data augmentation, segmentation and boundary detection techniques

A Comparative Study on Appraisal of Lithological Boundaries and Litho-Models Using Well Log Data at Bhogpara Oil Field in Assam of NE India

Accurate assessment of bed boundaries at a drill site is a crucial step for reservoir characterization. Generally, the interpreters in oil industries carry out such analysis through expensive softwares based on their experiences and geological knowledge in the study area. However, identifying bed boundaries using traditional interpretation is tedious. In this study, we adopted a synergistic approach of Walsh transform and a bed boundary demarcation technique to discriminate the lithological boundaries in Bhogpara oil field of Assam in NE India. This bed boundary detection technique was tested over self-potential (SP), gamma ray (GR) and laterolog deep resistivity (LLD) logs. Initially, we detected the possible lithological boundaries from GR log variations and hence proposed a lithological model as per the knowledge of traditional log interpretation techniques. Subsequently, Walsh low pass filter was applied to the self-potential (SP), GR and LLD logs to obtain their step function. Afterward, the step functions of logs were processed through the boundary detection algorithm to find out the possible thick and thin beds. Further, bed correlation was made among the Walsh-detected boundaries for the studied wells of the Bhogpara oil field. Finally, a bed boundary model was generated using the boundaries picked from the Walsh-based approach. Our analysis exhibit that the model generated from Walsh boundaries more explicitly envisaged the possible heterogeneity. Traditionally we discriminated the minimum bed thickness of the order of 5.15 m, whereas, from the Walsh-based approach we obtained thin bed of the order of 3.2 m. This proposed bed boundary assessment technique is also helpful in understanding the position of subsurface rock layers as well as possible thick and thin beds when we don’t have any pre-received core information.

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input of the ADSV, a shots boundary detection technique is used to segment prominent frames. Next, The Light-weight Convolution Neural Network (LWCNN) model receives the segmented frames to extract spatial and temporal information from the intermediate layer. Following that, spatial and temporal features are learned using Long Short-Term Memory (LSTM) cells and Attention Network from a series of frames for each anomalous activity in a sample. To detect motion and action, the LWCNN received chronologically sorted frames. Finally, the anomaly activity in the video is identified using the proposed trained ADSV model. Extensive experiments are conducted on complex and challenging benchmark datasets. In addition, the experimental results have been compared to state-of-the-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.

MRI and CT Image Based Breast Tumor Detection Framework with Boundary Detection Technique

Image segmentation is vital in image processing and computer vision, and it is also regarded as a bottleneck in image processing technology development. Picture segmentation is the process of dividing an image into a group of disjoint sections with uniform and homogeneous characteristics. Before proceeding with various statistical methods of analyzing segmentation of tumor, one has to understand the labels consisting of brain MR image. Because of the high inconstancy in tumor morphology and the low sign to-commotion proportion characteristic to mammography, manual characterization of mammogram yields a critical number of patients being gotten back to, and consequent enormous number of biopsies performed to decrease the danger of missing malignant growth. The convolutional neural networks (CNN) is a mainstream profound learning build utilized in picture arrangement. This procedure has accomplished huge progressions in enormous set picture arrangement challenges in later a long time. In this examination, we had acquired more than 3000 excellent unique mammograms with endorsement from an institutional survey board at the University of Kentucky. Various classifiers dependent on CNNs were manufactured, and every classifier was assessed dependent on its exhibition comparative with truth esteems created by histology results from biopsy furthermore, two-year negative mammogram follow-up affirmed by master. In this paper, a method for classifying traffic signs is proposed that is based on training the convolutional neural networks (CNN). Furthermore, it shows the preliminary classification performance of using this CNN to automatically learn and categorise RGB-D images. For this four-class classification job, the method of transfer learning known as fine tuning technique is proposed which involves reusing layers learnt on the ImageNet dataset to discover the optimal design.

Lagrangian differencing dynamics for incompressible flows

A Lagrangian meshless method is introduced for numerical simulation of flows of incompressible fluids with free surface. Poisson Pressure Equation (PPE) formulation of Navier-Stokes equations is discretised in Lagrangian context using recently introduced spatial operators based on finite differences. The advection adjusts to moving or deforming boundaries, and volume-conservation is enforced by solving a set of geometrical constraints. A technique for detecting the edge or boundary of a point cloud is introduced, which is based on the Laplacian properties for detecting jumps. Since accurate and fast discrete versions of the operator are not straightforward to implement in meshless context, the technique exploits favourable properties of a discrete Laplacian that handles highly irregular arrangements of points. A geometrical scan-sphere test is optionally performed to verify that points are on the point–cloud boundary. The boundary–detection technique is validated on significantly deformed point clouds by artificial numerical experiments, and by simulating unsteady incompressible flows. The method was validated by simulating lid-driven cavity flow, dam-break and water-entry problems, and comparing the kinematics of flow and hydrodynamic loading during the impact. The method reproduced complex free surface shapes, violent fluid-structure interaction, and pressure fields with second-order accuracy.

Geodesic simplex based multiobjective endmember extraction for nonlinear hyperspectral mixtures

This paper presents a novel multiobjective endmember extraction approach for nonlinear hyperspectral mixtures by assuming that the distribution of mixtures conforms to a nonlinear manifold and the endmembers correspond to its extreme points. To identify the endmembers, the approach aims to seek a set of pixels which define a simplex with the maximum volume along the manifold. Meanwhile, several obstacles are properly settled to make it a good performance. First, calculating a simplex’s volume along the manifold needs to calculate the geodesic distance (i.e., the shortest path) between its vertices on the k-nearest neighbor (kNN) graph of the manifold data, but it is time-consuming to go through all the manifold points to search the desired simplex. Therefore, a boundary detection technique is proposed to restrict the identification of endmembers within the boundary points of the manifold to improve the time efficiency. Second, the volume of the geodesic distance based simplex is sensitive to the deviations in the geodesic distance caused by noise. To settle this issue, the multiple regression based noise estimation method is applied due to the high correlation among hundreds of spectral bands. Therefore, the spectral noise can be removed before the calculation of geodesic distance. Third, the number of endmembers is of crucial importance but hard to determine, so it is usually specified beforehand in most unmixing approaches. The proposed approach can instinctively obtain a set of simplices with the maximum volume corresponding to different numbers of endmembers, thus providing more insight for determining the optimal combination of endmembers. In addition, the proposed method is a population based optimization method which is less likely to get trapped into the local optimum. The experiments on synthetic as well as real data sets demonstrate the validity and superiority of the proposed method as compared with the methods of the same type.

Removal of Noise in an Image using Boundary Detection Technique

Image data recorded by devices contain mistakes or noises due to geometry and brightness values of the pixels. These mistakes are rectified using proper mathematical models. Examples of models include contrast and edge enhancement, pseudo-colouring, shadow removal, noise filtering, sharpening, and magnifying. Applying filters to remove a noise may lead to loss of some important data like edges in the image. The proposed work removes the noise from the image using local neighbourhood processing and preserves the edges using boundary-based approach. The noise filtered image is tested against the parameter entropy to validate the result.

Load Position Detection of Container Crane Using Camera

In this study, the authors proposed an image processing algorithm to detect (measure) the rope length of container crane (distance from camera system to container spreader) and sway angle of the spearder (container). This measurement will be the main input to design the anti-sway control system for container cranes. The image processing algorithm includes the main steps: converting from BGR color space to HSV color space, then, binary image is used to extract the marker area. Next, the Canny boundary detection technique is applied to determine the boundary of the markers in the container spreader. The center location of each marker is determined and used to calculate the distance from the camera system to the container spreader is calculated. The rope length accuracy by the image processing algorithm is 99,79%. It is satisfied for crane control purpose.

Land use induced blurring of forest-grassland transition in north-west Himalaya — A case study using Moving Split Window boundary detection technique

Transition zone or ecotone is a unique community sandwiched between two communities/ecosystems/biomes. These ecotones in Himalaya remained unexplored for many ecological aspects like biodiversity, phyto-sociology, boundary detection and even impact of change in land use pattern (anthropogenic activity). The most accepted and widespread technique called as Moving Split Window (MSW) technique is used for detection of vegetation and environmental boundaries at four different sites in the lesser stratum of north-west Himalaya. All the four sites were at different distances from the nearest human inhabited area. Anthropogenic activities like grazing, herb collection, wood collection etc. were common at proximal sites. Such activities have led to the change in land use pattern. In this study, we have tried to work out the impact of the change in land use pattern (human interference) on the vegetation and basic environmental parameters like soil pH, electrical conductivity and moisture on forest-grassland ecotone in north-west Himalaya. Data on mountain steepness was also collected and analyzed. The dissimilarity profile using the statistical tool Squared Euclidian Distance (SED) indicated that species turnover locations increase with the increase in distance of ecotones from human settlements. The ecotones at distant locations from human villages are characterized with blunt as well as sharp peaks for vegetation data, however, conditions are reverse in case of the proximal sites. The study also reveals that as the distance between the ecotone and human settlements increases, the complex conditions like multiple vegetation boundaries prevails on the transitions. In this regard, land use induced blurring of forest-grassland transition in north-west Himalaya is summed up in the study.

An Efficient Shot Boundary Detection Using Data-cube Searching Technique

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

Time-averaged flow characteristics and mixing properties of double-concentric jets

We examined the flow behaviors and mixing characteristics of double-concentric jets using laser-assisted smoke flow visualization method to analyze typical flow patterns and binary boundary detection technique to investigate jet spread width. Time-averaged velocity vectors, streamline patterns, velocity distributions, turbulence properties, and vorticity contours were analyzed using Particle Image Velocimetry (PIV). Topological flow patterns were analyzed to interpret the vortical flow structures. Mixing properties were investigated using a tracer-gas concentration detection method. Four characteristic modes were observed: annular flow dominated mode, transition mode, central jet dominated mode-low shear, and central jet dominated mode-high shear. The jets’ mixing properties were enhanced by two major phenomena: the merging of annular flow and central jet at the centerline and the large turbulence fluctuations produced in the flow field. The merging of the jets induced stagnation points on the central axis in the annular flow dominated mode, which caused reverse flow on the central axis and drastic turbulence fluctuations of the near field region. When the central jet penetrated the recirculation region in the other three modes, the stagnation points on the central axis and the reverse flow vanished. Therefore, the mixing behaviors were prominently enhanced in the annular flow dominated mode.

A Shot boundary Detection Technique based on Visual Colour Information

The paper proposes a novel video segmentation system with maiden application of CIEDE2000 colour-difference and mean luminace pattern. CIEDE2000 colour-difference uses Lab colour space which is a stable and efficient colour space. The main advantage of Lab colour space model is that it can approximate all the available colours perceived by our human eye. CIEDE2000 colour difference is used for detecting abrupt transitions in the video. The novel contribution of the paper is the maiden use of the mean luminance pattern, increasing and decreasing patterns of the mean value of frame luminance, for detecting the gradual transition. The approach is validated on standard databases TRECVid 2001 and 2007 test video database. The performance of the proposed technique is compared with recently reported techniques and found to be superior as compared to other techniques. The accuracy achieved with the proposed method on the standard databases is 95.9% for cut transition, 78.6% for gradual transition and 92.1% overall.

SBD-Duo: a dual stage shot boundary detection technique robust to motion and illumination effect

In this paper, we propose a novel shot boundary detection technique using gradient and colour information. The gradient similarity and luminance distortion are calculated to measure the contrast and structural changes of each frame including luminance changes. In the proposed system, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to extract the possible transitions using an adaptive threshold across the videos. In the verification part, CIEDE2000 colour-difference values of the possible transition frames are compared for declaration of abrupt and gradual transitions. Our system takes effectively less computational time to detect abrupt and gradual transition for a video as compared with contemporary solutions. Our proposed system also gives dominate the performance as compared with latest techniques in terms of F1 score using TRECVid 2001 and 2007 selected dataset. We have performed a series of rigorous experimentation to validate our claims.

A hybrid phase boundary detection technique for two-phase-flow PIV measurements

Particle Image Velocimetry (PIV) measurement accuracy is lower along the phase boundaries of two-phase-flows, because the interrogation windows contain information from both phases. Different seeding density, background intensity, velocity magnitude and flow direction conditions often exist across the boundary, and the cross-correlation-based PIV algorithm selects only the highest correlation peak. The highest correlation peak is either influenced by the wrong phase (across the boundary), or the correctly calculated displacement is erroneously detected as an outlier at a later stage and is subsequently replaced. Phase-separated PIV measurements minimize this problem, and increase accuracy along the boundary by treating each phase separately. This type of measurement requires for each time step; (i) the accurate detection of the phase boundary in consecutive frames, (ii) generation of dynamic phase masks, (iii) an accurate PIV evaluation of each phase and (iv) recombination of the flow fields. In this article, we focus on the first step and test a hybrid phase boundary detection (PBD) technique in three different two-phase-flow configurations which manifest different challenges: The first configuration is the mixing of two liquids in a magnetic micromixer, the second is a combustion experiment where a turbulent, pre-mixed, low-swirl, lifted flame is investigated, and the third is a bubble column reactor where air bubbles are rising in a water tank. The PBD implementation uses a three-step procedure: approximate global thresholding, local Otsu thresholding, and discrimination of image gradients. Comparison of results with and without the use of PBD and phase separation indicate that there are significant measurement accuracy improvements along the boundary.

Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction

Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction.

Ultrasound Deep Learning for Wall Segmentation and Near-Wall Blood Flow Measurement

Studies of medical flow imaging have technical limitations for accurate analysis of blood flow dynamics and vessel wall interaction at arteries. We propose a new deep learning-based boundary detection and compensation (DL-BDC) technique in ultrasound (US) imaging. It can segment vessel boundaries by harnessing the convolutional neural network and wall motion compensation in the analysis of near-wall flow dynamics. The network enables training from real and synthetic US images together. The performance of the technique is validated through synthetic US images and tissue-mimicking phantom experiments. The neural network performs well with high Dice coefficients of over 0.94 and 0.9 for lumens and walls, outperforming previous segmentation techniques. Then, the performance of the wall motion compensation is examined for compliant phantoms. When DL-BDC is applied to flow influenced by wall motion, root-mean-square errors are less than 0.07%. The technique is utilized to analyze flow dynamics and wall interaction with varying elastic moduli of the phantoms. The results show that the flow dynamics and wall shear stress values are consistent with the expected values of the compliant phantoms, and their wall motion behavior is observed with pulse wave propagation. This strategy makes US imaging capable of simultaneous measurement of blood flow and vessel dynamics in human arteries for their accurate interaction analysis. DL-BDC can segment vessel walls fast, accurately, and robustly. It enables to measure the near-wall flow precisely by determining the vessel boundary dynamics. This approach can be beneficial in flow dynamics and wall interaction analyses in various biomedical applications.

A Review on Coverage-Hole Boundary Detection Algorithms in Wireless Sensor Networks

Wireless sensor nodes are extremely useful in different security and surveillance related applications which may be compromised with the existence of coverage-holes. Coverage-hole is the area in a wireless sensor network (WSN) where sensing or transmitting of data is not achievable and therefore, detecting such region is a prominent research topic in WSN. Subsequent to hole-detection, another significant task is to detect the boundary nodesoutlining the coverage-holes. In this paper, we have gone through different research articles describing the various boundary-detection techniques those have been recommended to facilitate hole-detection inside the sensor network. On the basis of the literature review, we have done categorization of the boundary-detection methods, their characteristics, pros & cons and their effect on sensor network’s performance for better understanding and future reference.

A Review on Coverage-Hole Boundary Detection Algorithms in Wireless Sensor Networks

Wireless sensor nodes are extremely useful in different security and surveillance related applications which may be compromised with the existence of coverage-holes. Coverage-hole is the area in a wireless sensor network (WSN) where sensing or transmitting of data is not achievable and therefore, detecting such region is a prominent research topic in WSN. Subsequent to hole-detection, another significant task is to detect the boundary nodesoutlining the coverage-holes. In this paper, we have gone through different research articles describing the various boundary-detection techniques those have been recommended to facilitate hole-detection inside the sensor network. On the basis of the literature review, we have done categorization of the boundary-detection methods, their characteristics, pros & cons and their effect on sensor network’s performance for better understanding and future reference.

A novel automatic shot boundary detection algorithm: robust to illumination and motion effect

Many researches have been done on shot boundary detection, but the performance of shot boundary detection approaches is yet to be addressed for the videos having sudden illumination and object/camera motion effects efficiently. In this paper, a novel dual-stage approach for an abrupt transition detection is proposed which is able to withstand under certain illumination and motion effects. Firstly, an adaptive Wiener filter is applied to the lightness component of the frame to retain some important information on both frequencies and LBP-HF is extracted to reduce the illumination effect. From the experimentation, it is also confirmed that the motion effect is also reduced in the first stage. Secondly, Canny edge difference is used to further remove the illumination and motion effects which are not handled in the first stage. TRECVid 2001 and TRECVid 2007 datasets are applied to analyze and validate our proposed algorithm. Experimental results manifest that the proposed system outperforms the state-of-the-art shot boundary detection techniques.