Distance Transform Research Articles

UMAP-DBP: An Improved DNA-Binding Proteins Prediction Method Based on Uniform Manifold Approximation and Projection.

DNA-binding proteins play a vital role in cellular processes. It is an extremely urgent to develop a high-throughput method for efficiently identifying DNA-binding proteins. According to the current research situation, some methods in machine learning and deep learning show excellent computational speed and accuracy, which are worthy of application. In this work, a novel predictor was proposed to predict DNA binding proteins called UMAP-DBP. Firstly, the feature extraction of primary protein sequence was realized based on physicochemical distance transformation, Profile-based auto-cross covariance and General series correlation pseudo amino acid composition. Secondly, uniform manifold approximation and projection (UMAP) and feature importance score methods were used for feature selection; there is a progressive relationship between them. Finally, the Adaboost operation engine with jackknife test were adopted for predicting DNA-binding proteins. For the jackknife test on the BP1075 and BP594, we obtained an overall accuracy of 82.97% and 82.14%, Cohen's kappa (CK) of 0.66 and 0.64, respectively. The results illustrate that a feasible method has been developed for predicting DNA-binding proteins by UMAP and Adaboost. This is the first study in which UMAP has been successfully applied to identify DNA-binding proteins. All the datasets and codes are accessible at https://github.com/Wang-Jinyue/UMAP-DBP .

On the role of distance transformations in Baddeley’s Delta Metric

Comparison and similarity measurement have been a key topic in computer vision for a long time. There is, indeed, an extensive list of algorithms and measures for image or subimage comparison. The superiority or inferiority of different measures is hard to scrutinize, especially considering the dimensionality of their parameter space and their many different configurations. In this work, we focus on the comparison of binary images, and study different variations of Baddeley’s Delta Metric, a popular metric for such images. We study the possible parameterizations of the metric, stressing the numerical and behavioural impact of different settings. Specifically, we consider the parameter settings proposed by the original author, as well as the substitution of distance transformations by regularized distance transformations, as recently presented by Brunet and Sills. We take a qualitative perspective on the effects of the settings, and also perform quantitative experiments on separability of datasets for boundary evaluation.

Cortical Thickness Estimation: A Comparison of FreeSurfer and Three Voxel-Based Methods in a Test-Retest Analysis and a Clinical Application.

The cortical thickness has been used as a biomarker to assess different cerebral conditions and to detect alterations in the cortical mantle. In this work, we compare methods from the FreeSurfer software, the Computational Anatomy Toolbox (CAT12), a Laplacian approach and a new method here proposed, based on the Euclidean Distance Transform (EDT), and its corresponding computational phantom designed to validate the calculation algorithm. At region of interest (ROI) level, within- and inter-method comparisons were carried out with a test-retest analysis, in a subset comprising 21 healthy subjects taken from the Multi-Modal MRI Reproducibility Resource (MMRR) dataset. From the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) data, classification methods were compared in their performance to detect cortical thickness differences between 23 healthy controls (HC) and 45 subjects with Alzheimer's disease (AD). The validation of the proposed EDT-based method showed a more accurate and precise distance measurement as voxel resolution increased. For the within-method comparisons, mean test-retest measures (percentages differences/intraclass correlation/Pearson correlation) were similar for FreeSurfer (1.80%/0.90/0.95), CAT12 (1.91%/0.83/0.91), Laplacian (1.27%/0.89/0.95) and EDT (2.20%/0.88/0.94). Inter-method correlations showed moderate to strong values (R > 0.77) and, in the AD comparison study, all methods were able to detect cortical alterations between groups. Surface- and voxel-based methods have advantages and drawbacks regarding computational demands and measurement precision, while thickness definition was mainly associated to the cortical thickness absolute differences among methods. However, for each method, measurements were reliable, followed similar trends along the cortex and allowed detection of cortical atrophies between HC and patients with AD.

Performance evaluation of SVM-based hand gesture detection and recognition system using distance transform on different data sets for autonomous vehicle moving applications

PurposeThis paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region.Design/methodology/approachGesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on “Jochen Triesch,” “Sebastien Marcel” and “11Khands” data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods.FindingsThis paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on “Jochen Triesch” data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on “Sebastien Marcel” data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on “11Khands” data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on “Jochen Triesch” data set images, 0.71 s as recognition time on “Sebastien Marcel” data set images and 0.22 s as recognition time on “11Khands” data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on “11Khands” data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments.Originality/valueThe modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures.

Segmentación de imágenes con la transformada de distancia rápida y esqueleto morfológico en aplicaciones de sistemas de cultivo de microalgas Raceway

Image segmentation is an important process in digital image analysis, there are several algorithms to carry out this process. In this work, an algorithm little studied in the segmentation of images is defined and that is also applicable in the processing of videos of biological systems, especially, the cultivation of microalgae in Raceway bioreactors. This algorithm is known as the Fast Distance Transform (FDT) and when operating it with the morphological skeleton, an algorithm capable of determining the number of objects in an image and separating them is generated. With the above, it is possible to give individual monitoring, to describe the behaviour of microalgae inside a Raceway bioreactor.

Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps.

Semantic segmentation of room maps is an essential issue in mobile robots’ execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.

A Method for Patterns of Cell-Like Images Based on Distance Transformation

A Method for Patterns of Cell-Like Images Based on Distance Transformation

Automated Papaya Farm Monitoring system using Unmanned Aerial Vehicle (UAV)

Papaya Farm monitoring plays a key role in taking the required early-stage steps to increase the efficiency of harvesting. Using Unmanned aerial vehicles (UAVs) we can increase the efficiency of faming and get maximum yield. Improving performance Unmanned aerial vehicles (UAVs) offer a quick and reliable means of collecting data from difficult to reach large farms. Awareness of the latest usability and technologies used in UAVs and the techniques of programming used to process images taken from UAVs. The approaches used to evaluate the stage of development with available algorithms. To plants, local binary patterns, distance transformation, and watershed segmentation methods are applied to pictures. Using YOLOv4 architecture trine the system to program to detect the plats and find the deficiency in it. this will help to monitor the large farm very easy. Papaya is one of the plants that have the we can find the deficiency of the tree by using monitoring the arial leaf images. at proper care of the plant yielding is can be improved. here with the help of ML (machine learning) the program can be done. using python, we are going to trine the system and gest the result from the program. making the use of available algorithms to modify the performance the get the best result up to loss parentage of 3. The available free were coding yolo is used to performing the task. Here we are using the lasted vision of YOLOv4. that improve the accuracy up to 97%. the increasing the iterations making the result best, here we use 3600 iterations for this project.

Semantic segmentation guided feature point classification and seam fusion for image stitching

Image stitching can be employed to stitch images taken from different times, perspectives, or devices into a panorama with a wider view. However, the imaging specification of images to be stitched is strict. If the imaging specification is not satisfied, artefacts caused by inaccurate alignment and unnatural distortion will occur. Semantic segmentation can solve the classification problem at the pixel level; however, image stitching significantly depends on the accuracy of feature points. Therefore, this paper proposes an image stitching algorithm based on semantic segmentation to guide feature point classification and seam fusion. First, the images are recognized by a cascade semantic segmentation network, and the image feature points are classified. Thereafter, the corresponding homography transformations are calculated using different class feature points, and the best homography mapping for the entire target image is selected. Finally, a seam-cutting algorithm based on semantic segmentation is used to compute the seam, and a feathering Poisson fusion with distance transformation is used to eliminate artefacts and light differences. Experiments show that the algorithm can generate transitional natural and perceptual stitching results even under the influence of perspective and light differences.

Cross-Bands Information Transfer to Offset Ambiguities and Atmospheric Phenomena for Multispectral Data Visualization

Visualization of multispectral images through band selection methods determines an information loss that in utmost cases proves to be critical for the adequate understanding of the represented scene. The R–G–B representation obtained by mapping the visual bands to the R, G, and B channels is highly used due to its great resemblance with the natural color one and aspects perceivable by the human eye. However, despite the similarity in terms of color code, ambiguities between classes such as water and vegetation or atmospheric phenomena like fog, clouds, and smoke that have been penetrated by other bands, remain visible and hinder the process of visualization of the Earth surface. This article presents a set of five different methods to offset the effects caused by ambiguities, fog, light clouds, and smoke by transferring relevant information between bands in order to visually reconstitute those parts of the image affected by atmospheric phenomena. The general concept shared by these methods implies a stacked autoencoder that successfully encompasses the information from all spectral bands into a latent representation used for visualization. Each proposed method is defined by different combination of input and error function formula. Spectral and polar coordinates features represent the possible options for the input, while formulas based on mean squared error or angular spectral distances determine the potential choices in terms of error function definition. The property of angular spectral distance and polar coordinates transformation to obtain illuminant invariant features determined their use in three out of five methods. We evaluate the methods through spectral signature graphical comparison and visual comparison related to the R–G–B representation. We conduct experiments on multiple Sentinel 2 full images.

ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation

Accurate detection and segmentation of multiple sclerosis (MS) brain lesions on magnetic resonance images are important for disease diagnosis and treatment. This is a challenging task as lesions vary greatly in size, shape, location, and image contrast. The objective of our study was to develop an algorithm based on deep convolutional neural network integrated with anatomic information and lesion-wise loss function (ALL-Net) for fast and accurate automated segmentation of MS lesions. Distance transformation mapping was used to construct a convolutional module that encoded lesion-specific anatomical information. To overcome the lesion size imbalance during network training and improve the detection of small lesions, a lesion-wise loss function was developed in which individual lesions were modeled as spheres of equal size. On the ISBI-2015 longitudinal MS lesion segmentation challenge dataset (19 subjects in total), ALL-Net achieved an overall score of 93.32 and was amongst the top performing methods. On the larger Cornell MS dataset (176 subjects in total), ALL-Net significantly improved both voxel-wise metrics (Dice improvement of 3.9% to 35.3% with p-values ranging from p < 0.01 to p < 0.0001, and AUC of voxel-wise precision-recall curve improvement of 2.1% to 29.8%) and lesion-wise metrics (lesion-wise F1 score improvement of 12.6% to 29.8% with all p-values p < 0.0001, and AUC of lesion-wise ROC curve improvement of 1.4% to 20.0%) compared to leading publicly available MS lesion segmentation tools.

Concurrent Optimization of Mountain Railway Alignment and Station Locations With a Three-Dimensional Distance Transform Algorithm Incorporating a Perceptual Search Strategy

The design of railway alignment and station locations involves two intertwined problems, which makes it a complex and time-consuming task. Especially in mountainous regions, the large 3-dimensional (3D) search spaces, complex terrain conditions, coupling constraints and infinite numbers of potential alternatives of this problem pose many challenges. However, most current optimization methods emphasize either alignment optimization or station locations optimization independently. Only a few methods consider coordinated optimization of alignment and stations, but optimize them sequentially. This paper proposes a concurrent optimization method based on a 3-dimensional distance transform algorithm (3D-DT) to solve this problem. It includes the following components: (1) To optimize the location of stations within specified spacing intervals, a novel perceptual search strategy is proposed and incorporated into the basic 3D-DT optimization process. (2) A combined-alignment-station 3D search neighboring mask is developed and employed to search for both the alignment and stations. In order to implement the perceptual process, two additional kinds of 3D reverse perceptual neighboring masks are also developed and employed in the algorithm. (3) Multiple coupling constraints between alignment and stations are also formulated and addressed during the search process. In this study, the effectiveness of the method is verified through a real-world case study in a complex mountainous region. The optimization results show that the proposed method can find high-quality alternatives satisfying multiple coupling constraints.

Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher's Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

Penny-shaped crack simulation with a single high order smooth boundary element

Based on Lagrange interpolation polynomials, the penny-shaped cracks placed on but not limited to flat surface are simulated with a single high order smooth boundary element in the present work. By taking advantage of geometrical features of circular shape such as the symmetry and periodicity, the smoothness within the element is realized by repeated use of real nodes for interpolation in both the radial and circumferential directions of the element using high order shape functions, so that the end node/line effects existing in conventional low order elements have been removed. In the application of the boundary element method (BEM) for crack problems, the hyper-singular integrals are treated with the aid of the technique of shape function manipulations. The near hyper-singular integrals are treated with the tangential distance transformations together with the subdivision techniques. The crack-related parameters including the crack opening displacements (COD), the stresses ahead of crack tip, the stress intensity factors (SIF) and the T-stresses are computed under various loads and compared with the analytical solutions in the numerical examples, showing the accuracy, efficiency and effectiveness of the proposed high order smooth crack element.

Data Balancing Method for Training Segmentation Neural Networks

Data imbalance is a common problem in machine learning and image processing. The lack of training data for the rarest classes can lead to worse learning ability and negatively affect the quality of segmentation. In this paper, we focus on the problem of data balancing for the task of image segmentation. We review major trends in handling unbalanced data and propose a new method for data balancing, based on Distance Transform. This method is designed for using in segmentation convolutional neural networks (CNNs), but it is universal and can be used with any patch-based segmentation machine learning model. The evaluation of the proposed data balancing method is performed on two datasets. The first is medical dataset LiTS, containing CT images of liver with tumor abnormalities. The second one is a geological dataset, containing of photographs of polished sections of different ores. The proposed algorithm enhances the data balance between classes and improves the overall performance of CNN model.

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.

Time-Domain Digital-Coding Active Frequency Selective Surface Absorber/Reflector and Its Imaging Characteristics

Switchable active frequency selective surface (AFSS) absorber/reflector can work in opposite scattering state and be widely applied in the stealth field. At present, abundant researches on material design have been conducted to obtain excellent electromagnetic (EM) properties. However, there are few studies on the modulation effect of switchable AFSS absorber/reflector on the EM wave. In this article, a time-domain digital-coding AFSS absorber/reflector is proposed to manipulate the spectral distribution of harmonic. The accurate control of each harmonic is achieved through AFSS modulating parameters. Then, a simple controller based on FPGA is designed. By coding “0” and “1” elements with controlled sequences, the AFSS can be switched flexibly in both absorbing and reflection states. On this basis, the imaging characteristics of time-domain digital-coding AFSS absorber/reflector are further analyzed, and distance transformation phenomena are described in detail. The synthetic aperture radar (SAR) experiments are performed to demonstrate the required function of distance transformation.

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

DNA-binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo-position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization.

Parallel Raster Scan for Euclidean Distance Transform

Distance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of m×m pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.