Histogram Distance Research Articles

Memory Effect in the Spatial Series Based on Diamond and Graphite Crystals.

To study the relation between the structure of a compound and its properties is one of the fundamental trends in chemistry and materials science. A classic example is the well-known influence of the structures of diamond and graphite on their physicochemical properties, in particular, hardness. However, some other properties of these allotropic modifications of carbon, e.g., fractal properties, are poorly understood. In this work, the spatial series (interatomic distance histograms) calculated using the crystal structures of diamond and graphite are investigated. Hurst exponents H are estimated using detrended fluctuation analysis and power spectral density. The values of H are found to be 0.27–0.32 and 0.37–0.42 for diamond and graphite, respectively. The calculated data suggest that the spatial series have long memory with a negative correlation between the terms of the series; that is, they are antipersistent.

Image matching based on the adaptive redundant keypoint elimination method in the SIFT algorithm

Scale invariant feature transform (SIFT) is one of the most effective techniques in image matching applications. However, it has a main drawback: existing numerous redundant keypoints located very close to each other in the image. These redundant keypoints increase the computational complexity while they decrease the image matching performance. Redundant keypoint elimination method (RKEM)–SIFT are incorporated to eliminate these points by comparing their distances with a fixed experimental threshold value. However, this value has a great impact on the matching results. In this paper, an adaptive RKEM is presented which considers type of the images and distortion thereof, while adjusting the threshold value. Moreover, this value is found separately for the reference and sensed images. In an image, the adaptive RKEM finds the histogram of the keypoints distances, for which the number and the width of the bins are determined based on the number of keypoints and the distances distribution metrics. Then, a maximum value for searching the optimal threshold value is determined. Finally, for each integer value smaller than the mentioned maximum, a set containing distances smaller than that value is created and the one with the smallest variance is selected. The integer value corresponding to that set is chosen as the adaptive threshold for that image. This approach can improve the efficiency of the RKEM-SIFT in eliminating redundant keypoints. Simulation results validated that the proposed method outperforms the SIFT, A2 SIFT and RKEM-SIFT in terms of the matching performance indices.

3D virtual pathohistology of lung tissue from Covid-19 patients based on phase contrast X-ray tomography.

We present a three-dimensional (3D) approach for virtual histology and histopathology based on multi-scale phase contrast x-ray tomography, and use this to investigate the parenchymal architecture of unstained lung tissue from patients who succumbed to Covid-19. Based on this first proof-of-concept study, we propose multi-scale phase contrast x-ray tomography as a tool to unravel the pathophysiology of Covid-19, extending conventional histology by a third dimension and allowing for full quantification of tissue remodeling. By combining parallel and cone beam geometry, autopsy samples with a maximum cross section of 8 mm are scanned and reconstructed at a resolution and image quality, which allows for the segmentation of individual cells. Using the zoom capability of the cone beam geometry, regions-of-interest are reconstructed with a minimum voxel size of 167 nm. We exemplify the capability of this approach by 3D visualization of diffuse alveolar damage (DAD) with its prominent hyaline membrane formation, by mapping the 3D distribution and density of lymphocytes infiltrating the tissue, and by providing histograms of characteristic distances from tissue interior to the closest air compartment.

Offline hand-drawn circuit component recognition using texture and shape-based features

Circuit diagram is the very foundation of electrical and electronic sciences. A circuit diagram consists of various symbols called circuit components that specify the functionality of that circuit. Every day-to-day gadgets that we use are made up with a number of electrical/electronic circuits to play out their particular tasks. Till date circuit designers have to physically enter all data from the hand-drawn circuits into computers, and this procedure requires some investment in terms of time and carries mistakes with high likelihood. To this end, in this paper, we propose a method that relaxes this constraint by introducing a method for recognition of hand-drawn electrical and electronic circuit components, with both analog and digital components included. In the proposed method, the pre-processed images of circuit components are used for training and testing a recognition model using a feature set consisting of a texture based feature descriptor, called histogram of oriented gradients (HOG), and shape based features that include centroid distance, tangent angle, and chain code histogram. In addition, the texture based feature, being large in number compared to others is optimized using a feature selection algorithm called ReliefF. Classification of components is done by using sequential minimal optimization (SMO) classifier. The proposed method has been evaluated on a dataset of 20 different circuit components with 150 samples in each class. The experimental outcome shows that the proposed approach provides average 93.83% accuracy on the present database. We also compare our method with some of the state-of-the-art methods and we see that our method outperforms these methods.

Quantitative assessment of capabilities of colour models for pen ink discrimination in handwritten documents

The addition of new words in handwritten documents such as bank cheques, bills, and notes is considered as common crime. Such immoral activities on handwritten documents have a bad effect on the victim in terms of mental and financial loss. For facilitating an impartial judicial process, it is important to differentiate between the used pen inks. Earlier, image processing and pattern recognition-based techniques for pen ink differentiation gained attention among researchers due to their non-destructive nature. Existing techniques use various colour models to represent the colour of pen inks. Thus, it is crucial to assess the capabilities of various colour models for differentiating pen inks in handwritten documents. In this paper, we propose a histogram distance based quantitative assessment technique for suitable colour model identification for differentiating pen inks. Seven blue and seven black pen ink samples are acquired on 112 cheque leaves. The k-means binarisation is used to identify pen ink pixels. colour model has been identified as the best colour model for this task. The statistical features of the ink colours in the identified colour model representation are extracted and a multi-layer perceptron (MLP) classifier validates the capability of the identified colour model for pen ink discrimination.

Optimization of angular diffractive lenses with extended depth of focus

Conventional refractive lenses concentrate the incident light at focal distance. A narrow beam waist can be achieved by increasing the lens numerical aperture, but strongly reduces the depth of focus. In this work, we explore diffractive lenses designs, with fast angular variation of the focal distance, that produce both a narrow beam waist and a long depth of focus. We predict the focusing properties of the diffractive lenses with a simple analytical model based on an incoherent superposition of standard lenses with different focal distances. The histogram of the local focal distances is used to determine the weights in the superposition. Our model optimizes the shape of the diffractive lenses, in order to extend the depth of focus, which corresponds to the lotus lens. We verify our results with numerical simulations based on Rayleigh–Sommerfeld approach. Experimentally, we validate our analytical and numerical solutions with a spatial light modulator. We have found configurations for the lotus lens where the depth of focus is significantly incremented with only a slight increment of the focal width. For example, we increased the depth of focus from 7.6 mm to 37.2 mm while the beam waist varied from 35.0 microns to 51.6 microns for a lens with diameter D = 4 mm, and focal distance f’ = 125 mm. These results may find applications in the design of contact and intraocular lenses with extended depth of focus, laser focus generators, and imaging applications where extended depth of focus is needed.

Evaluating Onset Times of Acoustic Emission Signals Using Histogram Distances

Determining the onset of a transient signal, for example in seismograms, acoustic emission (AE) signals, or ultrasonic signals is very important in non-destructive process monitoring and geophysics. In some cases, the AE from a malfunction is relatively weak, with a low signal-to-noise ratio. Thus, the signals are often hidden, making it rather difficult to separate them from the background noise. The present work proposes a new method of evaluating onset times based on bin-to-bin histogram distances measured with the Bhattacharyya coefficient. A criterion and a standard procedure for determining an onset are formulated. A key parameter, window length, was discussed in detail. Tests on AE signals from a pencil-lead break, single-grit scratching, and filament breakage reveal the feasibility and effectiveness of the proposed method. This method is believed to be especially appropriate when the emission signal strength of the target malfunction is lower than environmental AE signals generated by other stationary sources. It can also be used as an alternative method for identifying onsets in AE signals in process monitoring and other fields.

Word Replaceability Through Word Vectors

There have been many numerical methods developed recently that try to capture the semantic meaning of words through word vectors. In this study, we present a new way to learn word vectors using only word co-appearances and their average distances. However, instead of claiming semantic or syntactic word representation, we lower our assertions and claim only that we learn word vectors, which express word’s replaceability in sentences based on their Euclidean distances. Synonyms are a subgroup of words which can replace each other, and we will use them to show differences between training on words that appear close to each other in a local window and training that uses distances between words, which we use in this study. Using ConceptNet 5.5.0’s synonyms, we show that word vectors trained on word distances create higher contrast in distributions of word similarities than was done with Glove, where only word appearances close to each other were engaged. We introduce a measure, which looks at intersection of histograms of word distances for synonyms and non-synonyms.

Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis.

Background and purposeRadiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures.Materials and methodsThe DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability.ResultsThe DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95–0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring.ConclusionThe cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.

Fast modeling L2 cache reuse distance histograms using combined locality information from software traces

Abstract To mitigate the performance gap between CPU and the main memory, multi-level cache architectures are widely used in modern processors. Therefore, modeling the behaviors of the downstream caches becomes a critical part of the processor performance evaluation in the early stage of Design Space Exploration (DSE). In this paper, we propose a fast and accurate L2 cache reuse distance histogram model, which can be used to predict the behaviors of the multi-level cache architectures where the L1 cache uses the LRU replacement policy and the L2 cache uses LRU/Random replacement policies. We use the profiled L1 reuse distance histogram and two newly proposed metrics, namely the RST table and the Hit-RDH, that describing more detailed information of the software traces as the inputs. For a given L1 cache configuration, the profiling results can be reused for different configurations of the L2 cache. The output of our model is the L2 cache reuse distance histogram, based on which the L2 cache miss rates can be evaluated. We compare the L2 cache miss rates with the results from gem5 cycle-accurate simulations of 15 benchmarks chosen from SPEC CPU 2006 and 9 benchmarks from SPEC CPU 2017. The average absolute error is less than 5%, while the evaluation time for each L2 configuration can be sped up almost 30× for four L2 cache candidates.

Path histogram distance and complete subtree histogram distance for rooted labelled caterpillars

ABSTRACTA rooted labelled caterpillar (a caterpillar, for short) is a rooted labelled unordered tree transformed to a path after removing all the leaves in it. In this paper, we discuss two histogram distance between caterpillars. One is a path histogram distance as an -distance between the histograms of paths from the root to every leaf and another is a complete subtree histogram distance as an -distance between the histograms of complete subtrees for every node. While the latter is always a metric for general trees, the former is not a metric. In this paper, we show that, for caterpillars, the path histogram distance is always a metric, simply linear-time computable and incomparable with the edit distance. Furthermore, we give experimental results for caterpillars in real data of comparing the path histogram distance and the complete subtree histogram distance with the isolated-subtree distance as the most general tractable variation of the edit distance.

Automated Classification of Manufacturing Process Capability Utilizing Part Shape, Material, and Quality Attributes

Abstract The ability to classify the capabilities of different manufacturing processes based on computer-aided design (CAD) models of parts is a key missing link in cybermanufacturing. In this paper, we present a one-step approach for automatically classifying the capabilities of three discrete manufacturing processes—milling, turning, and casting—based on part shape, quality, and material property attributes. Specifically, our approach utilizes machine learning to classify manufacturing process capabilities of these processes in terms of part shape attributes such as curvature, rotational symmetry, and pairwise surface point distance (D2) histogram computed from CAD models, as well as part quality (surface finish and size tolerance) and material property attributes of parts. In this manner, historical data can be utilized to classify the capabilities of manufacturing processes. We show that it is possible to achieve high classification accuracies—88% and 83% for the training and test data sets, respectively—using this approach. In addition, a key insight gained from this work is that part shape attributes alone are inadequate for discriminating between the capabilities of the manufacturing processes considered. Specifically, the inclusion of material property and part quality attributes enables the classifier to predict viable manufacturing processes that would otherwise be ignored using shape attributes alone. Future extensions of this work will include enriching the classification process with additional attributes such as production cost, as well as alternative classification methods.

The General Shape Analysis applied for coarse classification of teeth shapes

Abstract In the paper we investigate an application of the General Shape Analysis approach to the coarse classification of teeth shapes. The main purpose of the General Shape Analysis is to find one or a few general templates with the highest similarity for an investigated object. This allows to obtain the most basic information about an object and is useful in the case of large amount of data, missing data or when classes are not evident. In this paper various shape description algorithms are applied to the problem of General Shape Analysis, namely the Two-Dimensional Fourier Descriptor, Generic Fourier Descriptor, UNL-Fourier Descriptor, Point Distance Histogram and Zernike Moments. The original description vectors are also investigated after reducing with the use of various approaches, among which are the Principal Component Analysis and Discrete Cosine Transform. We examine some methods using an original database for the General Shape Analysis. Then the selected methods are applied for the recognition of teeth shapes.

Surface Defect Detection using Novel Histogram Distance-based Multiple Template Anomalies Detection Algorithm

Surface defects in manufacturing are top challenges in various manufacturing field including LED manufacturing, die manufacturing and printing industry. Quality control through automated surface defect detection has been an emphasis to speed up the production without jeopardizing the quality of the product. However, complexity and flexibility in product design, specification and dataset availability posted challenges in existing referential-based algorithm. Golden template-based algorithms are sensitive to misalignment and product variations. Deep learning and its variant can be used as non-linear filter to segment anomalies area. However, deep learning requires huge labelled database and consume long learning time. Similarly, maximum likelihood-based algorithms require large database for learning. This research proposes a novel histogram distance based multiple templates anomalies detection (MTAD) algorithm to segment surface defect. Histogram distance based on kernel-wise histograms stacked across illumination normalized database of similar size can describe the degree of anomaly intuitively across the image. Then, surface defect can be justified intuitively according to anomaly heat map generated. The algorithm is tested against industrial samples and it can handle texture and design variation existed in the product while catching anomaly in real time. This research suggests future studies on extending dimensionality of the histogram. Suggested algorithm has wide range of application other than surface defect detection. For examples, video motion detection, decolorization detection on industrial lighting.

A Bilevel Contextual MRF Model for Supervised Classification of High Spatial Resolution Remote Sensing Images

Markov random field (MRF) based methods have been widely used in high spatial resolution (HSR) image classification. However, many existing MRF-based methods put more emphasis on pixel level contexts while less on superpixel level contextual information. To cope with this issue, this article presents a novel bilevel contextual MRF framework, named BLC-MRF, for HSR imagery classification. Specifically, pixel and superpixel level dependence are incorporated into the proposed MRF model to fully exploit spectral–spatial contextual information and preserve object boundaries in HSR images. In BLC-MRF, a pixel level MRF model is first performed and then cascaded as an input of a superpixel level MRF. In superpixel level, unary and pairwise potential terms are constructed by using the superpixel probability estimation method and spectral histogram distance, respectively. At last, a contextual MRF model is conducted and the final classification map can be computed by using $\alpha$ -expansion algorithm. The benefits of BLC-MRF are twofold: first, the pixel and superpixel level contextual information can be exploited under MRF framework to preserve object boundaries for improving the classification performance, and, second, the algorithm can provide promising results with a small number of training samples. Experimental results on three HSR datasets demonstrate that the proposed approach outperforms several state-of-the-art methods in terms of the classification performance.

Improved Distance Metrics for Histogram-Based Device-Free Localization

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. The existing HD-DFL methods use either Kullback–Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.

Generative image deblurring based on multi-scaled residual adversary network driven by composed prior-posterior loss

Conditional Generative Adversarial Networks (CGANs) have been introduced to generate realistic images from extremely degraded inputs. However, these generative models without prior knowledge of spatial distributions has limited performance to deal with various complex scenes. In this paper, we proposed a image deblurring network based on CGANs to generate ideal images without any blurring assumption. To overcome adversarial insufficiency, an extended classifier with different attribute domains is formulated to replace the original discriminator of CGANs. Inspired by residual learning, a set of skip-connections are cohered to transfer multi-scaled spatial features to the following high-level operations. Furthermore, this adversary architecture is driven by a composite loss that integrates histogram of gradients (HoG) and geodesic distance. In experiments, an uniformed adversarial iteration is circularly applied to improve image degenerations. Extensive results show that the proposed deblurring approach significantly outperforms state-of-the-art methods on both qualitative and quantitative evaluations.

Design and Implementation of Autonomous Flower Harvester using Image Processing

In this paper to design and implementation of fully autonomous system which can harvest flowers. The flowers will be able to cut from the plant by using the device proposed in this paper in a perfect condition which will take lesser time for harvesting as compared to the manual harvesting by humans. In recent days, automated flower harvesting is available only for large flowers like tulip. Thus there will be a requirement for harvesting smaller flowers like rose. This procedure will solve this problem and also it is cost efficient then manual harvesting. In this procedure the watershed algorithm is used to detect the flowers. By the histogram distance calculation, the detected flower is compared to the flowers which are already present in the database. If the detected flower matches 70-80%, then there will be the calculation of centroid of the flower and the distance from the centroid at which the stem is to be cut. The robotic arm is provided that will cut the matched flower when the signal has received to it via microcontroller. The project has to establish a cost effective harvesting systems for agricultural purpose.

DyS: A Framework for Mixture Models in Quantification

Quantification is an expanding research topic in Machine Learning literature. While in classification we are interested in obtaining the class of individual observations, in quantification we want to estimate the total number of instances that belong to each class. This subtle difference allows the development of several algorithms that incur smaller and more consistent errors than counting the classes issued by a classifier. Among such new quantification methods, one particular family stands out due to its accuracy, simplicity, and ability to operate with imbalanced training samples: Mixture Models (MM). Despite these desirable traits, MM, as a class of algorithms, lacks a more in-depth understanding concerning the influence of internal parameters on its performance. In this paper, we generalize MM with a base framework called DyS: Distribution y-Similarity. With this framework, we perform a thorough evaluation of the most critical design decisions of MM models. For instance, we assess 15 dissimilarity functions to compare histograms with varying numbers of bins from 2 to 110 and, for the first time, make a connection between quantification accuracy and test sample size, with experiments covering 24 public benchmark datasets. We conclude that, when tuned, Topsøe is the histogram distance function that consistently leads to smaller quantification errors and, therefore, is recommended to general use, notwithstanding Hellinger Distance’s popularity. To rid MM models of the dependency on a choice for the number of histogram bins, we introduce two dissimilarity functions that can operate directly on observations. We show that SORD, one of such measures, presents performance that is slightly inferior to the tuned Topsøe, while not requiring the sensible parameterization of the number of bins.

Minimum distance histograms with universal performance guarantees

We present a data-adaptive multivariate histogram estimator of an unknown density f based on n independent samples from it. Such histograms are based on binary trees called regular pavings (RPs). RPs represent a computationally convenient class of simple functions that remain closed under addition and scalar multiplication. Unlike other density estimation methods, including various regularization and Bayesian methods based on the likelihood, the minimum distance estimate (MDE) is guaranteed to be within an L_1 distance bound from f for a given n, no matter what the underlying f happens to be, and is thus said to have universal performance guarantees (Devroye and Lugosi, Combinatorial methods in density estimation. Springer, New York, 2001). Using a form of tree matrix arithmetic with RPs, we obtain the first generic constructions of an MDE, prove that it has universal performance guarantees and demonstrate its performance with simulated and real-world data. Our main contribution is a constructive implementation of an MDE histogram that can handle large multivariate data bursts using a tree-based partition that is computationally conducive to subsequent statistical operations.