Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical fields.The molecules of chemical compounds are modelled as a graph to study the properties of the compounds.The geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular weight.The article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and graphenylene.The topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological activities.This study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological indices.Also,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

W-系统矩和Fourier变换下Volume Descriptors不变特征的三维模型检索

针对V-系统矩难以构造快速算法的不足,提出一种W-系统矩及其快速算法,并融合volume descriptors不变特征应用于三维模型检索.首先对模型体素化;其次计算体素模型的W-系统矩和物体运动不变量,得到特征向量;最后进行特征融合及匹配.实验结果表明,该算法的检索效率和性能均优于三维球面调和函数方法和三维Legendre矩方法.

A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha

Molecular reconstruction is a rapid and reliable way to provide molecular detail of petroleum fractions,which is required in the kinetic modeling of petroleum conversation processes at the molecular level.In the typical stochastic reconstruction method,the estimation of properties of pseudo molecules that are generated by Monte Carlo sampling depends on the building of predefined molecular libraries,which is expensive and inaccessible for certain petroleum fractions.In this paper,a novel stochastic reconstruction strategy is proposed,which is based on a stratified library of structural descriptors.Properties of pseudo molecules generated in the novel strategy can be directly estimated by group contribution method in the condition of lacking predefined molecular libraries.In this strategy,the molecular building diagram comprises two steps.First,the ring structure is configured by determining the number of rings.Different from the length of chain adopted in the traditional stochastic reconstruction method,in the second step,number of structural descriptors(SDs)for binding site and chain were determined sequentially for the configuration of binding site and saturated acyclic hydrocarbon chain.These structural descriptors for binding site and chain were selected from group contribution methods.To count the number of partial overlapping sections between structural descriptors for chain,two supplementary structural descriptors were created.All possible saturated structures of hydrocarbon chains can be represented by structural descriptors at the scale of property estimation.This strategy separates the building of a predefined molecule library from the stochastic reconstruction process.The exact structures of pseudo molecules represented by structural descriptors in this work can be determined with sufficient chemical knowledge.Fifty naphtha samples are tested independently to demonstrate the performance of the proposed strategy and the results show that the estimated properties were close enough to the experimental values.This strategy will benefit the molecular management of petrochemical industries and therefore improve economic and environmental efficiencies.

Fourier Descriptors with Different Shape Signatures:a Comparative Study for Shape Based Retrieval of Kinematic Constraints

Fourier Descriptors（FD） has been widely used in image analysis and computer vision for shape recognition as they can be made independent of translation,rotation,as well as scaling.They have also been used for developing methods for the analysis and synthesis of four-bar linkages for path generation.This paper focuses on a comparative study of Fourier descriptors derived from various shape signatures of planar closed curves.This includes representations based on Cartesian coordinates,centroid distance,cumulative angle,and curvature.The comparison is conducted not only using commonly used criteria for shape representation and identification but also in the context of shape based retrieval of kinematic constraints for task centered mechanism design.Examples are provided to seek to extract geometric constraints such as circle,circular arc,ellipse and line-segment from a given motion.

A Revisited Definition of the Three Solute Descriptors Related to the Van der Waals Forces in Solutions

It is currently admitted that the intermolecular forces implicated in Gas Liquid Chromatography (GLC) can be expressed as a product of parameters (or descriptors) of solutes and of parameters of solvents. The present study is limited to those of solutes, and among them the three ones are involved in the Van der Waals forces, whereas the two ones involved in the hydrogen bonding are left aside at this stage. These three studied parameters, which we call δ, ω and ε, respectively reflect the three types of Van der Waals forces: dispersion, orientation or polarity strictly speaking, and induction-polarizability. These parameters have been experimentally obtained in previous studies for 121 Volatile Organic Compounds (VOC) via an original Multiplicative Matrix Analysis (MMA) applied to a superabundant and accurate GLC data set. Then, also in previous studies, attempts have been made to predict these parameters via a Simplified Molecular Topology procedure (SMT). Because these last published results have been somewhat disappointing, a promising new strategy of prediction is developed and detailed in the present article.

Adsorption Properties and Quantum Molecular Descriptors of the Folic Acid Drug Adsorbed onto Zigzag and Armchair Single Walled Carbon Nanotubes:DFT Simulations

The adsorption behavior of folic acid onto（5,0） zigzag and（5,5） armchair carbon nanotube（SWCNT） has been investigated using B3 LYP density functional at the 6-31G＊ level. The adsorption energies,molecular orbital analysis and structural changes at the adsorption site are indicative of covalent adsorption on the zigzag SWCNT surface,while the adsorption is physical on the armchair SWCNT surface. The density of states（DOS） Plot and the quantum molecular descriptors（QMD） are witness to the significant changes in the electronic properties of SWCNT systems after the attachment of adsorbed species to the tube surface. According to the calculated results,the single-walled carbon nanotubes are expected to be a potential efficient adsorbent for the adsorption of folic acid drug and also can be used as a suitable drug delivery vehicle within biological systems.

Recognition of 3-D Aircrafts by Fourier Descriptors with Fast and Efficient Library Search

Fourier descriptors are used as features for 3-D aircraft classification and pose determination from a 2-D image recorded at an arbitrary viewing angle. By the feature ranking of Fourier descriptors, a classification procedure based on the fast nearest neighbour rule is proposed to save the matching time of an unknown aircraft with a partial library search. The testing results of some typical examples indicate this method is generally applicable and efficient in 3-D aircraft recognition.

Using three-dimensional discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition

This paper presents a new method for extract three-dimensional （3D） discrete spherical Fourier descriptors based on surface curvature voxels for pollen particle recognition. In order to reduce the high amount of pollen information and noise disturbance, the geometric normalized curvature voxels with the principal curvedness are first extracted to represent the intrinsic pollen volumetric data. Then the curvature voxels are decomposed into radial and angular components with spherical harmonic transform in spherical coordinates. Finally the 3D discrete Fourier transform is applied to the decomposed curvature voxels to obtain the 3D spherical Fourier descriptors for pollen recognition. Experimental results show that the presented descriptors are invariant to different pollen particle geometric transformations, such as pose change and spatial rotation, and can obtain high recognition accuracy and speed simultaneously.

The Use of Fourier Descriptors for the Classification and Analysis of Peripheral Blood Smears Image

The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.

DFT-Based Prediction of Bioconcentration Factors of Polychlorinated Biphenyls in Fish Species Using Molecular Descriptors

Experimental determination of BCFs is expensive and demanding if performed correctly. Because of this, measuring the BCFs of many thousands of chemical substances that are potential regulatory interest is simply not possible. Hence, prediction of BCFs of the PCBs based on QSAR were made time to time to increase the probability of success and reduce the time and cost in exploring the toxicological and ecological characteristics of molecules. DFT methods are, in general, capable of generating a variety of isolated molecular descriptors as well as local reactivity descriptors quite accurately. In this work, prediction of BCFs of the fifty seven PCBs based on quantum chemical descriptors derived from DFT method using the B88-PW91 GGA energy function with the DZVP basis set have been made. The study concluded that dipole moment and ionization potential are reliable descriptors for correlation of bioconcentration factors of polychlorinated biphenyls with their electronic structures. The resulted QSAR model (r2 = 0.9139, ?= 0.8986, k = 2, SE = 0.2668) can be useful for predicting the BCFs of compounds prior to their synthesis.

Genetic Divergence in Mango and Obtaining Minimum Efficient Descriptors

Mangifera indica (mango) is a typically tropical fruit with considerable economic value. Brazil features a wide variety of cultivars of this fruit, most of which are known under several different names. Indeed, the nomenclature of mango varieties is still quite confusing. Up to now there has been no well-defined scientific principle to differentiate them. The objective of the present work is to compare the different clustering methods in assessing genetic divergence among mango accessions, as well as identify the minimum efficient descriptors for that crop. A total of 20 mango accessions in Cáceres, Mato Grosso state, Brazil were evaluated. When building dissimilarity matrices, the descriptors were divided according to the following groups: leaf, flower/inflorescence, fruit, seed and growth habit/ripening period. With these divisions, combinations were performed among the groups of descriptors. The similarity index was used to obtain the dissimilarity matrices. Later, the accessions were clustered using the methods of Tocher, Ward and UPGMA. The study observed that it was possible to reduce the number of descriptors from 64 to 35, and that the clustering methods were compatible with the study of the genetic diversity of mango.

Relationships between morphological descriptors and RAPD markers for assessing genetic variability in Hippophae rhamnoides L.

Knowledge of genetic relatedness among accessions of germplasm is necessary for the development of breeding strategies to produce improved cultivars. The present investigation on Hippophae rhamnoides was carried out to assess its genetic variability in Himachal Pradesh, India, by employing morphological and RAPD markers. Different areas of Himachal Pradesh were surveyed and eight sites were finally selected Twenty four genotypes were selected for further studies, i.e., three genotypes from each site. On the basis of morphological studies, the genotypes of the Ropa site （Kinnaur） were considered elite genotypes. To assess the variability at the molecular level, RAPD patterns were studied by random primers. The total number of bands amplified was 607, out of which 487 bands were identified as polymorphic, depicting 80.23 per cent variability. Six unique bands were produced from three primers （OPA- 05, OPA-10 and OPD-08） specific for five genotypes, including three genotypes of the Ropa site, Kinnaur District.

Extracting and smoothing contours in mammograms using Fourier descriptors

Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficient smoothing method are needed. In X-ray images, such as mammograms, where object edge is not clearly discernible, estimating the object’s contour may yield substantial shift along the boundary due to noise or segmentation drawbacks. An appropriate smoothing is therefore required to reduce these effects. In this paper, an approach based on local adaptive threshold segmentation to extract contour and a new smoothing approach founded on Fourier descriptors are introduced. The experimental results of extraction obtained from a set of mammograms and compared with the breast regions delineated by radiologists yielded a percent overlap area of 98.7% ± 0.9% with false positive and negative rates of 0.36 ± 0.74 and 0.93 ± 0.44 respectively. The proposed method was tested on a set of images and improved the accuracy, leading to an average error of less than one pixel.

Updated Definition of the Three Solvent Descriptors Related to the Van der Waals Forces in Solutions

Innovative viewpoint on the older topic of the van der Waals forces, is of interesting and significant issue to be concerned in both the fields related to the fundamental investigation and thus valuable in guiding the new physiochemical phenomena and processes for both academic research and practical applications. The intermolecular Van der Waals forces involved in solutions have been recently deeply reconsidered as far as the solute side is concerned. More precisely, the solute descriptors (or parameters) experimentally established, have been accurately related to molecular features of a Simplified Molecular Topology. In the present study, an equivalent result is reached on the solvent side. Both experimental parameters have been obtained simultaneously in previous Gas Liquid Chromatographic studies for 121 Volatile Organic Compounds and 11 liquid stationary phases, via an original Multiplicative Matrix Analysis. In that experimental step, five groups of forces were identified, two of hydrogen bonding and three of Van der Waals: 1) dispersion (London), 2) orientation or polarity strictly speaking (Keesom), and 3) induction-polarizability (Debye). At this stage, an attempt of characterization the solvent parameters via the SMT procedure has been limited to those related to the Van der Waals forces, those related to the hydrogen bonding being for now left aside.

Morphological Characterization of Mango （Mangifera indica） Accessions Based on Brazilian Adapted Descriptors

The aim of this study was to characterize 103 mango accessions of the field germplasm collection of Embrapa semi-arid region, located in Juazeiro, Bahia, Brazil and to apply 50 morphological descriptors established by the Brazilian Ministry of Agriculture, livestock and supply to help in the development of new mango cultivars for the Northeastern region of Brazil. Four trees were selected for each accession and eight adult leaves, eight flowers and 16 fruits were collected from each tree. Morphological characteristics ranging from plant size to seed embryo were evaluated. Simple percentages were estimated for all the descriptors. Only the descriptors for leaf symmetry and fruit waxiness did not show variability among the accessions. Eight accessions did not show fruits with fibers, while nine other accessions presented flesh firmness, which is an important characteristic to improve breeding. The soluble solids content was high, above 14 ~Brix for 95% of the accessions with Tommy Atkins showing the lowest value, 12.5 ~Brix. A great diversity was found in the color of the epidermis ranging from green to red. The accessions Amrapali and Salitre presented a dark orange flesh color. The obtained data set, are the most comprehensive so far in Brazil, it allows choosing the best parents to develop new cultivars and will also contribute to the protection of mango cultivars in Brazil.

Modeling of the Chemico-Physical Process of Protonation of Molecules Entailing Some Quantum Chemical Descriptors

Relying upon the basic tenets of scientific modeling, an ansatz for the evaluation of proton affinity of mole-cules are evolved in terms of a four component model. The components of the model chosen are global de-scriptors like ionization energies, global softness, electronegativity and electrophilicity index. These akin quantum mechanical descriptors of atoms and molecules are linked with the charge rearrangement and polarization that occur during the physico-chemical process of protonation of molecules. The suggested ansatz is invoked to compute the protonation energy of as many as 43 compounds of diverse physico-chemical nature viz, hydrocarbons, alcohols, carbonyls, carboxylic acids, esters, aliphatic amines and aromatic amines. A detailed comparative study of theoretically evaluated protonation energies of the above mentioned molecules vis-à-vis their corresponding experimental counterparts reveals that there is a close agreement between the theory and experiment. Thus the results strongly suggest that the proposed modeling and the ansatz for computing PA, the proton affinity, of molecules for studying the physico-chemical process of protonation may be valid proposition.

Facial Expression Recognition Based on Local Fourier Coefficients and Facial Fourier Descriptors

The recent boom of mass media communication (such as social media and mobiles) has boosted more applications of automatic facial expression recognition (FER). Thus, human facial expressions have to be encoded and recognized through digital devices. However, this process has to be done under recurrent problems of image illumination changes and partial occlusions. Therefore, in this paper, we propose a fully automated FER system based on Local Fourier Coefficients and Facial Fourier Descriptors. The combined power of appearance and geometric features is used for describing the specific facial regions of eyes-eyebrows, nose and mouth. All based on the attributes of the Fourier Transform and Support Vector Machines. Hence, our proposal overcomes FER problems such as illumination changes, partial occlusion, image rotation, redundancy and dimensionality reduction. Several tests were performed in order to demonstrate the efficiency of our proposal, which were evaluated using three standard databases: CK+, MUG and TFEID. In addition, evaluation results showed that the average recognition rate of each database reaches higher performance than most of the state-of-the-art techniques surveyed in this paper.

Minimal descriptors for tests on distinctness, uniformity and stability of sesame

To set a distinction method for sesame cultivars, 110 sesame （Sesamum indicum L.） germplasms were collected from China, Cuba, the former Soviet Union, Mozambique, Japan, and the United States. Materials were characterized for various agro-morphological traits at 3 locations in China （Wuhan, Zhumadian and Shijiazhuang） during 2005 and 2006. 34 traits were observed. Yield-related traits showed wide range of phenotypic variability. Number of nodes to the first flower, initial flowering, leaf length, leaf width, leaf length/ width ratio at initial flowering, lobe incision of basal leaf, petiole length of basal leaf, branch number, plant height, capsule length, capsule width, 1000-seed weight, seed oil content, and seed crude protein content were scored according to grades 3, 4, 5, 6 and 7. Stem hairiness, leaf color, flower hairiness, color on outer side of corolla, pink color on inner side of lower lip, anther filament color, and capsule hairiness ranged from few （light） to more （heavy） and were scored according to the minimal descriptors 1, 2 and 3. Number of flow-ers per leaf axil, pink color on inner side of corolla, enations on back side of a leaf, petiole color, number of carpels per capsule, plant growth type, stem branching, main stem color at maturity, capsule color at maturity, capsule dehiscence at maturity, seed shape, and seed coat texture varied little and were scored according to two grades, either 1 and 2 or 1 and 9. The 5 seed coat colors white, yellow, brown, grey and black, were scored as 1,2, 3, 4, and 5 respectively. Based on traits variability, a set of 34 minimal descriptors was suggested for characterization of sesame for distinctness, uniformity and stability （DUS）, and was recom-mended in national agriculture standard NY/T 2434-2013.