Contour Description Research Articles

Use BIarc-Curves for Contour Description of the Turbine Profiles

The article analyzes the possibility of using BiArc-curves for the mathematical description of turbine profiles. Confirmed the relevance of the application BiArc-curves in the manufacture of turbine blades. The article presents some of the elements of the theory of building BiArc-curves. The algorithm for determining the coordinates of conjugate points, radii and angles of opening arcs BiArc-curve is shown in the work. Strong influence on the quality of BiArc-curve, provide the coordinates of the point arc connection. Also, the description of certain structural features of convex and concave contours profiles turbines using BiArc-curves. Here the algorithm of automatic construction of a turbine profile using geometric criteria of quality. Optimization problem is solved for each element BiArc-curve using a quadratic fit DSC-Powell. Method of construction of turbine profiles using BiArc-curves programmed in the language c++. Screenshot of dialog boxes and graphics programs can be found in the article. The examples of constructing profiles turbines using BiArc-curves with different initial data. Received confirmation of the possibility to describe the contours of turbine profiles using BiArc-curves.

Geometrical Model of Lemon Fruit

Abstract A proposal of a mathematical method of modelling of the lemon shape with Bézier's curves was presented. Lisbon, Verna, Genoa lemon cultivars were selected for verification of the modelling method. The lemon contour, which is its meridian, was described with three smoothly combined Bézier's curves. Pictures taken in 10 locations changing every 36o were the basis for description of lemon contours. Bézier's curves, which approximate meridians located on the surface of lemons, are their 3D models. The presented method may be applied for mathematical modelling of the lemon shape.

AERIAL OBJECT RECOGNITION ALGORITHM BASED ON CONTOUR DESCRIPTOR

Abstract. This paper describes the image recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the aerial object of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can gather set of training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types. The proposed orientation estimation algorithm showed good accuracy in all case studies.

Breast masses in mammography classification with local contour features

BackgroundMammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.MethodsIn this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.ResultsThe proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.ConclusionThe performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.

Aerial vehicles detection and recognition for UAV vision system

This article focuses on aerial vehicle detection and recognition by a wide field of view monocular vision system that can be installed on UAVs (unmanned aerial vehicles). The objects are mostly observed on the background of clouds under regular daylight conditions. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The described algorithm is able to detect small targets, but unlike many other approaches is designed to work with large-scale objects as well. The suggested algorithm is also intended to recognize and track the aerial vehicles of specific kind using a set of reference objects defined by their 3D models. For that purpose a computationally efficient contour descriptor for the models and the test objects is calculated. An experimental research on real video sequences is performed. The video database contains different types of aerial vehicles: airplanes, helicopters, and UAVs. The proposed approach shows good accuracy in all case studies and can be implemented in onboard vision systems.

Geometrical Contact Zone Analysis of the Skive Hobbing Process

In this paper, an innovative approach for contact zone analysis of skive hobbing is presented. This process is characterised by varying tool engagements and very small chip thicknesses. The paper describes the process-modelling and the analysis of the workpiece-tool contact. For efficient modelling, a novel dexel-based method for the description of a gear segment with discretized stock allowance is presented. Characteristic for the method is an analytical description of the target workpiece contour which allows to describe points of the surface by Cartesian coordinates on the winding off. On this workpiece contour, dexel are modelled in surface-orthogonal direction. The initial lengths of these dexel describe the stock allowance. By cutting these dexel with a tool, a time-and position-dependent prediction of material removal values, like width of undeformed chip, undeformed chip thickness or cutting directions can be obtained. Selected results are presented and interpreted.

Toward an Automated Identification of Anastrepha Fruit Flies in the fraterculus group (Diptera, Tephritidae).

In this study, we assess image analysis techniques as automatic identifiers of three Anastrepha species of quarantine importance, Anastrepha fraterculus (Wiedemann), Anastrepha obliqua (Macquart), and Anastrepha sororcula Zucchi, based on wing and aculeus images. The right wing and aculeus of 100 individuals of each species were mounted on microscope slides, and images were captured with a stereomicroscope and light microscope. For wing image analysis, we used the color descriptor Local Color Histogram; for aculei, we used the contour descriptor Edge Orientation Autocorrelogram. A Support Vector Machine classifier was used in the final stage of wing and aculeus classification. Very accurate species identifications were obtained based on wing and aculeus images, with average accuracies of 94 and 95%, respectively. These results are comparable to previous identification results based on morphometric techniques and to the results achieved by experienced entomologists. Wing and aculeus images produced equally accurate classifications, greatly facilitating the identification of these species. The proposed technique is therefore a promising option for separating these three closely related species in the fraterculus group.

Guest Editorial: Environmental Multimedia Retrieval

Guest Editorial: Environmental Multimedia Retrieval

Coordination of contour descriptions in the class of equivalence with the group of affine transformations

The paper puts forward two theorems underlying a method of coordination of the descriptions of contours for the equivalence class with the group of affine transformations. High speed performance of the method is achieved by excluding the calculations of similarity estimates. Fundamentals of the method are explained by examples of coordination of the descriptions of contours of trapezoids.

Shape Retrieval Using ECPDH with Dynamic Programming

The matching and retrieval of the 2D shapes are challenging issues in object recognition and computer vision. In this paper, we propose a new object contour descriptor termed ECPDH (Elliptic Contour Points Distribution Histogram), which is based on the distribution of the points on an object contour under the polar coordinates. ECPDH has the essential merits of invariance to scale and translation. Dynamic Programming (DP) algorithm is used to measure the distance between the ECPDHs. The effectiveness of the proposed method is demonstrated using some standard tests on MPEG-7 shape database. The results show the precision and recall of our method over other recent methods in the literature.

Fine-grained bird recognition by using contour-based pose transfer

We propose a pose transfer method for fine-grained classifications of birds that have wide variations in appearance due to different poses and subcategories. Specifically, bird pose is transferred by using Radon-transform-based contour descriptor, k -means clustering, and K nearest neighbors (KNN) classifier. During training, we clustered annotated image samples into certain poses based on their normalized part locations and used the cluster centers as their consistent part constellations for a particular pose. At the testing stage, Radon-transform-based contour descriptor is used to find the pose a sample belongs to with a KNN classifier by using cosine similarity, and normalized part constellations are transferred to the unannotated image according to the pose type. Bag-of-visual words with OpponentSIFT and color names extracted from each part and from the global image are concatenated as feature vector, which is input to support vector machine for classification. Experimental results demonstrate significant performance gains from our method on the Caltech-UCSD Birds-2011 dataset for the fine-grained bird classification task.

Fuzzy qualitative human model for viewpoint identification

The integration of advance human motion analysis techniques in low-cost video cameras has emerged for consumer applications, particularly in video surveillance systems. These smart and cheap devices provide the practical solutions for improving the public safety and homeland security with the capability of understanding the human behaviour automatically. In this sense, an intelligent video surveillance system should not be constrained on a person viewpoint, as in natural, a person is not restricted to perform an action from a fixed camera viewpoint. To achieve the objective, many state-of-the-art approaches require the information from multiple cameras in their processing. This is an impractical solution by considering its feasibility and computational complexity. First, it is very difficult to find an open space in real environment with perfect overlapping for multi-camera calibration. Secondly, the processing of information from multiple cameras is computational burden. With this, a surge of interest has sparked on single camera approach with notable work on the concept of view specific action recognition. However in their work, the viewpoints are assumed in a priori. In this paper, we extend it by proposing a viewpoint estimation framework where a novel human contour descriptor namely the fuzzy qualitative human contour is extracted from the fuzzy qualitative Poisson human model for viewpoint analysis. Clustering algorithms are used to learn and classify the viewpoints. In addition, our system is also integrated with the capability to classify front and rear views. Experimental results showed the reliability and effectiveness of our proposed viewpoint estimation framework by using the challenging IXMAS human action dataset.

Speech Melody Properties in English, Czech and Czech English: Reference and Interference

Two major objectives were set for the present study: to provide reference data for the description of Czech and English F0 contours, and to investigate the limits of the ‘interference hypothesis’ on Czech English data. Altogether, the production of 40 speakers in 2392 breath-group F0 contours was analyzed. The speech of 32 professional speakers of English and Czech provides reference values for various acoustic correlates of pitch level, pitch span and downtrend gradient. These values were subsequently used as a benchmark for a confirmation of the interference hypothesis through comparison with a further sample of 8 non-professional speakers of English and Czech-accented English. The native English speakers of both genders produced significantly higher pitch level indicators, wider pitch span and a steeper downtrend gradient than the reference native speakers of Czech. Although the pitch level of the Czech-accented material lies in between the two reference groups, the pitch span of this group is the narrowest, which indicates that factors of foreign-accentedness other than simply interference are in effect.

Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors

The assessment of the state of the acrosome is a priority in artificial insemination centres since it is one of the main causes of function loss. In this work, boar spermatozoa present in gray scale images acquired with a phase-contrast microscope have been classified as acrosome-intact or acrosome-damaged, after using fluorescent images for creating the ground truth. Based on shape prior criteria combined with Otsu's thresholding, regional minima and watershed transform, the spermatozoa heads were segmented and registered. One of the main novelties of this proposal is that, unlike what previous works stated, the obtained results show that the contour information of the spermatozoon head is important for improving description and classification. Other of this work novelties is that it confirms that combining different texture descriptors and contour descriptors yield the best classification rates for this problem up to date. The classification was performed with a Support Vector Machine backed by a Least Squares training algorithm and a linear kernel. Using the biggest acrosome intact-damaged dataset ever created, the early fusion approach followed provides a 0.9913 F-Score, outperforming all previous related works.

An Alternative Approach to the Conventionality Control Doctrine

Judge Eduardo Ferrer Mac-Gregor presents a very clear and concise description of the main contours of the conventionality control theory articulated by the Inter-American Court of Human Rights (“Court,” “Tribunal,” or “Inter-American Court”). So, I will not repeat his masterful explanation, which states, in brief, that the conventionality control requires all State authorities, particularly judges, to apply the American Convention on Human Rights (“the Convention”) as interpreted by the Court.

Investigation of the Skive Hobbing Process by Applying a Dexel-based Cutting Simulation

In this paper, a novel approach for cutting simulation of skive hobbing is presented. Skive hobbing is a process applied for finishing of already hardened gears. The process is characterized by varying tool engagements and very small chip thicknesses. The paper describes the process-modeling and the analysis of the workpiece-tool contact. For efficient modeling, a novel dexel-based method is presented for the description of a gear segment with discretized stock allowance. Characteristic of the method is an analytical description of the target workpiece contour which allows to describe points of the surface by Cartesian coordinates on the winding off. Dexel are oriented orthogonal to the surface of the final workpiece contour. The initial lengths of the dexel describe the stock allowance. By cutting these dexel with a tool, a time- and position-dependent prediction of material removal values, e.g. width of undeformed chip or undeformed chip thickness, is possible. Selected results are presented and interpreted.

Features extraction based on the Discrete Hartley Transform for closed contour

In this paper the authors propose a new closed contour descriptor that could be seen as a Feature Extractor of closed contours based on the Discrete Hartley Transform (DHT), its main characteristic is that uses only half of the coefficients required by Elliptical Fourier Descriptors (EFD) to obtain a contour approximation with similar error measure. The proposed closed contour descriptor provides an excellent capability of information compression useful for a great number of AI applications. Moreover it can provide scale, position and rotation invariance, and last but not least it has the advantage that both the parameterization and the reconstructed shape from the compressed set can be computed very efficiently by the fast Discrete Hartley Transform (DHT) algorithm. This Feature Extractor could be useful when the application claims for reversible features and when the user needs and easy measure of the quality for a given level of compression, scalable from low to very high quality.

A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition.

Shape representation is an extremely important and longstanding problem in the field of pattern recognition. Closed contour, which refers to shape contour, plays a crucial role in the comparison of shapes. Because shape contour is the most stable, distinguishable, and invariable feature of an object, it is useful to incorporate it into the recognition process. This paper proposes a method based on genetic algorithms. The proposed method can be used to identify the most common contour fragments, which can be used to represent the contours of a shape category. The common fragments clarify the particular logics included in the contours. This paper shows that the explicit representation of the shape contour contributes significantly to shape representation and object recognition.

Contour descriptor based on space symmetry and its matching technique

In this paper a novel contour descriptor and its matching technique is proposed. The method describes the contour based on global space symmetry, and then adapts a new probability matching technique to complete the shape recognition. The method is tested on data set of MPEG7 Core Experiments Shape-1 Part B, experiments show that the proposed method is suit for shape description and matching.

Semantic annotation based on image segmentation

In order to effectively resolve the "semantic gap" in image retrieval,this paper studied a new method for semantic annotation.Based on image segmentation,the method constructed image dictionary during the training phase,through analysis and description of color,texture and wavelet contour,established the two-stage annotation model combining comparison of wavelet contour and probability.It adopted corresponding method for different images by phases.The experimental results indicate the method can significantly improve recall and precision,and the maximum of precision is 23.6 percent.Besides,the model can understand image better and it also has good annotation effect and retrieval performance.