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In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings. [ABSTRACT FROM AUTHOR]

Due to the widespread use of titanium alloy in various applications, such as maritime transportation and aviation, the process of mass production of this material has become more challenging. Traditional methods of removing materials have high wear rates and slow processing speeds. This has prompted the development of new techniques that can handle the complexity of this material. Wire Electrical Discharge Machining (WEDM) is a method frequently utilized in the removal of electrically conductive and hard materials. It can be used to create intricate designs by taking into account the input parameters, such as peak current and pulse duration. The various performance indicators of WEDM such as rate of material removal, surface roughness, dimensional deviation, and form/orientation tolerance errors are analyzed using a technique developed by Taguchi. The factors that influence the performance of this process are then analyzed through an ANOVA analysis. The goal of this research paper is to develop an optimization model based on a hybrid Taguchi grey approach that can be used to determine optimal process variable for achieving best performance in machining. The results of the explorative analysis shows that the various process variables can influence the performance of WEDM in Titanium grade 5. Contour plot and interaction analysis also adopted to divulge the interactive effect of factors on chosen performance measures. Furthermore, regression models were evolved to reveal the relationship amongst the dependent and independent variables. The findings of this study can be utilized by manufacturers to choose the suitable factors for the WEDM process. [ABSTRACT FROM AUTHOR]

In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings.

Introduction: The objective of this study was to examine the effect of photofunctionalization on the soft-tissue contour formed at the interface of various abutment materials using end-point analyses obtained from the three-dimensional oral mucosal model (3D-OMMs). Methods: Commercially pure titanium (CPTi), alumina-toughened zirconia (ATZ), and yttria-stabilized zirconia (YSZ) made into discs shapes were classified into two groups: UV-treated (PTx) and non-treated (NTx). The materials in PTx groups were exposed to UV light for 12 min. Human gingival fibroblasts and TR146 epithelial cell lines co-cultured on the acellular dermal membrane were used to construct the 3D-OMM. After 4 days of culture, the discs were inserted into the holes prepared within the membrane of 3D-OMMs. The contour formed by the tissue was evaluated after 14 days of culture. Results: The UV treatment of abutment materials resulted in the formation of more non-pocket-tissue types among the PTx group (p = 0.002). Of all materials tested, soft tissue contour around YSZ showed higher scores for the non-pocket type in both non- and UV-treated groups. Conclusions: The non-pocket type of tissue attachment was frequently found in all surfaces modified by photofunctionalization, particularly zirconia. The 3D-OMM can be used to evaluate the biological endpoints of implant surface modifications. [ABSTRACT FROM AUTHOR]

The paper presents some improvements of fast contour recognition algorithm. The main idea of this algorithm is based on calculation of geometric characteristics of a two-dimensional surface in , which encodes digital images. To obtain the contours, Minkowski fractal dimension calculation is used. A postprocessing algorithm of contour thinning without loss of quality of entire contour picture is proposed in the paper. An estimate of the speed of the new algorithm is presented in comparison with the well-known implementation of the Canny algorithm using the OpenCV computer vision library and the parallel architecture of CUDA. Examples demonstrating the efficiency of the new algorithm are presented. [ABSTRACT FROM AUTHOR]

In order to realize the accurate identification of weld features in the digital model of ship welded parts, and to improve the speed and accuracy of welding process selection of welding robots, a weld seam feature recognition algorithm based on triangulation and contour analysis is proposed. First, the concave and convex vertices of the polygon are judged by the angle coefficient method, and based on the Delaunay triangulation of the concave vertices and the triangle rotation, the triangle mesh of the 3D model surface is constructed and the STL file is generated. Second, based on the normal vector angles of adjacent triangles, the contours and points of the model are extracted. Finally, the welding joint and groove shape are identified according to the joint space position and the minimum contour line distance. The test results show that the mesh division based on the triangle rotation is suitable for complex polygons such as "combs", compared with other related methods, the factors of grid average and associated quality are increased by an average of 12.06% and 12.26% respectively, which effectively reduces the generation of deformed triangles and improves the quality of the mesh. Furthermore, after the meshing and contour analysis are merged, 4 types of joint forms and 10 types of groove weld features can not only be identified, but also the recognition efficiency is high and the recognition accuracy is good, which verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Introduction: The objective of this study was to examine the effect of photofunctionalization on the soft-tissue contour formed at the interface of various abutment materials using end-point analyses obtained from the three-dimensional oral mucosal model (3D-OMMs). Methods: Commercially pure titanium (CPTi), alumina-toughened zirconia (ATZ), and yttria-stabilized zirconia (YSZ) made into discs shapes were classified into two groups: UV-treated (PTx) and non-treated (NTx). The materials in PTx groups were exposed to UV light for 12 min. Human gingival fibroblasts and TR146 epithelial cell lines co-cultured on the acellular dermal membrane were used to construct the 3D-OMM. After 4 days of culture, the discs were inserted into the holes prepared within the membrane of 3D-OMMs. The contour formed by the tissue was evaluated after 14 days of culture. Results: The UV treatment of abutment materials resulted in the formation of more non-pocket-tissue types among the PTx group (p = 0.002). Of all materials tested, soft tissue contour around YSZ showed higher scores for the non-pocket type in both non- and UV-treated groups. Conclusions: The non-pocket type of tissue attachment was frequently found in all surfaces modified by photofunctionalization, particularly zirconia. The 3D-OMM can be used to evaluate the biological endpoints of implant surface modifications.

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We report on a methodological framework that analyzes land-use images with engineered (manually designed) features. As older feature engineering methods suffered from excessive computation of their features, we therefore, introduce techniques that are faster and also more elaborate than any previous approach. Feature extraction and description is based on contour and region information. The contour analysis comprises the detection of ridge, river, and edge contours and is based on a technique of minimal complexity (without requiring costly multiplicative operations). The traced contour segments are then partitioned and abstracted; then they are clustered to form group descriptors. The region analysis consists of the detection of brighter, darker, and flatter regions, as well as regions obtained from clustering; the clustering is carried out with minimal complexity using a hierarchical analysis. Region segments are partitioned and abstracted using a fast implementation of the symmetric-axis transform. A total of ca. 70 parameters is developed and land-use classification experiments are performed. On the UC Merced (UCMD) collection, the classification accuracy of Deep Nets is reached; on the NWPU-RESISC45 collection the accuracy still lags somewhat. [ABSTRACT FROM AUTHOR]

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize "true stromata," but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms. [ABSTRACT FROM AUTHOR]

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize “true stromata,” but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.

The paper presents a description of the developed APC and implemented the algorithm of EyeTracking (based on convolutional neural networks and morphological analysis), which allows people suffering from motor dysfunction and neuroparalytic syndromes to carry out the process of communication with the outside world through computer control and text printing by eye movement

Abstract Background The categorical description of leaf shapes is of paramount importance in ecology, taxonomy and paleobotanical studies. Classification systems proposed by domain experts support these descriptions. Despite the importance of these visual descriptive systems, classifications based on this expert’s knowledge may be ambiguous or limited when representing shapes in unknown scenarios, as expected for biological exploratory domains. This work proposes a novel strategy to automatically discover the shape categories in a set of unlabeled leaves by only using the leaf-shape information. In particular, we overcome the task of discovering shape categories from different plant species for three different biological settings. Results The proposed method may successfully infer the unknown underlying shape categories with an F-score greater than 92%. Conclusions The approach also provided high levels of visual interpretability, an essential requirement in the description of biological objects. This method may support morphological analysis of biological objects in exploratory domains.

Dalam industri makanan dan minuman dengan kemasan kaleng, pengecekan atau inspeksi kecacatan kaleng merupakan bagian penting dalam menjaga kualitas sebuah produk. Inspeksi tersebut sebagian masih menggunakan operator manual yang bergantung pada penglihatan para pekerja dengan segala keterbatasannya. Untuk menanggulangi masalah tersebut, artikel ini mengusulkan proses inspeksi kecacatan menggunakan filter warna HSL dan template matching berbasis contour analysisyang dilakukan secara otomatis tanpa bantuan operator. Proses inspeksi diawali dengan pengambilan citra RGB oleh kamera. Citra RGB tersebut kemudian dikonversi ke citra aras keabuan (gray level). Untuk dapat mendeteksi kecacatan, penulis memakai filter luminance dari ruang warna HSL. Proses template matching diterapkan pada masing-masing citra dengan cara membandingkan contour citra template sebagai contour citra referensi dengan contour citra uji. Kemasan kaleng yang digunakan sebagai template mempunyai diameter 4 cm. Dengan menggunakan kriteria uji jarak masing-masing pixel pada tiap lingkaran kaleng dengan lingkaran pada kaleng template, kaleng diklasifikasikan menjadi dua kategori yaitu kaleng OK (Good) atau reject. Hasil pengujian menunjukkan, dengan menerapkan nilai threshold sebesar 4 cm, nilai RMS untuk objek kategori OK adalah 0,59. Sementara untuk objek kategori reject didapatkan nilai RMS sebesar 19,59; 5,05 dan 15, 05 berturut-turut untuk objek reject karena diameter tidak beraturan, objek reject dengan pembuka kaleng terbuka dan objek reject tanpa pembuka kaleng.

An approach to the study of wave processes in the human cardiovascular system is presented in order to identify the constituent components in the complex signal of the pulse pressure wave—direct and reflected waves. This makes it possible to calculate the pulse wave propagation time and velocity, which are indicators that play an established role in the diagnosis and prognosis of cardiovascular diseases. Testing of the approach is based on a combination of methods for measuring the pulse wave with subsequent mathematical processing of empirical data and methods for direct numerical modeling of hemodynamic processes in the arterial tree. [ABSTRACT FROM AUTHOR]

This paper reports a study into the methods for recognizing the type of an air object on a digital image acquired from an air situation video monitoring system. A method has been proposed that is based on the application of a specific neural network, which solves the problem of categorizing multidimensional complex vectors of objects’ features based on complex calculations. In this case, a feature vector for recognizing the type of an air object is built on the basis of a Fourier transform for the sequence of coordinates of its two-dimensional contour. A technique has been proposed to train a neural network to recognize the type of an air object based on three image classes corresponding to three projections. This makes it easier to solve the classification problem owing to a more compact arrangement of the multidimensional feature vectors. The architecture of an air situation video monitoring system has been suggested, which includes an image preprocessing module and a module of a complex-valued neural network. Pre-processing makes it possible to identify an object’s contour and build a sequence of normalized descriptors, which are partially independent of the spatial position of the object and the contour processing technique. Existing methods of air object recognition require significant computational resources and do not take into consideration the specificity of recognizing objects with three degrees of freedom or do not account for the complex nature of the numerical representation of a contour. This study has shown that the reported results make it easier to train a neural network and reduce the hardware requirements in order to solve the task of air situation video monitoring. The proposed solution leads to increased mobility and extends the scope of application of such systems, including individual devices [ABSTRACT FROM AUTHOR]

An efficient computational method of segmentation of license plate characters is considered. Division into blocks is carried out on the assumption that the characters are localized in certain specific areas. The license plate image is divided into n parts, where n is the number of characters. The paper also describes a segmentation algorithm that underlies a modified version of this algorithm. Contour analysis was used as a mathematical apparatus for the solution of this problem. The proposed character segmentation algorithm allows its use in vehicle license plate recognition systems. [ABSTRACT FROM AUTHOR]

• A new approach for generation of polygonal approximations based on the convex hull of contour is proposed. • The proposed algorithm takes into account the symmetry of the contour. • A final improvement process is applied to increase the quality of the polygonal approximation. • The new algorithm is non-optimal but unsupervised (automatic), because no parameters have to be set or tuned. • Experiments using a public available dataset show that the new proposal outperforms other unsupervised algorithms. The present paper proposes a new non-optimal but unsupervised algorithm, called ICT-RDP , for generation of polygonal approximations based on the convex hull. Firstly, the new algorithm takes into account the convex hull of the 2D closed curves or contours to select a set of initial points; secondly, the significance levels of the contour points are computed using a symmetric version of the well-known Ramer, Douglas-Peucker algorithm; and, finally, a thresholding process is applied to obtain the vertices or dominant points of the polygonal approximation. Since the convex hull can select many initial points in rounded parts of the contour, an additional deletion process is required to remove quasi-collinear dominant points. Furthermore, an additional improvement process is applied to shift the dominant points in order to increase the quality of the polygonal approximation. Experiments performed on a public available dataset show that the new proposal outperforms other unsupervised algorithms for generation of polygonal approximations. [ABSTRACT FROM AUTHOR]

Morphology is the most visible and distinct character of plant organs and is accepted as one of the most important tools for plant biologists, plant breeders and growers. A number of methods based on plant morphology are applied to discriminate in particular close cultivars. In this study, image processing analysis was used on 20 grape cultivars (“Amasya beyazı“, “Antep karası“, “Bahçeli karası”, “Çavuş“, “Cevşen“, “Crimson“, “Dimrit“, “Erenköy beyazı“, “Hafızali“, “Karaşabi“, “Kırmızı“, “İzabella (Isabella) “, “Morşabi“, “Müşgüle“, “Nuniya“, “Royal“, “Sultani çekirdeksiz (Sultanina)“, “Yalova incisi“, “Yerli beyazv“, “Yuvarlak çekirdeksiz“) to classify them. According to image processing analysis, the longest and the greatest projected area values were observed in “Antep karası“ cultivar. The “Sultani çekirdeksiz“ cultivar had the least geometric mean diameter. The greatest sphericity ratios were observed in “Yerli beyaz“, “Erenköy beyazı“ and “Amasya beyazı“ cultivars. According to principal component analysis, dimensional attributes were identified as the most significant source of variation discriminant grape cultivars from each other. Morphological differences between the cultivars were explained by sphericity and elongation variables. According to elliptic Fourier analysis (EFA) results, grape morphology largely looks like ellipse and sphere. However, there are some cultivars that look similar to a water drop. The cultivars with similar morphology were identified by a pair-wise comparison test conducted with the use of linear discriminant analysis, and they were presented in a scatter plot. According to cluster analysis, present grape cultivars were classified into seven sub-groups, which indicated great diversity.

In this study, berry dimensions and shape traits, which are important for the design of the grape processing system and the classification of 10 different grape varieties grown in same ecological conditions (‘Ata Sarısı’, ‘Barış’, ‘Dımışkı’, ‘Hatun Parmağı’, ‘Helvani’, ‘Horoz Karası’, ‘Hönüsü’, ‘İtalia’, ‘Mevlana Sarısı’, and ‘Red Globe’) were determined; differences between the varieties were identified with the use of discriminant analysis. The largest grape varieties were identified as ‘Ata Sarısı’ and ‘Red Globe’. The ‘Red Globe’ and ‘Helvani’ varieties had geometrically sphere-like shape. The ‘Barış’ variety had the lowest size averages. According to elliptic Fourier analysis, the primary source of shape variation was ellipse and sphere-looking varieties. However, shape variation was seen due to the existence of a small number of drop-like varieties. According to discriminant analysis, shape differences of the varieties were defined by two discriminant functions. Based on these discriminant functions, the greatest classification performance was achieved for ‘Mevlana Sarısı’ and ‘Dımışkı’. In scatter plots, three shape definitions (sphere, ellipse, and drop) were made for grape varieties. Cluster analysis revealed 4 sub-groups. The first sub-group included the ‘Mevlana Sarısı’ variety; the second sub-group included the ‘Hönüsü’, ‘Hatun Parmağı’, ‘Dımışkı’, and ‘Horoz Karası’ varieties; the third sub-group included the ‘Ata Sarısı’ variety; the fourth sub-group included the ‘Barış’, ‘Helvani’, ‘İtalia’, and ‘Red Globe’ varieties. The variety in the first group had a geometrically ellipse-like shape, the largest length, and the smallest width. The size data were the smallest for the second sub-group. The third sub-group, with the ellipse-like shape, had the large size data. The grape varieties the closest to the sphere were classified in the fourth group, and these varieties had the large sizes.

The length of time in manually recording vehicle license plates in the parking system leads to long vehicle queues. This research developed automatic parking system based on RFID and vehicle plate number recognition. Authentication used a camera and Arduino Uno as the controller for RFID reading, feedback and gatekeeper control. The system will compare the character's image and RFID in the record of the database to authorize a vehicle. Image processing was done by contour analysis method and had 91% in accuracy at 60 cm of distance and 131.89 milliseconds of reading speed. The system had been able to work to automatically open and close gates based on the match of RFID and vehicle plate number recognition.

Purpose: The aim of the present study was to analyze quantitively the lower lid contour of patients with Graves orbitopathy. Methods: Bézier curves were manually adjusted to the ciliated and nasal (or lacrimal) portions of the lower lid contour of 41 patients with Graves orbitopathy and 43 normal subjects using National Institute of Health (NIH) ImageJ software. Considering the main coordinates of the Bézier functions, the Matlab software was employed to express the lid contours with 1000 points. The first order numerical derivative of the curves was calculated in order to compare the curvature of the whole lid contour of patients and controls. The same comparison was made for the temporal and nasal contour segments. Other parameters measured included the location of the lowest point on the contours (contour peak); the position of the lateral and medial canthi, as well as the angle formed by the lower lacrimal punctum and the vertex of the inner canthus. Results: The curvature of the temporal portion of the lid contours of patients and controls correlated with both the height of the lateral canthus and the magnitude of the MRD2. Graves retraction affects the medial and lateral portion of the lid. Both segments were more curved in patients than in controls. The lower punctum was significantly lowered, increasing the angle between the lower punctum and the medial canthus. Conclusions: The increased lateral curvature of the lower lid seen in patients with Graves disease is a natural geometric effect of the increment of MRD2. The nasal portion of the lid is also significantly lowered by retraction. [ABSTRACT FROM AUTHOR]

• Phenotypic variability can be powerful to reveal the population structure. • Polymorphism found in the otolith was linked to fish growth and migratory processes. • Contingent theory is proposed as the best model to explain the population structure. Studies have described the presence of different population units of blue jack mackerel, Trachurus picturatus , in the NE Atlantic region. However, the hypothesis of several populations has been subtly questioned due to the high similarity in the otolith shape among some regions. It suggests the possibility of migrations processes connecting them, especially between Madeira, the Canary Islands and the African coast, being the Canary Islands the region with higher potential of mixing due to the oceanographic conditions. In order to explore this hypothesis, we quantified the phenotypic variability in the otolith contour of the blue jack mackerel from Canary Islands using wavelets as mathematical descriptor. Our findings revealed the presence of three otolith phenotypes (M1, M2 and M3) in similar proportions. They were not linked to sex, age and size, but showed temporal variations associated with spawning, recruitment and feeding seasons. The best model to explain the population structure of T. picturatus in the Canary waters was based on local migration triangles and the 'contingent theory', where migrants and residents compose the population. In addition, we estimated different somatic growth parameters linked to these phenotypes. These results suggest a complex population structure in the region with possibility of connectivity with the closest populations inhabiting the Madeira archipelago and the African coast. However, future studies are necessary including the whole Atlantic distribution of the species, with special attention to the seasonal variations in the frequency of these phenotypes to clarify the intraspecific polymorphism and the migratory processes. [ABSTRACT FROM AUTHOR]

Computer vision based detection approaches are widely employed to detect or calibrate different types of meters nowadays. However, traditional detection algorithms suffer drawbacks in accuracy and adaptability upon detecting various types of automobile dashboards. Plenty of parameters of these algorithms need to be tuned to suit certain types of dashboards. Besides, theses algorithms cannot automatically read the speed value, which requires manual setting operations. In this paper, a novel approach is presented to adaptively detect different types of automobile dashboards. The contour analysis based method is first implemented to extract the connected component of the pointer. A robust character segmentation classifier, which is designed by cascading histogram of oriented gradients (HOG)/support vector machine (SVM) binary classifier, character filter as well as HOG/multiclass SVM digit classifier, is then proposed to recognize digit characters on the dashboard. Simultaneously, tick marks are then extracted based on recognition results. Finally, Newton interpolation linear relationship is established to diagnose the potential responding errors of the pointer. The experimental results show that the pointer extraction method is robust to interferences caused by connected components of digits and also that the established character segmentation classifier has a more accurate detection result. Furthermore, compared with similar algorithms, it has a significant advantage in detecting a vast majority of different dashboards without manual tuning of the parameters. [ABSTRACT FROM AUTHOR]

In this paper we present an on-board Computer Vision System for the pose estimation of an Unmanned Aerial Vehicle (UAV) with respect to a human-made landing target. The proposed methodology is based on a coarse-to-fine approach to search the target marks starting from the recognition of the characteristics visible from long distances, up to the inner details when short distances require high precisions for the final landing phase. A sequence of steps, based on a Point-to-Line Distance method, analyzes the contour information and allows the recognition of the target also in cluttered scenarios. The proposed approach enables to fully assist the UAV during its take-off and landing on the target, as it is able to detect anomalous situations, such as the loss of the target from the image field of view, and the precise evaluation of the drone attitude when only a part of the target remains visible in the image plane. Several indoor and outdoor experiments have been carried out to demonstrate the effectiveness, robustness and accuracy of developed algorithm. The outcomes have proven that our methodology outperforms the current state of art, providing high accuracies in estimating the position and the orientation of landing target with respect to the UAV. [ABSTRACT FROM AUTHOR]

An efficient computational method of segmentation of license plate characters is considered. Division into blocks is carried out on the assumption that the characters are localized in certain specific areas. The license plate image is divided into n parts, where n is the number of characters. The paper also describes a segmentation algorithm that underlies a modified version of this algorithm. Contour analysis was used as a mathematical apparatus for the solution of this problem. The proposed character segmentation algorithm allows its use in vehicle license plate recognition systems. [ABSTRACT FROM AUTHOR]

Copyright of Cybernetics & Systems Analysis / Kibernetiki i Sistemnyj Analiz is the property of V.M. Glushkov Institute of Cybernetics of NAS of Ukraine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article is devoted to analyzing the noise immunity of pulse waveform contour analysis. Different approaches to the implementation of the pulse waveform contour analysis were considered. It was demonstrated that a classical approach for the morphological analysis of the pulse wave based on using the stiffness index and the reflection index do not provide sufficient diagnostic accuracy due to the instability of detection of the reflected wave peak. The technique of contour analysis based on spectral transformation of replicated fragments of the pulse wave is proposed. As a diagnostic index the ratio of the amplitudes of the first two harmonics of the spectrum was used. The influence of measurement uncertainty of the pulse wave amplitude on determining the accuracy of the morphological analysis of the pulse wave realized by different methods was researched. [ABSTRACT FROM AUTHOR]

A number of problems of modern quantitative EEG are considered: the non-stationarity of the processed signal, the decreasing accuracy of the received results due to the averaging effect in the spectral analysis methods used in computer EEG, and the impossibility of detecting complexes in analyzed EEG without involving a clinician. A structural (syntactic) approach is suggested, which reduces the negative effect of these problems, and the mathematical apparatus for its realization is developed. The results of application of the structural approach to analysis of a real EEG are given. [ABSTRACT FROM AUTHOR]

Edge/structure-preserving operations for images aim to smooth images without blurring the edges/structures. Many exemplary edge-preserving filtering methods have recently been proposed to reduce the computational complexity and/or separate structures of different scales. They normally adopt a user-selected scale measurement to control the detail smoothing. However, natural photos contain objects of different sizes, which cannot be described by a single scale measurement. On the other hand, contour analysis is closely related to edge-preserving filtering, and significant progress has recently been achieved. Nevertheless, the majority of state-of-the-art filtering techniques have ignored the successes in this area. Inspired by the fact that learning-based edge detectors significantly outperform traditional manually-designed detectors, this paper proposes a learning-based edge-preserving filtering technique. It synergistically combines the differential operations in edge-preserving filters with the effectiveness of the recent edge detectors for scale-aware filtering. Unlike previous filtering methods, the proposed filters can efficiently extract subjectively meaningful structures from natural scenes containing multiple-scale objects. [ABSTRACT FROM AUTHOR]

In this paper, a new hierarchical method of contour analysis is proposed. The first stage of the analysis is based on the syntactic approach in which elementary segments of the contour are extracted by using geometric features. As a result, each extracted segment belongs to one of the equivalence classes which make the set of primitives. The contour is described as a string of primitives. The applied syntactic approach is a generalization of a classical shape language. Such description enables to classify some cases, but not all. Therefore, the second stage of the classification is applied. It consists in analysing other local geometric features of the contour in the context of the syntactic description obtained at the first stage of the analysis. The values of these features are used to define the arguments of the membership functions of the family of fuzzy sets which is the basis for the contour classification at the second stage of the analysis. The proposed formalism has been applied effectively to the recognition of bone contours in X-ray images. Healthy bones, the bones with erosions and the bones with osteophytes have been classified correctly. The proposal described in this paper turned out to be effective in the classification of contours of bones. [ABSTRACT FROM AUTHOR]

This article offers an efficient algorithm for preprocessing digital images for further detection of traffic signs in real time. The article considers the possibility of using the HSV color space, HSL for red color extraction. [ABSTRACT FROM AUTHOR]

Formal analysis and computer recognition of 2D color images is important branch of modern computer geometry. However, existing algo-rithms, although they are highly developed, are not quite satisfactory and seem to be much worse than (unknown) algorithms, which our brain uses to analyze eye information. Almost all existing algorithms omit colors and deal with grayscale transformations only. But in many cases color information is important. In this paper fundamentally new method of coding and analyzing color digital images is suggested. The main point of this method is that a full-color digital image is represented, without dropping colors, by special 2D surface in 3D space, after which it is analyzed by methods of differential ge-ometry, rather than traditional gradient-based or Hessian-based methods (like in SIFT, GLOH, SURF, Canny operator, and many other algorithms). [ABSTRACT FROM AUTHOR]