Contour Description Research Articles

Clothing Style Recognition Method Based on Digital Image Processing

The use of digital image processing technology to identify clothing styles from clothing images has broad application potential in clothing consumption analysis, auxiliary clothing design and identity recognition. In the current clothing style recognition field for feature extraction and classification of clothing contours, the main methods are extreme learning machine classification based on wavelet Fourier descriptor, and Euclidean distance classification based on fusion features. However, these methods also have some shortcomings. Currently, there is still no effective method to extract features and classify clothing contours. In order to solve this problem, this paper is based on the SVM classification method of Fourier contour description features. The contour curvature characteristic point in the figure can be described as the bending characteristic of the clothing contour curve, and its calculation is simple and intuitive, which improves the judgment effect of the similarity performance of the Hausdorff distance. This paper constructs a multi-resolution framework of the new image, uses edge detection algorithms to obtain the edges of each level of resolution image, and expands them into narrow edge bands, and combines the point distribution model with the edge narrow bands obtained by edge detection to perform multi-resolution image ASM search for. This paper proposes an image fusion algorithm based on algebraic multigrid and adaptive block. Because the coarse grid extracted by the algebraic multi-grid method can extract the detailed information of the image to a certain extent, the original image can be reconstructed from the grid data. Experimental research shows that from the overall recognition rate of various clothing styles, the Fourier descriptor has a high recognition rate for each clothing, and the stability of multiple experiments is also good.

A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network

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

A marker-less assembly stage recognition method based on segmented projection contour

In man–machine cooperative assembly, assembly recognition that determines the current manual working stage is key information to driving automatic computer-aided assistance. Focused on three features of the assembly scene—movable view, process stage, and CAD model template— a view-free and marker-less assembly stage recognition method is proposed in this paper. By constructing the semantic model for the assembly scene and the stage model for CAD parts, a depth image of assembly and a CAD model can be extracted as point clouds. Then we propose the segmented projection contour descriptor to uniformly express the shape information as a series of contours, so the 3D registration issue is converted to a 2D registration issue. The vertex-to-edge Hausdorff distance is proposed in the partial registration to determine the transformation matrix for each pair of contours. Finally, the overall matching algorithm based on the overlay ratio is given, and the best matching stage model indicates the current assembly stage. The recognition and classification experiments are carried out to verify the proposed method. A comparison with traditional Hausdorff distance proves the proposed algorithm performs better in stage recognition. Our study reveals that the proposed view-free and marker-less method can solve the stage recognition issue based on the assembly’s depth image, so as to connect the on-site assembly with the digital information.

Combining of objects images under conditions of inaccurate description of their images

An attribute of modern robotic systems and control systems for unmanned vehicles is a machine vision, which is based on processing of images obtained from highly informative radar or optoelectronic sensors. Effective image processing and recognition procedures are based on use of morphological features of objects images with high space‑time stability. This paper considers one of the aspects of images morphological analysis related to the influence of errors in the reference contour descriptions of objects. With use of developed analytical apparatus it is shown that the result of such errors is a decreasing of object/background contrast and, as a result, increasing of image comparison errors. The degree of this increase depends on the object type (area, linear) and its characteristics (size, contrast). Practical recommendations on the acceptable level of errors in the representation of object descriptions are justified.

Object Identification for Task-Oriented Communication with Industrial Robots.

To make the human-robot collaboration effective, it may be necessary to provide robots with “senses” like vision and hearing. Task-oriented man-machine speech communication often relies on the use of abstract terms to describe objects. Therefore it is necessary to correctly map those terms into images of proper objects in a camera’s field of view. This paper presents the results of our research in this field. A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed. We demonstrate that existing methods are not appropriate for this purpose because it is difficult to formulate general rules that humans employ to rank shapes into proper classes. Therefore, the rules for shape classification should be individually formulated by the users for each application. Our aim was to create appropriate tool facilitating formulation of those rules as it could potentially be a very labor-intensive task. The core of our solution is FCD (flexible contour description) format for description of flexible templates. Users will be able to create and edit flexible contour templates, and thus, adjust image recognition systems to their needs, in order to provide task-oriented communication between humans and robots.

Fourier-based contour descriptors to relax positional standardization of the otolith images in AFORO queries

The otolith digital catalogue AFORO allows unknown otoliths to be classified automatically by using a comparison with its classified records. To do this, the otolith’s contour, which is extracted from an image, is used. In AFORO, otolith images follow a strict positional normalization. Only the left sagitta is considered, and the images must show the internal side of the whole otolith, with the sulcus acusticus visible, the dorsal side (D) placed in the dorsal position and the rostral side (R) placed on the right. The otolith in the incoming image to be classified must also follow the same positional normalization. Variations from the reference position worsen the classification results. In this article, robust contour descriptors are proposed to extend this functionality of AFORO to the images of otoliths that are poorly normalized, contain rotations, are entirely inverted or came from the right rather than the left sagitta. These descriptors are based on the discrete Fourier transform and could extend the classification functionality to incoming images that are taken and sent, for instance, from smartphones in a wide range of working conditions.

A Method for Aircraft Labeling in Aerial and Satellite Images Based on Continuous Morphological Models

A method of labeling aerial and satellite remote sensing images for detecting objects of a priori known shapes is proposed. The implementation of this method uses an algorithm of piecewise linear approximations of the boundary of template objects and an algorithm of transforming the template contour descriptions to fit them to images of objects in the photographs. The developed algorithms are tested using the practical problem of determining the types of aircraft in remote sensing images remote sensing images. It is shown that the proposed method improves the labeling accuracy and is faster than the manual detection of aircraft images in photographs.

Unified multi‐scale method for fast leaf classification and retrieval using geometric information

Leaf image identification is a significant and challenging research work. Here, a unified multi-scale method is proposed to capture leaf geometric information for plant leaf classification and image retrieval. For each point on the leaf contour, the unified multi-scale method utilises a simple yet effective three-step strategy to locate corresponding neighbour points. The descriptor extracted using these neighbour points can provide a coarse-to-fine description of leaf contours and is of multi-scale characteristic intrinsically. More importantly, there is no scale parameter to be adjusted in the method, and hence no optimisation procedure is required. The proposed method is applied to three well-known contour features to capture geometric information of leaves, including angle, arch-height, and triangle-area representation. FFT is applied on the features in unified multi-scale method for convenient and fast leaf matching. Leaf classification and image retrieval experiments are conducted on four challenging leaf datasets to test the proposed method and evaluated using three standard performance evaluation metrics. The experimental results and comparisons with the state-of-art methods indicate that the unified multi-scale method has remarkable performance.

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feature analysis is proposed in this paper. Firstly, the TIR image is processed by opening- or closing-based gray-level morphological reconstruction (GMR) to smooth intricate background clutter while maintaining the intensity, shape, and contour features of ship target. Then, considering the intensity and contrast features, the fused saliency detection strategy including intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) is presented to highlight potential ship targets and suppress sea clutter. After that, an effective contour descriptor namely average eigenvalue measure of structure tensor (STAEM) is designed to characterize candidate ship targets, and the statistical shape knowledge is introduced to identify true ship targets from residual non-ship targets. Finally, the dual method is adopted to simultaneously detect both bright and dark ship targets in TIR image. Extensive experiments show that the proposed method outperforms the compared state-of-the-art methods, especially for infrared images with intricate sea clutter. Moreover, the proposed method can work stably for ship target with unknown brightness, variable quantities, sizes, and shapes.

Microstructural feature analysis of commercial Li-ion battery cathodes by focused ion beam tomography

Four positive electrodes are extracted from two high-power and two high-energy Li-ion battery consumer cells, and then analyzed by focused ion beam-scanning electron microscopy (FIB-SEM) tomography. The active material phase, carbon black-binder phase and pore phase are quantitatively evaluated using adequate 3D analysis techniques by their volume fractions, surface areas, particle size distributions and tortuosities. The active material phase is composed of LiCoO2 (Sanyo 1500 mA h), or LiNiCoAlO2 (Sony 2600 mA h) or one of two LiNiCoAlO2-LiCoO2 blends (Kokam high-power 350 mA h and high-energy 560 mA h). Special emphasis was put on the three-dimensional distribution of the submicron-sized (yet highly agglomerated) carbon black-binder phase. Vacuum infiltration with a two-component silicone resin gave greyscale contrast between the carbon black-binder and pore phases for contour description at voxel sizes from 30 to 50 nm. This study elucidates the cathode microstructural features for high-energy and high-power application and provides a complete parameter set of commercial electrodes with differing chemical composition. This information can be used by the scientific community as input parameters in simulations and models, filling the gap between the experimental and the simulation fields.

Quality assessment for view synthesis using low-level and mid-level structural representation

View synthesis is the most important technique in multi-view and free-viewpoint videos. The whole view synthesis includes the acquisition and processing of texture and depth images, and the virtual view rendering stage. Existing quality metrics for view synthesis have limited ability for the whole synthesis process for the following reasons. First, they are dedicated to a single stage of view synthesis, overlooking the commonality of all the possible distortions introduced in the whole process. Moreover, they only extract low-level features for quality assessment, ignoring the perceptual degradation caused by the mid-level contours that are destructed by heavy distortions in texture/depth images and the imperfect view rendering, which represent the spatial distribution/connection of adjacent contour pixels. Inspired by the above facts, this paper presents a quality metric for view synthesis using both Low-level and Mid-level Structural representation (LMS), aiming to accurately evaluate the distortions in the whole view synthesis process. Specifically, the scale space is first constructed to mimic the hierarchical property of the human visual system. Then, the statistics of gradient orientation is integrated with the statistics of gradient intensity for the low-level structural representation, which is motivated by the importance of the orientation selectivity mechanism to visual perception. Further, the mid-level structure is represented using bag of words for contour description based on the sparse coding of the primary visual cortex. Then the distances of both the low-level and mid-level features between the synthesized and reference images are calculated. Finally, two distances are integrated to generate the whole quality score. Extensive experiments on two public view synthesis databases demonstrate the superiority of the proposed method to the state-of-the-arts in evaluating the quality of the whole view synthesis.

Shape Matching Based on Multi-Scale Invariant Features

Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance.

Introducing the Concept of Potential-Based Organ Contours

The aim of this paper is to explore a new method for organ contour description in radiology and radiation protection. The method bases on the mathematical computation of electrical fields, exploited are the equipotential lines caused by a potential field of a distribution of point sources in analogy to electric charges. The organ shape is described by the potential values of the field, the contour by the equipotentials. The potential-dependent methods offers an inside–outside criterion and can be scaled in size and edited by changing the source points. Because of that it offers a flexible possible framework for organ contour editing and also toward segmentation. The main focus of this paper is the proof of principle, i.e., the optimization of the source point coordinates and source strengths, to show the transfer of voxelized organ borders to potential-based contours. The already voxelized organ borders were from a human voxel phantom generated from 2-D CT images of a real patient. Results for several closed and compact organs shall be presented and the limitations, future applications and possibilities addressed, e.g., the advantages of an implementation in Monte Carlo calculations of radiation transport.

Spline-Based Parameterization Techniques for Twin-Screw Machine Geometries

The fully automated generation of computational meshes for twin-screw machine geometries constitutes a mandatory aspect for the numerical simulation (and shape-optimization) of these devices but proves to be a challenging task in practice. Therefore, the successful generation of computational meshes requires sophisticated mathematical tools. Commercially available classical mesh generators can produce high quality meshes from no more than a description of the rotor contours. However, since we are particularly interested in numerical simulations using the principles of Isogeometric Analysis, a spline-based geometry description rather than a classical mesh is needed.In this paper, we propose a practical approach for the automated generation of spline-based twin-screw machine geometry parameterizations in two dimensions. For this purpose, we adopt the principles of Elliptic Grid Generation and present a parameterization algorithm that is compatible with an automated simulation pipeline based on the principles of isogeometric analysis.To demonstrate the proposed techniques, we apply them to an example geometry and present the resulting parameterizations. Finally, we give a qualitative explanation of how the discussed techniques can be utilized to generate geometry parameterizations in three dimensions and their applications to shape-optimization on a variable rotor-pitch.

Description of the surface contour and dispersion relation of subharmonical waves in Q-2D granular media

When a granular media having a subharmonic pattern is simulated, the first challenge is to define the surface contour of the granular media. This work proposes a method to define this contour. After define this contour, we propose an automated method to detect the presence or absence of the subharmonic oscillation pattern. Finally, with this information, we used the results to study the dispersion relation of the subharmonic pattern and its dependence with the depth of the granular media, resulting in a coincidence with the dispersion relation for shallow water.

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the [Formula: see text]-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

A curvature salience descriptor for full and partial shape matching

Shape representation and matching is one of the fundamental problems in compute vision. In this paper, we propose a novel shape contour descriptor, called curvature salience descriptor (CSD), for full shape matching. The presented descriptor utilizes only the most representative information to globally describe shape contour and is invariant to translation, rotation, scaling and partial occlusion of shapes. Shape matching is performed using the Dynamic Time Warping (DTW) to establish the point-to-point correspondence. To the solution of partial shape matching, we slightly modify the descriptor and develop a new matching framework that can establish the correspondence between the open query and candidate from coarse to fine. Different from previous methods, a coarse matching process is implemented to fast reject the false candidates before we establish the point-to-point correspondence. We conduct the experiments on the public datasets but also in the context of some specific applications and the results demonstrate that the proposed technique can achieve favorable performance compared to the existing methods.

Combining shape and contour features to improve tool wear monitoring in milling processes

In this paper, a new system based on combinations of a shape descriptor and a contour descriptor has been proposed for classifying inserts in milling processes according to their wear level following a computer vision based approach. To describe the wear region shape we have proposed a new descriptor called ShapeFeat and its contour has been characterized using the method BORCHIZ that, to the best of our knowledge, achieves the best performance for tool wear monitoring following a computer vision-based approach. Results show that the combination of BORCHIZ with ShapeFeat using a late fusion method improves the classification performance significantly, obtaining an accuracy of 91.44% in the binary classification (i.e. the classification of the wear as high or low) and 82.90% using three target classes (i.e. classification of the wear as high, medium or low). These results outperform the ones obtained by both descriptors used on their own, which achieve accuracies of 88.70 and 80.67% for two and three classes, respectively, using ShapeFeat and 87.06 and 80.24% with B-ORCHIZ. This study yielded encouraging results for the manufacturing community in order to classify automatically the inserts in terms of their wear for milling processes.

Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification

This paper presents new modeling of local binary patterns for texture representation. Referred to as local binary gradient contours (LBGC), the proposed models are expected to better represent the salient local texture structure. Thanks to the flexibility of repulsive-attractive characteristics, which represent the cornerstone of the proposed descriptors, two distinct LBP-like descriptors are built: repulsive and attractive local binary gradient contours (RLBGC and ALBGC). Conventional methods such as LBP, the family of binary gradient contours (BGC1, BGC2 and BGC3), LBP by neighborhoods (nLBPd) and several other LBP-like methods, are based on pairwise comparison of adjacent pixels. Unlike these methods, the proposed RLBGC and ALBGC operators encode the differences between local intensity values within triplets of pixels, along a closed path around the central pixel of a 3x3 gray-scale image patch. In order to increase the robustness of the proposed RLBGC and ALBGC descriptors, the triplet formed by the average local and average global gray levels (ALGL and AGGL) and the central pixel is incorporated in the modeling. In order to make the proposed approach more robust and stable, the RLBGC and ALBGC are concatenated together to form multi-scale repulsive-and-attractive local binary gradient contour (RALBGC) descriptor. Extensive experimental results from 13 challenging representative texture datasets show that the proposed descriptors, applied on each dataset, can achieve interesting classification accuracy, which is competitive or better than a great number of state-of-the-art LBP variants and non-LBP methods. Furthermore, statistical hypothesis testing is performed to prove the statistical significance of the achieved accuracy improvement over all the tested datasets.

The Mechanism to Eliminate the Accumulated Damage to the System of Socio-Economic Development of the Region

The article is devoted to the problem of the formation of the mechanism to eliminate the regional detriment in the context of recent changes in Russian legislation and the implementation of public programs in this area. As the object of the study the authors have selected the Rostov region as one of the subjects of the Russian Federation on the territory of which has been the target of accumulated environmental damage. The elimination of this damage will expand the capacity to attract investment and will improve the quality of life of the local population.The article analyzes the institutional, legal and economic conditions of the Russian Federation that allow the regions to implement measures for to reduce and eliminate the accumulated harm to environment. The documents that form the basis include the strategic documents explaining the process of governing of the federal and regional authorities' interaction in implementing the projects dealing with the existing damage.As a result of the study the authors have come to the conclusion that authorities in the Rostov region must form a coherent mechanism that provides efficient interaction for all the affected parties at regional level. Authors propose a contour description of this mechanism, carried out on the basis of the main requirements (targets, aims, objects, subjects and tools of impact) and form the proposals to ensure its effective functioning in the Rostov region.