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This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain multiple solutions, while a good quality of learning cannot be obtained by a single neural network approach. Therefore, this paper proposes employing Fourier descriptors to represent trajectory curves in a systematic and normalized form, developing a multi-solution distribution evaluation by random restart local searches (MDE-RRLS) to examine a better solution-space partitioning scheme, utilizing multiple DNNs to learn subspace regions separately, and creating a multi-facet query (MFQuery) to cooperatively predict multiple solutions. The experiments demonstrate that the proposed approach can obtain better or at least competitive outcomes compared to previous work in the literature. Furthermore, to verify the effectiveness and applicability, this paper investigates the design problem of an industrial six-linkage-bar ladle mechanism used in a die-casting system, and the proposed method can obtain several superior design solutions and offer alternatives in a short period of time when faced with redesign requirements. [ABSTRACT FROM AUTHOR]

As an effective tool for data analysis, formal concept analysis (FCA) is widely used in software engineering and machine learning. The construction of concept lattice is a key step of the FCA. How to effectively to update the concept lattice is still an open, interesting and important issue. To resolve this problem, an incremental algorithm for concept lattice on image structure similarity (SsimAddExten) was presented. The proposed method mapped each knowledge class on the conceptlattice into a graphic, when a new object was added or deleted in a knowledge class, the boundary profile of graphic will be changed, the graphic edge structure similarity was introduced as the calculation index of the change degree before and after the knowledge, and the concept lattice will be updated on the basis of the index. We performed experiments to test SsimAddExtent, whose computational efficiency obtains obvious advantages over mainstream methods on almost all test points, especially on the data set with a large number of attributes. But, its complexity is not reduced compared with mainstream methods. Both theoretical analysis and performance test show SsimAddExtent algorithm is better choice when we apply the FCA to large scale data or non-sparse data. [ABSTRACT FROM AUTHOR]

Sign language is a natural language mostly used by persons with hearing- and speech-based impairments to communicate with other people. In these modern times, sign language guides are used to eliminate the communication gap between people having hearing impairments and those with or without hearing impairments; however, they are very limited in number. To solve this challenge, automatic sign language recognition systems are developed to better reduce the communication gap for people with hearing disabilities. This paper presents the development of an automatic Amharic sign language translator which translates Amharic alphabet signs into their corresponding text using digital image processing and machine learning algorithms. The proposed system has four major developmental stages which include preprocessing, segmentation, feature extraction and classification. A total number of thirty-four features were extracted from shape, motion and color of hand gestures to represent both the base and derived class of Amharic sign characters. Classification models were built using artificial neural network (ANN) and multi-class support vector machine (SVM). The results show that the recognition system is capable of recognizing the Amharic alphabet signs with an average accuracy of 80.82% and 98.06% using the ANN and SVM classifiers, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a feature characterization method that can discriminate between man-made and natural objects on the seabed using a pair of SAS and optical images. Feature matching methods, like scale-invariant feature transform (SIFT), usually utilize gradient information to detect feature points, whereas contour-based features often use geometrical descriptors such as roughness, circularity, and solidity. However, due to the different characteristics of sonar and optical imagery, current descriptors cannot be directly applied over sonar-optical image pairs. The clarity of the water (muddy water, concentration of particles that scatter light) for optical images, and the stability of the platform, and reflections from seabed for SAS images are important parameters that distort the two images and make it hard to match between the two image sources. This paper proposes a feature characterization method that can identify man-made objects in underwater optic-SAS image pairs. Two new contour-based feature descriptors are introduced. The first is the entropy angle feature, which aims to reflect the entropy of the angles that originate from points on the contour. The second feature is local curve fitting, which characterizes the radial-polar space of the local curve polynomial fitting. The descriptors are designed to characterize man-made objects whose contour is expected to be smoother than natural objects such as rocks. Experimental results on a real, large dataset comprised of 1,519 optic-SAS image pairs, are shown to verify that the proposed method has a high level of classification accuracy and good discrimination between man-made and natural objects. [ABSTRACT FROM AUTHOR]

Roundness and sphericity have long been considered as measurements of the basic morphological properties of soil particles. Recent progress in optical image processing and analysis has enabled us to automate and rapidly perform Fourier analysis. Thus, obtaining complex Fourier descriptors describing the outline boundary of a 2-dimensional projected image of a soil particle could be performed. In this regard, we performed correlation analysis with the values of roundness, sphericity, and complex Fourier descriptor coefficients for 115 images of soil particles measured and reported in previous studies. The results of the statistical analysis revealed that roundness was strongly negatively correlated with Fourier descriptors of the order less than −5 or higher than +5. The width-to-length ratio sphericity was strongly correlated with the Fourier descriptor of the order of +2. [ABSTRACT FROM AUTHOR]

This article revisits methods based on global descriptors to estimate the pose of a known object using a monocular camera, in the context of space rendezvous between an autonomous spacecraft and a noncooperative target. These methods estimate the pose by detection, i.e., they do not require any prior information about the pose of the observed object, making them suitable for initial pose acquisition and the monitoring of faults in other on-board estimators. We consider here specifically methods that retrieve the pose of a known object using a precomputed set of invariants and geometric moments. Three classes of global invariant features are analyzed, based on complex moments, Zernike moments, and Fourier descriptors. The robustness, accuracy, and computational efficiency of the different invariants are tested and compared under various conditions. We also discuss certain implementation aspects of the method that lead to improved accuracy and efficiency over previously reported results. Overall, our results can be used to identify which variations of the method offer a sufficiently fast and robust solution for pose estimation by detection, with low computational requirements that are compatible with space-qualified processors. [ABSTRACT FROM AUTHOR]

Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

Nowadays, thermal cameras play a major role because of its temperature-based photography in many applications such as video surveillance, monitoring electronics/electrical machines, forest monitoring, monitoring babies/adult patients, and suspicious object detection. Tracking pedestrians in thermal video is a major task for such applications. Thermal cameras usually create images based on temperature emitted by the object only and not on the lighting conditions and outdoor environment conditions. But still thermal images have constraints like no texture or colour information, more number of dead pixels, low resolution, and noticeable visual colour patterns in case of any temperature variations. So the challenge in tracking pedestrians in thermal videos is tracking objects/pedestrians throughout the video without an identity switch by overcoming these constraints which may mislead the tracking process. To overcome these constraints, the proposed system uses tri feature matrix (TFM) as an object descriptor which is used to uniquely identify and represent objects in thermal images. TFM is represented in more compact way as a triple matrix. It is a simple and accurate descriptor suitable for tracking objects in thermal video sequences without an identity switch. The proposed Pedestrian tracking system uses most of the advantages of thermal cameras by overcoming challenges in thermal videos effectively based on a novel descriptor TFM. The proposed system is evaluated with various data sets, and the results are analysed using true positive, true negative, false negative, false positive, accuracy, precision, recall, F-score, global identity mismatch (GMME) and track matching error (TME). The performance metrics such as accuracy, precision, recall, F-score, GMME and TME are computed as 99%, 100%, 99%, 99%, 2.3%, and 2.1%, respectively. From the observation, it is found that the performance of proposed TFM-based system is significantly improved. The experimental result shows that the proposed system achieved more accurate tracking compared to the conventional methods. [ABSTRACT FROM AUTHOR]

Real-time applications of image and video processing algorithms have seen explosive growth in number and complexity over the past decade driven by consumer, scientific and defense applications exploiting inexpensive digital video cameras and networked computing device. This growth has opened up different alternatives to greatly enhance the surveillance capabilities using new architectures and parallelization strategies developed due to the increased accessibility of multicore, multi-threaded processors along with general purpose graphics processing units (GPUs). In this paper, we present a new implementation of a moving human detection algorithm on GPU based on the programming language CUDA. In our approach, the moving object is extracted by background subtraction based on the GMM (Gaussian Mixture Model) on GPU. Then, two complementary features are extracted for moving object classification. They are contour-based description: FD or Fourier Descriptor and region-based description: HOG or Histogram of Oriented Gradient. Both descriptors will then be effectively integrated to SVM (Support Vector Machine), which is able to provide the posterior probability, to achieve better performance. The implementation of such algorithm on a GPU allows a great performance in terms of execution time since it is 19 times faster than that on a CPU. Experimental results show also that the proposed approach outperforms some existing techniques and can detect pedestrians in real-time effectively. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)

Purpose: Intracranial aneurysms (IA) are abnormal dilatation of the arteries at the circle of Willis whose rupture can lead to catastrophic complications such as hemorrhagic stroke. The purpose of this work is to detect IA in 2D-DSA images. The proposed detection framework uses local binary patterns for the determination of initial aneurysm candidates and generic Fourier descriptor (GFD) for false positive removal. Methods: Here, the designed framework takes DSA images including IA as input and produces images where the IA is clearly identified and localized. The multi-step approach is defined as the following: The first phase presents the determination of initial aneurysm candidates using the uniform local binary patterns (LBPs). The LBPs are calculated from these images in order to identify texture contents of both aneurysm and no-aneurysm classes. The second phase presents the false positives removal using a shape descriptor based on contours: the GFD. Results: We demonstrated that the proposed detection method successfully recognized morphological features of intracranial aneurysm. The results demonstrated excellent agreement between manual and automated detections. With the computerized IA detection framework, all aneurysms were correctly detected with zero false negative and low FP rates. Conclusion: This study shows the potential of LBP and GFD as a feature descriptors and paves the way for a whole image analysis tool to predict intracranial aneurysm risk of rupture. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office 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.)

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. [ABSTRACT FROM AUTHOR]

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth. [ABSTRACT FROM AUTHOR]

Indian sign language (ISL) is the main communication medium among deaf Indians. An ISL vocabulary show that the hand plays a significant role in ISL. ISL includes static and dynamic hand gesture recognition. The main aim of this paper is to present multi-feature static hand gesture recognition for alphabets and numbers. Here, comparative analysis of various feature descriptors such as chain code, shape matrix, Fourier descriptor, 7 Hu moments, and boundary moments is done. Multi-feature fusion descriptor is designed using contour (Boundary moments, Fourier descriptor) and region based (7Hu moments) descriptors. Analysis of this new multi-feature descriptor is done in comparison with other individual descriptors and it showed noteworthy results over other descriptors. Three classification methods such as, Nearest Mean Classifier (NMC), k-Nearest Neighborhood (k-NN) and Naive Bayes classifier are used for classification and comparison. New Multi-feature fusion descriptor shows high recognition rate of 99.61% among all with k-NN. Real time recognition for number signs 0-9, of fusion descriptor with NMC gave 100% accuracy. [ABSTRACT FROM AUTHOR]

Image retrieval based on text annotation has become obsolete and is no longer interesting for scientists because of its high time complexity and low precision in results. Alternatively, increase in the amount of digital images has generated an excessive need for an accurate and efficient retrieval system. This paper proposes content based image retrieval technique at a local level incorporating all the rudimentary features. Image undergoes the segmentation process initially and each segment is then directed to the feature extraction process. The proposed technique is also based on image's content which primarily includes texture, shape and color. Besides these three basic features, FD (Fourier Descriptors) and edge histogram descriptors are also calculated to enhance the feature extraction process by taking hold of information at the boundary. Performance of the proposed method is found to be quite adequate when compared with the results from one of the best local level CBIR (Content Based Image Retrieval) techniques. [ABSTRACT FROM AUTHOR]

With the increase in the number of music files on various devices, it can be difficult to locate a desired file, especially when the title of the song or the name of the singer is not known. We propose a new query-by-singing/humming (QbSH) system that can find music files that match what the user is singing or humming. This research is novel in the following three ways: first, the Fourier descriptor (FD) method is proposed as the first classifier; it transforms the humming or music waveform into the frequency domain. Second, quantized dynamic time warping (QDTW) using symmetrical search space and quantized linear scaling (QLS) are used as the second and third classifiers, respectively, which increase the accuracy of the QbSH system compared to the conventional DTW and LS methods. Third, five classifiers, which include the three already mentioned along with the conventional DTW using symmetrical search space and LS methods, are combined using score level fusion, which further enhances performance. Experimental results with the 2009 MIR-QbSH corpus and the AFA MIDI 100 databases show that the proposed method outperforms those using a single classifier and other fusion methods. [ABSTRACT FROM AUTHOR]

In this paper, various prominent local and global descriptors are evaluated against each other for analyzing their performance on shape-based image retrieval. Local descriptors include Fourier descriptors, Weber's local descriptor, local binary patterns, and local ternary patterns. The prominent global descriptors include moment invariants, generic Fourier descriptor (GFD), angular radial transform (ART), wavelet moments (WM), and Zernike moment descriptor (ZMD). In addition, a novel local descriptor is proposed based on the histograms of circular arcs and linear edges, which are detected by means of Hough transform. The proposed local descriptor provides features, which are invariant to geometric transformations and are robust to noise as compared to some existing prominent local descriptors. We also propose an improvement in the performance of global descriptors GFD, ART, WM, and ZMD by taking advantage of the phase information in the comparison process along with their magnitude. Subsequently, the local and global descriptors with the best image-retrieval performances are combined to design an effective retrieval system, which further enhances the retrieval performance substantially. All descriptors are analyzed in terms of six principles set by MPEG-7. Detailed experiments are performed on standard benchmark image databases along with their rotation-invariance and noise test. The results of experiments reveal that the proposed fusion of local and global descriptors outperforms other major descriptors. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) model retrieval has gathered great importance in recent years, since the number of available 3D models on the Internet has drastically increased. Many content-based 3D model retrieval approaches have been proposed. Among these methods, visual similarity-based methods have shown higher retrieval accuracy. However, because these methods capture enormous shape features from different viewpoints or locations, a large amount of calculation and comparison is necessary. Furthermore, there is a trade-off between retrieval accuracy and speed. In this paper, a 3D model retrieval method constituting Continuous Principal Component Analysis (CPCA), Fourier descriptors, and Zernike moments is proposed. CPCA is applied to extract significant shape features based on projecting the model along the principal axes. Then, Fourier descriptors and Zernike moments are used to provide shape descriptors with rotation invariants. In addition, a feature integration process combines them. A strategy of similarity measure is proposed to solve the axes switching problem. To conclude, the experimental results show that the approach outperforms SECTORS2 and D2,18 and has slightly better retrieval results than Light Field Descriptor (LFD)6 and spin-image signatures.3 Moreover, the approach is more efficient and the storage size is much less. [ABSTRACT FROM AUTHOR]

This paper presents a novel wavelet transform saliency model to detect salient objects. In this model, a saliency map is generated by combining orientation feature maps obtained from wavelet transform of different scale images derived from the same image. Then, the order map of a saliency map is obtained by using Fourier descriptor, which could be used as a guidance to process the most important objects. Experiments indicate that this saliency model is robust to noise and superior to other saliency models in the literature. [ABSTRACT FROM AUTHOR]

Image retrieval based on region is one of the most promising and active research directions in recent year's CBIR, while region segmentation, feature selection and feature extraction of region are key issues. However, the existing approaches always adopt a uniform approach of segmentation and feature extraction for all images in the same system. In this paper, we propose adaptive image segmentation and feature extraction approach according to different category image for image retrieval system. To improve performance, we propose adaptive segmentation approach according to different category image. Textured image is segmented by Gaussian Mixture Models (GMM), while non-textured image is segmented by our proposed block-based normalized cut. To accurately describe feature of region, we propose weight assignment method for centroid pixel and its neighbor by convolution with normal distribution when image segmentation by GMM. To improve generalization, we propose adaptive number of Fourier descriptors of shape signature which depends on the energy distribution of Fourier descriptors, instead of fixed number by experience. To simply and efficiently describe the spatial relationships of multi-object or multi-region in same image, we apply simplified topological relationships. The experiments demonstrate that proposed approaches are superior to the traditional approaches. [ABSTRACT FROM AUTHOR]

In this paper we propose a new shape descriptor to describe closed curves. This descriptor is based on both local and global properties of the curve and uses a Harmonic State Model (HSM). The HSM model is based on the periodicity hypothesis of the closed contour. It uses the Kalman filter as the estimation tool. By applying Kalman filter, two state vectors are derived from the radius and the angle coordinates (with the origin at the gravity of the contour). A combination of both state vectors helps to describe the contour under study. From this double vector, we determine the items of our descriptor. These elements therefore allow describing both the local and the global aspect of the shape. The local information is included by a double sign-changes map (positive to negative, negative to positive) of the vector state elements and the global parameters are extracted from the vector state. These both parameters ensure invariance with respect to affine transformations and a solid description of the contour. A similarity measure based on the Manhattan distance is defined This measure is applied to MPEG-7 shapes database provide good results comparatively to the references in the area. [ABSTRACT FROM AUTHOR]

Image retrieval has nowadays several industrial applications. In these imaging applications, which typically use large image archives, the matter of computational efficiency is essential. Therefore, compact and efficient features are required to describe the visual content of the images. In this paper, we introduce a new Fourier-based object descriptor that combines the shape and color information of the objects occurring in images into a single low-dimensional descriptor. The experiments performed with an industrial surface defect image database show that the proposed descriptor is an accurate and computationally light approach to object description in a real-world retrieval problem. [ABSTRACT FROM AUTHOR]

The 3D progressive compression has received great interest in recent years, mainly due to the need for transmitting a large model over the network. This paper proposes a new compression method using Fourier descriptors for the contour shapes. Upon acquisition of the set of planar contours, a vertex list is obtained. The vertex coordinate (x,y) on the plane, is considered as a complex value (x+jy), which is transformed to the frequencies by using the Fourier descriptor. The priority filter based on the magnitude of frequencies determines the significance of the frequency components. Instead of using all frequencies, the most significant frequencies are selectively used to reconstruct the contour by the inverse Fourier transform. As a result, data size in the frequency domain is considerably reduced. Three application examples are demonstrated to show the effectiveness of the proposed method . [ABSTRACT FROM AUTHOR]

In this paper, we investigate a novel method for an individual's handwritten Chinese character font generation, using stroke correspondence between the reference character database and the compressed character database, by vector quantization. Chinese characters are composed of a combination of radicals. A radical may be separated into several strokes, with each stroke corresponding to two or more common strokes. By paying attention to the characteristics of Chinese characters and the strokes that form them, we consider each stroke to be a vector and compress the stroke pattern using vector quantization. A compression rate of 1.27% is achieved by the vector quantization. We performed the evaluation experiments using both subjective and objective criteria involving 26 subjects and demonstrated that fonts generated successfully reflect the user's individual handwriting. [ABSTRACT FROM AUTHOR]

Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32 ± 0.62 mm and mean absolute distance error of 0.54 ± 0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36 ± 0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve. [ABSTRACT FROM AUTHOR]

Space curves are highly descriptive features for 3-D objects. Two invariant representations for space curves are discussed in this paper. One represents space curves by complex waveforms. The other represents space curves using the 3-D moment invariants of the data points on the curves. Space curve matching using invariant global features is discussed. An algorithm for matching partially occluded 3-D curves is also presented, in which rigidity constraints on pairwise curve segments are used to determine the globally consistent matching. An association graph can be constructed from the local matches. The maximal cliques of the graph will determine the visible part of the model curves in the scene. Experimental results using 3-D curves obtained from stereo matching and edges detected from the range data are also presented. [ABSTRACT FROM AUTHOR]

Fourier descriptors based upon the waveform induced by a pattern's projection overcome a number of classic difficulties with Fourier-descriptor methodology. Not only are the descriptors invariant with respect to scale, translation, and rotation (as is usually the case), they are also continuous in the Hausdorff metric and robust with respect to both point noise and occlusion; insensitivity with respect to minimum occlusions is perhaps their most significant advantage. Continuity in the Hausdorff metric allows prediction of the effect on the descriptors when morphologically filtering a pattern. The effect of occlusion is also predictable. [ABSTRACT FROM AUTHOR]

By generating Fourier descriptors based upon the waveform induced by a pattern's geometric projection, a number of classic difficulties with the Fourier-descriptor methodology are mitigated. Not only are the descriptors invariant with respect to scale, translation, and rotation (as is usually the case), they are also continuous in the Hausdorff metric and robust with respect to both point noise and occlusion. An additional advantage is that they can be computed relative to a thresholded image without first finding an edge, thereby avoiding the difficulties typically present in thinning and orientation determination. The present paper discusses the method of projection-generated Fourier descriptors, as well as a study of the sensitivity to point noise. A companion paper will present the morphological properties and the effect of pattern occlusion. [ABSTRACT FROM AUTHOR]

Soil particle shape analysis was conducted on two calcareous sands from Dissa and Matmata in Tunisia and one silica sand from Jumunjin in Korea using an image analysis method. This technique uses complex Fourier shape descriptors and image analysis of sand grains SEM photographs to provide accurate quantification of particle morphology and texture. The Fourier descriptors, denoted "Signature Descriptors", provide measures of Elongation, Triangularity, Squareness, and Asymmetry and identify the overall shape of soil particles. A summary of higher-order descriptors provides textural information linked up to local roughness and texture characteristics. After analyzing the Fourier descriptors, elongation was found to be the major characteristics of the three sands, mainly for the Dissa and Matmata carbonate sands. In addition, the elongation signature descriptors for the Dissa and Matmata sands have very high values compared to those of Ras Alkhair (Saudi Arabia) and Dog's bay (Ireland) sands. Therefore, the Dissa and Matmata carbonate sands have higher angles of friction and higher critical state angle of friction than Ras Alkhair and Dog's bay sands. Furthermore, the Dissa and Matmata carbonate sands show higher roughness than Jumunjin's silica sand. [ABSTRACT FROM AUTHOR]

Digital watermarking technology is an effective method for copyright protection of digital information, such as images, documents, etc. In this paper, we propose a high capacity text image watermarking technique against printing and scanning processes. Firstly, this method obtains the invariant in the process of printing and scanning under the mathematical hypothesis model of print-scan transformation. Then based on the print-scan invariant, the Fourier descriptor is used to flip the trivial pixel points with high frequency information on the character boundary. Next, considering the resolution of the print-scan equipment and its influence on the print-scan invariant, a quadratic quantization function is proposed to embed watermark information of multiple bits for a single character. Finally, the QR code (Quick Response code) is researched, which has large information capacity, robust error correction ability and high decoding reliability. By using the QR code as the watermark information, we can reduce the impact of bit error rate during watermark extraction, and the robustness of the watermark information can be improved. The experimental results show that the proposed text watermarking algorithm has the advantages of anti-print scanning, anti-scaling, large capacity and good visual effects. [ABSTRACT FROM AUTHOR]

In the clustering validity analysis, three main methods including intra-class cohesion, inter-class separation, and artificial judgment index can be used to evaluate the clustering results. If the clustering result is efficient, it means that the clustering stability is better. However, when those methods are used, it is essential to provide the sample data or clustering algorithms in advance. This paper proposes a clustering stability evaluation method based on the Elliptic Fourier Descriptor structural similarity index (EFD-SSIM), which can evaluate the clustering stability only when the clustering result is available. Its mechanism is that cluster is mapped into 2D graphics, and the degree of intra-class cohesion is measured based on the structural similarity (SSIM) on the graphics. As shown by the experimental results, EFD-SSIM has a good evaluation effect and it is consistent with the existing effectiveness evaluation indices of the clustering algorithm. [ABSTRACT FROM AUTHOR]