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The nanowood powder made from varying wood species has different properties. Therefore, while preparing nanowood powder, it is critical for us to accurately identify the wood species. By adopting the method of analyzing the wood species from the microstructure of the plate cell, we are able to ensure a high level of accuracy of the recognition. It is one of the commonly used parameters to identify sheet materials for further exploration of the microscopic cell shape. While applying the method of improved Fourier descriptor and the method of shape context, we have carried out the mathematical analysis of common soft wood plank cells in 5 slice images. In addition, we have conducted studies on the single cell graphics and quadrilateral, pentagon, round, oval similarity analysis respectively, and explored the use of benchmark cell model, thus providing statistical support for the subsequent material identification. Experiments have shown that this method is both efficient and robust in identifying sheet materials.

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.

This paper proposes a model to quantify the multiscale representation of a polyline based on iterative head/tail breaks. A polyline is first transformed into a corresponding Fourier descriptor cons...

Fourier descriptors are classical global shape descriptors with high matching speed but low accuracy. To obtain higher accuracy, a novel framework for forming Fourier descriptors is proposed and named as MSFDGF (multiscale Fourier descriptor using group feature). MSFDGF achieves multiscale description by generating coarser contours. Then, a group of complementary features are extracted on the generated coarser contours. Finally, Fourier transform is performed on the features. MSFDGF-SH is a new global descriptor using the MSFDGF framework and shape histograms. Experiments are conducted on four databases, which are MPEG-7 CE-1 Part B, Swedish Plant Leaf, Kimia 99 and Expanded Articulated Database, to evaluate the performance of MSFDGF-SH. The experimental results show that MSFDGF-SH is an effective and efficient global shape descriptor. This new descriptor has a high accuracy of 87.76%, which exceeds the Shape Tree on the MPEG-7 CE-1 Part B dataset. This is the first Fourier descriptor that surpasses the Shape Tree method in terms of both accuracy and speed on this dataset.

The omnidirectional M-mode echocardiogram provides a new method for human heart functional analyses. In this article, to sharpen object edges, we designed image processing kernel based on Fractional differential for image enhancement. After that, the contour of the left ventricle in a short axis is first extracted using both an improved Canny edge detection algorithm and the gray level searching algorithm in the radial direction as auxiliary. The modified Canny edge detection algorithm with the matching method between adjacent frames then is adopted for the subsequent frames to extract the left ventricular contours. The non-functional movements in the B-ultrasonic plane are determined by using the movement extracting method based on Fourier descriptors and the mass center with the inertia axis method, and the movements are removed from a compound motion. The Fourier descriptors are applied to get a series of image contour curves with the principal translation and rotation. Hence the curve of the cardiac motion can accurately show functional movements in any location of the heart. Using our technique, we can reduce multi-lines and excursion, as well as correct the omnidirectional M-mode echocardiography.

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. 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. 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. 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.

Shape information plays an important role in the human visual system, and how to represent the shape accurately is a very challenging issue in shape recognition. In recent years, many shape description approaches have been proposed. However, most of the existing methods are still facing challenges on accuracy and computational efficiency. Fourier descriptor is regarded as a promising shape description method because it has a solid theoretical foundation and at the same time possesses the strength of appealing invariance properties and high computational efficiency. We introduce a novel multiscale Fourier descriptor based on triangular features which is used to identify shapes. The local and global characteristics of a shape are effectively captured by the proposed shape descriptor. Meanwhile, the proposed shape descriptor has good properties, such as the invariant of the geometric transformation and the starting point of an object. We tested this descriptor on four popular benchmarking datasets, including MPEG-7, Swedish leaf, ETH-80, and Flavia leaf. The results confirm that our method is better than comparable-complexity approaches based on Fourier descriptors and does not perform unfavorably with respect to more complex, state-of-the-art, shape descriptors. In particular, our method is far superior to the complex shape description methods in terms of retrieval efficiency and computational complexity.

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.

Pottery is an important material in archaeological studies, and the accurate classification of pottery shapes largely depends on the experience and knowledge of archaeologists. In this thesis, pottery taken from the Gansu-Zhanqi site is used for sampling. Three-dimensional models of the pottery were obtained by three-dimensional scanning, and a computer-assisted pottery typology was studied through quantitative analysis and elliptic Fourier analysis. This method can enhance and supplement the traditional methods of classifying pottery in archaeology, thereby enriching the parameters and breadth of pottery analysis. This method represents a new means for exploring and experimenting with objective classification and provides a new tool for traditional archaeological analysis methods.traditional methods of classifying pottery in archaeology, thereby enriching the parameters and breadth of pottery analysis. This method represents a new means of exploring and experimenting with objective classification and provides a new tool for traditional archaeological analysis methods.

In this study we propose a novel cerebral microbleed (CMB) detection technique which simultaneously utilizes distribution information in dual domains and shape information obtained by a Fourier descriptor, and does not rely on a large set of training data. Specifically, the dual domain distribution modeling aims to simultaneously examine the image content in both gradient domain and voxel domain, while the Fourier descriptor further characterize the shape of the candidate region. A set of labeled data is used to form the dualdomain distribution as well as the distribution of Fourier coefficients. Then the probability of a region containing a CMB is estimated by combining the two types of distributions. Experimental results show that the proposed approach is efficient and desirable for scenarios where the number of samples is limited.

Tracking tools for target structures in ultrasound imaging are a crucial point in the development of semiautonomous image guided procedures. This paper presents an algorithm for semi-autonomous target-structure tracking, which uses a parametric active contour model with a Fourier descriptor formulation. The algorithm is tested in a soft real-time scenario, and its robustness is analysed w.r.t. probe translation and rotation, including also target structure deformations. In terms of temporal performance, the algorithm implemented in MATLAB has shown the ability to operate easily at 25 fps when coupled with an ultrasound probe, showing however capability to reach 40 fps when tested as a standalone algorithm.

The Juxtapleural nodule regions are often missed in the result of lung segmentation algorithms. To tackle this problem, an identification method basing on the SIFT information and elliptic Fourier descriptor is proposed. Firstly, the SIFT information is used to locate the position of key points in the lung mask. Then with the help of distance relationship, the support borderlines of key points are calculated. Thirdly, the elliptic Fourier descriptor is introduced to describe a support line. Finally, an adaptive threshold is designed to decide whether the current support line is corresponding to an under-segmented region. Experiments on real CT images demonstrate that the proposed model provides an efficient way to perform under-segmented region identification task.

Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourier-based multi-scale descriptors achieve some success in shape retrieval, especially make real-time application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourier-based method with novel multi-scale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors; Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor.

With the popularity of automobiles, road traffic accidents and congestion have become increasingly serious. Therefore, technologies are needed to solve problems such as speeding and congestion. The detection and tracking of vehicles based on computer vision and Internet of Things monitoring are an important part of the intelligent traffic monitoring system. The angle between the camera and the vehicle will cause the gradually moving vehicles to have a connection during image segmentation. This paper aims to improve the detection accuracy of vehicles from camera images. A new separation method of the vehicle detection area was proposed in this paper. Moving areas are extracted by inter-frame differences, and vehicle areas are formed from the areas. If more than one vehicle area partially overlaps as one area, it is necessary to separate the area. The existing method extracts a place to be separated from an outline of the area. However, it is impossible for the method to separate vehicles using the extracted shape. Therefore, a new method is proposed that makes the place to be separated defined by the reshaping of the area with the use of the Fourier descriptor. The method tries to detect the place from the area. As a result, this method makes it possible to separate the area that the existing method cannot separate and it has obtained a high accuracy of separation in the experimental data of the Internet of Things monitoring.

There are various possible approaches to the task of automatic identification of football players on video sequences. In the paper the numbers on jerseys are analysed and investigated. Firstly, the algorithm for the localisation of sport shirts and consecutively numbers is described. Secondly, the method for the recognition of the extracted numbers is proposed and experimentally investigated. The approach analysed in the paper applies the Generic Fourier Descriptor for the representation of extracted binary shapes of numbers.

Coupled serial mechanism is a class of mechanisms that couple the relative rotation of successive links utilizing gears or cable-pulley systems. They can be used to generate complex end-effector trajectories or motions with a single actuator. With the employment of Fourier descriptors, a novel approach to integrate type synthesis and dimensional synthesis of such mechanisms is proposed in this paper. Through the Fourier analysis of two arbitrary trajectories from the given motion, the simplest trajectory that contains the least number of harmonic components is identified. Then, characteristic information of those harmonics such as their numbers, amplitudes and initial phases are used to determine the topology and dimensions of the corresponding coupled serial mechanism, thus effectively solving the motion synthesis problem of this type of mechanisms. Finally, three examples are given to demonstrate the validity of the proposed method.

This paper attempts to design an automated, efficient and intelligent tomato grading method that facilitates the graded selling of the fruit. Based on machine vision, the color images of tomatoes with different morphologies were studied, and the color, shape and size were selected as the key features. On this basis, an automated grading classifier was created based on the surface features of tomatoes, and a grading platform was set up to verify the effect of the classifier. Specifically, the Hue value distributions of tomatoes with different maturities were investigated, and the Hue value ranges were determined for mature, semi-mature and immature tomatoes, producing the color classifier. Next, the first-order Fourier descriptor (1D- FD) was adopted to describe the radius sequence of tomato contour, and an equation was established to compute the irregularity of tomato contour, creating the shape classifier. After that, a linear regression equation was constructed to reflect the relationship between the transverse diameters of actual tomatoes and tomato images, and a classifier between large, medium and small tomatoes was produced based on the transverse diameter. Finally, a comprehensive tomato classifier was built based on the color, shape and size diameters. The experimental results show that the mean grading accuracy of the proposed method was 90.7%. This means our method can achieve automated real-time grading of tomatoes.

In image retrieval, the shape feature is one of the key features of image content description. At present, most of the widely used Fourier descriptors in shape descriptors are invariant in translation, rotation and scale expansion. But Fourier descriptors are susceptible to the location of the starting point. In this paper, an improved image retrieval method based on Fourier descriptors is proposed. First, the image is preprocessed and the edge of the image is extracted. Secondly, the starting point of the contour is determined by the minimum inertia axis. Then Fourier transform is used to get eigenvectors. Finally, the correlation coefficient is used to calculate the similarity. Experiments show that the Fourier Descriptor Image Retrieval Method based on the minimum inertia axis is more efficient than other methods in Swedish Leaf database.

In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms.

A shape descriptor is an effective tool for describing the shape feature of an object in remote sensing images. Researchers have put forward a lot of excellent descriptors. The discriminability of some descriptors is very strong in the experiments, but usually their computational cost is large, which makes them unsuitable to be used in practical applications. This paper proposes a new descriptor-FMSCCD (Fourier descriptor based on multiscale centroid contour distance)&mdash, which is a frequency domain descriptor based on the CCD (centroid contour distance) method, multiscale description, and Fourier transform. The principle of FMSCCD is simple, and the computational cost is very low. What is commendable is that its discriminability is still strong, and its compatibility with other features is also great. Experiments on three databases demonstrate its strong discriminability and operational efficiency.

Dimensional synthesis of a double-rocker linkage using the discrete Fourier transform (DFT) is presented. The coupler point curve of a double-rocker linkage is open and cannot be expressed by the Fourier series with periodic coefficients. Hence, in this study, a procedure is first proposed to transform such an open curve into a closed curve such that the DFT can be applied. Subsequently, the relations between the path of a coupler point and the design parameters of linkage are established and the motion generation problem is formulated as two prescribed paths having a constant distance. Finally, a least-square based optimization scheme is used to synthesize the design parameters and two numerical examples are illustrated for the procedure.

In the current study, we tested the effectiveness of a method using brain shape information for classification of healthy subjects and Alzheimer's disease patients. A P-type Fourier descriptor was used as shape information, and the lateral ventricle excluding the septum lucidum was analyzed. Using a combination of several descriptors as features, we performed classification using a support vector machine. The results revealed classification accuracy of 87.5%, which was superior to the accuracy achieved using volume ratio to intracranial volume (81.5%), which is widely used for conventional evaluation of morphological changes. The current findings suggest that shape information may be more useful in diagnosis, compared with conventional volume ratio.

The emergence of civil-use depth sensors made vision-based man-machine interaction possible through hand gesture, which relies fundamentally on hand pose estimation techniques. In this work, a novel hand pose estimation method is proposed, based essentially on Fourier-descriptor-based indexing in a pre-built hand template library. The proposed method takes color-depth as input, extracts necessary hand information in preprocessing, and estimate hand pose through two stages, from rough to accurate. This proposed hand pose estimation method is simple and efficient; quantitative and qualitative evaluations exhibit its effectiveness.

In order to distinguish two shapes with similar centroid contour distances. This paper proposes a novel descriptor-AFD-CCD (advanced Fourier descriptor based on centroid contour distances), which is based on FD-CCD ( Fourier descriptor based on centroid contour distance ) descriptor. FD-CCD only provides the distance information from the centroid point to the contour point, and the lack of direction information affects the accuracy of matching. The algorithm uses circularity and rectangularity to supplement discriminability, with low computational complexity. Tests were performed on three datasets. The experimental results show that AFD-CCD has further improved the matching accuracy while ensuring that the matching speed does not decrease.

This paper presents a generalized framework to solve m-pose, n-path points mixed synthesis problems, known as Alt-Burmester problems, using a task-driven motion synthesis approach. We aim to unify the path and motion synthesis problems into an approximate mixed synthesis framework. Fourier descriptors are used to establish a closed-form relationship between the path and orientation data. This relationship is then exploited to formulate mixed synthesis problems into pure motion synthesis ones. We use an efficient algebraic fitting based motion synthesis algorithm to enable simultaneous type and dimensional synthesis of planar four-bar linkages.

Fourier descriptor based path synthesis algorithms rely on harmonic decomposition of four-bar loop closure equation to split the design space into smaller subsets. The core of the methodology depends on calculation and fitting of Fourier descriptors. However, a uniform time parametrization is assumed in existing literature. This paper aims to explore the use of non-uniform time parametrization of input data and calculation of an optimal parametrization. Additionally, design-centric constraints have been proposed to give user enhanced control over coupler speed. As a result, this work improves the existing algorithm tremendously.

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.

International audience; With increasing number of Unmanned Aerial Vehicles (UAVs) -also known as drones- in our lives, safety and privacy concerns have arose. Especially, strategic locations such as governmental buildings, nuclear power stations etc. are under direct threat of these publicly available and easily accessible gadgets. Various methods are proposed as counter-measure, such as acoustics based detection, RF signal interception, micro-doppler RADAR etc. Computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. In this work, 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features (which are analyzed with a neural network) are used for classifying aerial targets as a drone or bird. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate.

The performance evaluation of fault diagnosis algorithm is an indispensable link in the development and acceptance of the fault diagnosis system. Aiming at the stability evaluation of the fault diagnosis model based on the characteristic clustering, an image edge detection method based on the Elliptic Fourier Descriptor (EFDSE) is proposed to evaluate the stability of the fault diagnosis model, which applies similarity measurement of image to effective evaluation of faulty diagnosis algorithm. The quantitative evaluation index of the diagnostic capability of characterization based cluster fault diagnosis model is used to provide reference for the acceptance and reliability of the diagnosis results. Finally, the effectiveness of the stability evaluation is verified by the fault data of the motor bearings.

Chebyshev polynomial descriptor for representing open curves is proposed.The reconstructed curve can accurately approximate the represented open curve.Chebyshev polynomial descriptor exhibits stability and similarity-invariant.Chebyshev polynomial descriptor can be used to analyse open curves. Effective and efficient representation of open curves is a challenging problem in statistical shape analysis. In this paper, we propose a novel shape descriptor, called Chebyshev polynomial descriptor (CPD) for representing open curves. Firstly, a general formula for the computation of CPDs and parametric equations of reconstructed curves are given; secondly, we investigate properties of CPDs, including its stability, similarity-invariant and invariance under different starting points. Finally, the reconstructed curve from CPDs is used to compute the curvature of an original open curve. Experimental results demonstrate the effectiveness of both representation of handwriting and computation of curvatures.

Automatic grading of Harumanis mangoes by its shape and weight analysis.The Fourier descriptor method was developed to grade the shape of Harumanis mango.The cylinder method was developed to grade the weight of Harumanis mango.Multi-classification using Support Vector Machine and Discriminant analysis. This paper presents the work on the use of visible imaging as a tool in grading the mangoes. A Fourier-descriptor method was applied on mango images acquired by a CCD camera, to grade the fruits by their shapes. The method was able to correctly classify 98.3% using DA and 100% using SVM. It is also possible to estimate the weight of the mangoes from their images by applying the Cylinder approximation analysis method. The scatter plot between the estimated and actual values of the weight shows high correlation, with R2 equal to 94.0%. The high prediction accuracy obtained shows that this simple formula is adequate for the prediction of fruit weight and volume (measured volume using the cylinder method). The correlation formula derived based on the collected data is determined as w=2.256V-157.7 where w is estimated weight in grams and V is estimated volume. Overall result for weight grading using our proposed method yields 95% accuracy.

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.

This paper presents a gait recognition method which is based on spatio-temporal movement characteristics of human subject with respect to surveillance camera. Different measures, like leg rise from ground (LRFG), the angles created between the legs with the centre of Mass (ABLC), angles created between the Centre of Mass Knee and Ankle with the (CKA), angles created between Centre of Mass, Wrist and knee (CWK), the distances between the control points and centre of Mass (DCC) have been taken as different features. Fourier descriptor has been used for shape extraction of individual frames of a subject. Statistical approach has been used for recognition of individuals based on the n feature vectors, each of size 23(collected from LRFG, ABLCs, CKA, CWK and DCCs) for each video frame. It has been found that recognition result of our approach is encouraging with compared to other recent methods.

Automatic orange quality classification based on computer image processing is accurate and efficient. In this paper, we discuss the orange feature extraction and description method based on image processing. Design an orange image edge detection method based on Canny operator, color characteristics description methods based on HIS model and shape characteristics description methods based on Fourier descriptor operator. The experiment result proof that Canny operator is high SNR,high accuracy and low computation;HIS model is more accord with human vision and low computation also; shape characteristics description methods based on Fourier descriptor operator is more easy to shape classification.

Person re-identification in public areas (such as airports, train stations and shopping malls) has recently received increased attention within computer vision research due, in part, to the demand for enhanced levels of security. Re-identifying subjects within non-overlapped camera networks can be considered as a challenging task. Illumination changes in different scenes, variations in camera resolutions, field of view and human natural motion are the key obstacles to accurate implementation. This study assesses the use of Generic Fourier Shape Descriptors (GFD) on person silhouettes for re-identification and furthermore identifies which sections of a subjects' silhouette is able to deliver optimum performance. Human silhouettes of 90 subjects from the CASIA dataset walking 0° and 90° to a fixed CCTV camera were used for the purpose of re-identification. Each subject's video sequence comprised between 10 and 50 frames. For both views, silhouettes were segmented into eight algorithmically-defined areas: head and neck, shoulders, upper 50%, lower 50%, upper 15%, middle 35%, lower 40% and whole body. A GFD was used independently on each segment at each angle. After extracting the GFD feature for each frame, a linear discriminant analysis (LDA) classifier was used to investigate re-identification accuracy rate, where 50% of each subject's frames were used for training and the other 50% were used for testing. The results show that 97% identification accuracy rate at the 10th rank is achieved by using GFD on the upper 50% segment of the human silhouette front (0°) side. For 90° images, using GFD on the upper 15% silhouette segment resulted in almost 98% accuracy rate at the 10th rank.

A method based on elliptic Fourier descriptor (EFD) is used for the classification of pottery ware. Traditionally, the contour lines used in elliptic Fourier descriptor is measured in 2D image. In this article, we took the pottery from Tianma-Qucun Burial site as an example, introduced a new method for obtaining data using three-dimensional scanning technology and computer programming. Compared with the traditional extraction method, this method can improve measurement speed and accuracy. The extracted data are used to classify the pottery ware by elliptic Fourier descriptor, which is different from the methods used in traditional archaeology. It provides a new tool to illustrate the relationship between objects.

In this thesis, we develop a supervised object recognition method using new global image descriptors inspired by the model of the human primary visual cortex V1. Mathematically speaking, the latter is modeled as the semi-discrete roto-translation group SE (2,N)=R² x ZN semi-direct product between R² and ZN. Therefore, our technique is based on generalized and rotational Fourier descriptors defined in SE (2,N) , and which are invariant to natural geometric transformations (translations, and rotations). Furthermore, we show that such Fourier descriptors are weakly complete, in the sense that they allow to distinguish over an open and dense set of compactly supported functions in L² (SE(2,N)) , hence between real-world images. These descriptors are later used in order to feed a Support Vector Machine (SVM) classifier for object recognition purposes. We have conducted a series of experiments aiming both at evaluating and comparing the performances of our method against existing both local - and global - descriptor based state of the art techniques, using the RL, the CVL, and the ORL face databases, and the COIL-100 image database (containing various types of objects). The obtained results have demonstrated that our approach was able to compete with many existing state of the art object recognition techniques, and to outperform many others. These results have also shown that our method is robust to noise. Finally, we have applied the proposed method on vessels recognition in the framework of maritime surveillance., Dans cette thèse, nous développons une approche supervisée de reconnaissance d’objets basée sur l’utilisation de nouveaux descripteurs d’images globaux inspirés du modèle du cortex visuel humain primaire V1 en tant que groupe de roto-translations semi-discrètes SE (2,N)=R² x ZN produit semi-direct entre R² et ZN. La méthode proposée est basée sur des descripteurs de Fourier généralisés et rotationnels définis sur le groupe SE (2,N), qui sont invariants aux transformations géométriques (translations, et rotations). De plus, nous montrons que ces descripteur de Fourier sont faiblement complets, dans le sens qu’ils permettent de discriminer sur un ensemble ouvert et dense L² (SE(2,N)) de fonctions à support compact, donc distinguer entre des images réelles. Ces descripteurs sont ensuite utilisés pour alimenter un classifieur de type SVM dans le cadre de la reconnaissance d’objets. Nous avons mené une séries d’expérimentations dans le but d’évaluer notre méthode sur les bases de visages RL, CVL et ORL et sur la base d’images d’objets variés COIL-100, et de comparer ses performances à celles des méthodes basées sur des descripteurs globaux et locaux. Les résultats obtenus ont montré que notre approche est en mesure de concurrencer de nombreuses techniques de reconnaissance d’objets existantes et de surpasser de nombreuse autres. Ces résultats ont également montré que notre méthode est robuste aux bruits. Enfin, nous avons employé la technique proposée pour reconnaître des navires dans un contexte de surveillance maritime.

Records of cases involving neurological disorders often exhibit abnormalities in the gait pattern of an individual. As mentioned in various articles, the causes of various gait disorders can be attributed to neurological disorders. Hence analysis of gait abnormalities can be a key to predict the type of neurological disorders as a part of early diagnosis. A number of sensor-based measurements have aided towards quantifying the degree of abnormalities in a gait pattern. A shape oriented motion based approach has been proposed in this paper to envisage the task of classifying an abnormal gait pattern into one of the five types of gait viz. Parkinsonian, Scissor, Spastic, Steppage and Normal gait. The motion and shape features for two cases viz. right-leg-front and left-leg-front will be taken into account. Experimental results of application on real-time videos suggest the reliability of the proposed method.

Aiming at the innovation of the contours of automobile side, this paper presents an innovative design method of vehicle side profile based on Fourier descriptor. The design flow of this design method is: pre-processing, coordinate extraction, standardization, discrete Fourier transform, simplified Fourier descriptor, exchange descriptor innovation, inverse Fourier transform to get the outline of innovative design. Innovative concepts of the innovative methods of gene exchange among species and the innovative methods of gene exchange among different species are presented, and the contours of the innovative design are obtained separately. A three-dimensional model of a car is obtained by referring to the profile curve which is obtained by exchanging xenogeneic genes. The feasibility of the method proposed in this paper is verified by various aspects.

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures., Comment: under review

Sign languages are used all over the world as a primary means of communication by deaf people. Sign language translation is a promising application for vision-based gesture recognition methods. Therefore, it is need such a tool that can translate sign language directly. This paper aims to create a system that can translate static sign language into textual form automatically based on computer vision. The method contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification approach to recognize the signs. The system was applied to recognize each 120 stored images in database and 120 images which is captured real time by webcam. We also translated 5 words in video sequences. The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored images in database and 69 % for testing data which is captured real time by webcam.

Indian Sign Language (ISL) interpretation is the major research work going on to aid Indian deaf and dumb people. Considering the limitation of glove/sensor based approach, vision based approach was considered for ISL interpretation system. Among different human modalities, hand is the primarily used modality to any sign language interpretation system so, hand gesture was used for recognition of manual alphabets and numbers. ISL consists of manual alphabets, numbers as well as large set of vocabulary with grammar. In this paper, methodology for recognition of static ISL manual alphabets, number and static symbols is given. ISL alphabet consists of single handed and two handed sign. Fourier descriptor as a feature extraction method was chosen due the property of invariant to rotation, scale and translation. True positive rate was achieved 94.15% using nearest neighbourhood classifier with Euclidean distance where sample data were considered with different illumination changes, different skin color and varying distance from camera to signer position.&nbsp

Radon transform is not only robust to noise, but also independent on the calculation of pattern centroid. In this paper, Radon–Mellin transform (RMT), which is a combination of Radon transform and Mellin transform, is proposed to extract invariant features. RMT converts any object into a closed curve. Radon–Fourier descriptor (RFD) is derived by applying Fourier descriptor to the obtained closed curve. The obtained RFD is invariant to scaling and rotation. (Generic) R-transform and some other Radon-based methods can be viewed as special cases of the proposed method. Experiments are conducted on some binary images and gray images.

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.

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

In this chapter, a mobile system is proposed to aid doctors and general users in monitoring skin cancer. In this system, image retrieval techniques are used to help users find similar skin cancer cases in a database. The main idea of the system is to use shape features of a query image taken by a smartphone or uploaded from other resources to perform content-based image retrieval. In the system, the shape features of an image are first extracted by the skin lesion extraction method proposed in Ref. 1, then the features are used to match the skin lesions in the database. The features in the proposed system are obtained by Fourier descriptor. We developed an iPhone application with which users can compare the skin lesions detected by an iPhone to the skin lesion cases stored in a database and potentially detect malignant skin lesions early on in their development.