Trajectory-based sign language recognition using Discriminant Analysis in higher-dimensional feature space

Sign language is a basic method for solving communication problems between deaf and hearing people. In order to communicate, deaf and hearing people normally use hand gestures, which include a combination of hand positioning, hand shapes, and hand movements. Thai Sign Language is the communication method for Thai hearing-impaired people. Our objective is to improve the dynamic Thai Sign Language translation method with a video captioning technique that does not require prior hand region detection and segmentation through using the Scale Invariant Feature Transform (SIFT) method and the String Grammar Unsupervised Possibilistic C-Medians (sgUPCMed) algorithm. This work is the first to propose the sgUPCMed algorithm to cope with the unsupervised generation of multiple prototypes in the possibilistic sense for string data. In our experiments, the Thai Sign Language data set (10 isolated sign language words) was collected from 25 subjects. The best average result within the constrained environment of the blind test data sets of signer-dependent cases was 89–91%, and the successful rate of signer semi-independent cases was 81–85%, on average. For the blind test data sets of signer-independent cases, the best average classification rate was 77–80%. The average result of the system without a constrained environment was around 62–80% for the signer-independent experiments. To show that the proposed algorithm can be implemented in other sign languages, the American sign language (RWTH-BOSTON-50) data set, which consists of 31 isolated American Sign Language words, is also used in the experiment. The system provides 88.56% and 91.35% results on the validation set alone, and for both the training and validation sets, respectively.

Principal Components Analysis (PCA) is used to classify the running condition of a machine by means of projecting the original data to the Principal Components space. However, if the data are concentrated in a nonlinear subspace, PCA will fail to work well. Kernel Principal Components Analysis (KPCA) transforms the input data from the original input space into a higher dimensional feature space with the nonlinear mapping, and then uses the nonlinear principal components to realize the classification. In this paper a case of gear fault diagnosis was studied with KPCA. The characteristic values of frequent domain from vibration signals of the gearbox under the running condition were extracted, and the KPCA method was used to classify gear crack fault. The result shows that KPCA is more effective to distinguish the state of the gear and more suitable to diagnose the gear faults in early stage.

Kernel principal component analysis (KPCA) is a method for performing a nonlinear form of principal component analysis (PCA). It involves nonlinear mapping of the input data onto a high dimensional feature space and performing PCA there. Through KPCA, nonlinear relations in the input space are detected as linear relations in the feature space. Furthermore, explicit computation of the mapping is not necessary in KPCA. Only dot products of the mapped data items in the feature space are required which are obtained directly from the input data using a kernel function. Moreover, control limits of traditional statistical process monitoring indices are usually determined under assumption of normal (Gaussian) distribution. Although, it is recognized that this is inappropriate in nonlinear processes, the impact of using kernel density estimation (KDE), which is a nonparametric method, to determine control limits in KPCA-based process monitoring has not been reported. This paper seeks to bridge this gap. The KPCA with KDE approach is applied to the Tennessee Eastman process for the detection of faults. The results confirm that associating KPCA with kernel density estimated control limits provides better monitoring performance than using control limits based on the normal probability density function.

Summary The principal component analysis (PCA) is exists the problem of lack of probability model and the absence of higher-order statistics information. Therefore，in order to overcome its shortcomings，this paper studies the method that can overcome two disadvantages of the principal component analysis (PCA) — the probability kernel principal component analysis (PKPCA) which is based on Bayesian theory and kernel principal component analysis (KPCA). First，the sample data are mapped to the high dimensional feature space，then define probability model of the data in high-dimensional space，and finally，expectation maximization (EM) estimated is used to get the best results. This method has both the advantage of probability analysis and kernel principal component analysis (KPCA). It is able to effectively adapt to more complex reservoir conditions and can realize the non-linear probability analysis. The probability of kernel principal component analysis (PKPCA) is applied to reservoir prediction of the oilfield. The predicted results show that the method can improve the precision attribute optimization，while improving the accuracy of the forecasts of reservoir.

Manufacturing industries collect massive amounts of multivariate measurement through automated inspection processes. Noisy measurements and high-dimensional, irrelevant features make it difficult to identify useful patterns in the data. Principal component analysis provides linear summaries of datasets with fewer latent variables. Kernel Principal Component Analysis (KPCA), however, identifies nonlinear patterns. One challenge in KPCA is to inverse map the denoised signal from a high-dimensional feature space into its preimage in input space to visualize the nonlinear variation sources. However, such an inverse map is not always defined. This article provides a new meta-method applicable to any KPCA algorithm to approximate the preimage. It improves upon previous work where a strong assumption was the availability of noise-free training data. This is problematic for applications such as manufacturing variation analysis. To attenuate noise in kernel subspace estimation the final preimage is estimated as the average from bagged samples drawn from the original dataset. The improvement is most pronounced when the parameters differ from those that minimize the error rate. Consequently, the proposed approach improves the robustness of any base KPCA algorithm. The usefulness of the proposed method is demonstrated by analyzing a classic handwritten digit dataset and a face dataset. Significant improvement over the existing methods is observed.

Viewing to monitoring the continuous casting process, a nonlinear fault detection method based on kernel principal component analysis (KPCA) was introduced. KPCA can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions, which is to first map the input space into a feature space via nonlinear mapping and then to compute the principal components in that feature space. Based on T2 and SPE charts in feature space, principal component analysis(PCA)can be used to detect faults.. The simulation results show that the proposed approach effectively captured the nonlinear relationship in the process variables and showed superior process monitoring performance compared to linear PCA.

There is a trend in recent machine learning community to construct a nonlinear version of a linear algorithm using the 'kernel method', e.g. Support Vector Machines (SVMs), kernel principal component analysis, kernel fisher discriminant analysis and the recent kernel clustering algorithms. In unsupervised clustering algorithms using kernel method, typically, a nonlinear mapping is used first to map the data into a potentially much higher feature space, where clustering is then performed. A drawback of these kernel clustering algorithms is thatthe clustering prototypes lie in high dimensional feature space and hence lack clear and intuitive descriptions unless using additional projection approximation from the feature to the data space as done in the existing literatures. In this paper, a novel clustering algorithm using the 'kernel method' based on the classical fuzzy clustering algorithm (FCM) is proposed and called as kernel fuzzy c-means algorithm (KFCM). KFCM adopts a new kernel-induced metric in the data space to replace the original Euclidean norm metric in FCM and the clustered prototypes still lie in the data space so that the clustering results can be reformulated and interpreted in the original space. Our analysis shows that KFCM is robust to noise and outliers and also tolerates unequal sized clusters. And finally this property is utilized to cluster incomplete data. Experiments on two artificial and one real datasets show that KFCM hasbetter clustering performance and more robust than several modifications of FCM for incomplete data clustering.

In this paper, a novel feature extraction method is proposed; Genetic Programming (GP) is used to discover features, while the Fisher criterion is employed to provide fitness values. This produces nonlinear features for both two-class and multi-class recognition problems by revealing the discriminating information between classes. The proposed approach is experimentally compared to conventional nonlinear feature extraction methods, including kernel generalised discriminant analysis (KGDA), kernel principal component analysis (KPCA). Results demonstrate the capability of the proposed approach to transform information from the high dimensional feature space into a single dimensional space by automatically discovering the relationships among data.

In this paper, we present dictionary learning methods for sparse signal representations in a high dimensional feature space. Using the kernel method, we describe how the well known dictionary learning approaches, such as the method of optimal directions and KSVD, can be made nonlinear. We analyze their kernel constructions and demonstrate their effectiveness through several experiments on classification problems. It is shown that nonlinear dictionary learning approaches can provide significantly better performance compared with their linear counterparts and kernel principal component analysis, especially when the data is corrupted by different types of degradations.

A major challenge faced by our society is the difficulty individuals with disabilities encounter in expressing their emotions to others. People with disabilities often depend on sign (gesture) languages for communication. This project aims to develop a model capable of recognizing and translating sign language alphabets from hand gestures into text and audio. The primary objective is to enhance communication between individuals with hearing impairments and the broader society. We assessed our model's performance using a publicly accessible Indian Sign Language (ISL) dataset. For image classification, we employed Convolutional Neural Networks (CNNs) with the Inception V3 architecture. Hand gestures are captured through webcams, and our model identifies the corresponding alphabet for each gesture. This project seeks to overcome current challenges in Sign Language Recognition and aims to enhance its effectiveness and efficiency.

A lightweight network-based sign language robot with facial mirroring and speech system

Little is known about the nature of fingerspelling during sign language interpretation. In this small-scale, exploratory study, we examined the fingerspelling of interpreters working in five different sign languages: American Sign Language (ASL), Australian Sign Language (Auslan), British Sign Language (BSL), Irish Sign Language (ISL), and Italian Sign Language (LIS). Sixteen interpreters were video-recorded as they rendered President Barack Obama’s 2009 inaugural address in their country’s sign language. After completing their interpretations, the participants engaged in a retrospective interview about their work. The data were analyzed both quantitatively (for frequency and type of fingerspelling) and qualitatively (for factors influencing fingerspelling). Results indicate that the most fingerspelled items ( n = 137) were produced in the ASL interpretations and the fewest ( n = 18) were produced in the LIS interpretations; variation between the groups was found in lexicalized fingerspelling and the fingerspelling of place names. We suggest that the variation in fingerspelling both within and between groups may be explained by sociolinguistic factors, including interpreters’ language attitudes and perceptions of the deaf audience. This exploratory study provides a first step in investigating the fingerspelling of interpreters in a variety of sign languages.

Finding pre-images via evolution strategies

In this paper, we present a new method to de-noise speech in the complex spectral domain. The method is derived from kernel principal component analysis (kPCA). Instead of applying PCA in a high-dimensional feature space and then going back to the original input space by using a solution to the pre-image problem, only the pre-image step is applied for de-noising. We show that the de-noised audio sample is a convex combination of the noisy input data and that the resulting algorithm is closely related to the soft k-means algorithm. Compared to kPCA, this method reduces the computational costs while the audio quality is similar and speech quality measures do not degrade.

It is supposed that there exist means to generate material microstructure representations by varying processing parameters numerically [1] or experimentally [2], the goal of this work is to establish a parameterized geometrical description model from learning the set of given snapshot instances. In this work, we propose a parameterization model for the representation of the Representative Volume Elements (RVE) of composite materials based on Kernel Principal Component Analysis (KPCA) method. The set of synthetic RVE snapshots governed by a series of control parameters is firstly mapped into a high-dimensional feature space. Then, Principal Component Analysis (PCA) is performed, together with the establishment of approximated response surfaces of the retained PCA projection coefficients. We showcase the performance of KPCA-based surrogate by applying it for an optimal design of the effective mechanical properties of a two-phase composite material.