Abstract The use of binary systems as accelerators in the vulcanization of rubber has received considerable attention, since many of them in suitable combinations, have been shown to provide efficient vulcanization systems. Dogadkin and co-workers and Skinner and Watson reported mutual activation with a number of popular accelerator combinations. It was suggested that the mutual activation occurs by the interaction of the accelerators to form intermediate complexes which decompose to produce free radicals responsible for initiating the various reactions involved in the vulcanization process. Recently Krymowski and Taylor studied the reaction between N-oxydiethylenethiocarbamyl-N′-oxydiethylenesulfenamide (OTOS) and N-oxydiethylene-2-benzothiazylsulfenamide (OBTS), a synergistic accelerator system, in tetrachloroethane at 142°C, and identified the various products formed. Many of these reaction products themselves are familiar as vulcanization accelerators and thus may contribute to the synergistic activity observed with the OTOS-OBTS system. In the present investigation, we have studied the reaction between thiocarbamylsulfenamides and dibenzothiazyl disulfide (MBTS) in the solid phase in order to get an insight into the mutual activity provided by this system in filled and gum vulcanizates of NR.
This paper presents a novel Hybrid Approach to Face Recognition using Generalized Two-Dimensional Fisher’s Linear Discriminant (HAFR G-2DFLD) Method. It has been seen that the facial changes due to variations of pose, illumination, expression, etc. are appeared only some regions of the whole image. Therefore, the conventional face recognition methods, which use whole image for feature extraction and recognition, do not result much success. To cope with the above facial changes, face images are divided into a number of non-overlapping sub-images and then G-2DFLD method is applied to each of these sub-images as well as to the whole image to extract local and global discriminant features, respectively. A multi-class SVM is used as a classifier for each of the sub-images and also for the whole image for recognition based on those extracted features. Finally, a decision is made for recognition of the image by fusing the decisions of the individual SVMs. The proposed HAFR G-2DFLD method was evaluated on two popular face recognition databases, the AT&T and the UMIST face databases. The experimental results show that the new HAFR G-2DFLD method outperforms the conventional global feature extraction methods like, PCA, 2DPCA, PCA+FLD, 2DFLD and G-2DFLD methods in terms of face recognition.
Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters. After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features. These features are then fed to Neural classifier and in support vector machine for classification. In neural classifier, we explored three ways of combining decisions of four MLP's designed for four different features.
This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.
This paper proposes a technique for automatic face recognition using integrated multiple feature sets extracted from the significant blocks of a gradient image. We discuss about the use of novel morphological, local directional pattern (LDP) and gray-level co-occurrence matrix GLCM based feature extraction technique to recognize human faces. Firstly, the new morphological features i.e., features based on number of runs of pixels in four directions (N,NE,E,NW) are extracted, together with the GLCM based statistical features and LDP features that are less sensitive to the noise and non-monotonic illumination changes, are extracted from the significant blocks of the gradient image. Then these features are concatenated together. We integrate the above mentioned methods to take full advantage of the three approaches. Extraction of the significant blocks from the absolute gradient image and hence from the original image to extract pertinent information with the idea of dimension reduction forms the basis of the work. The efficiency of our method is demonstrated by the experiment on 1100 images from the FRAV2D face database, 2200 images from the FERET database, where the images vary in pose, expression, illumination and scale and 400 images from the ORL face database, where the images slightly vary in pose. Our method has shown 90.3%, 93% and 98.75% recognition accuracy for the FRAV2D, FERET and the ORL database respectively.
This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
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