Set Of Geometric Features Research Articles

An automatic system of classification of weld defects in radiographic images

In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an artificial neural network (ANN) for weld defect classification was used. With the aim of obtaining the best performance of ANN three different methods for improving network generalisation was used. The results was compared with a method without generalisation. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used.

A Multimodal Approach for Face Modeling and Recognition

In this paper, we present a fully automated multi- modal (3-D and 2-D) face recognition system. For the 3-D modality, we model the facial image as a 3-D binary ridge image that contains the ridge lines on the face. We use the principal curvature to extract the locations of the ridge lines around the important facial regions on the range image (i.e., the eyes, the nose, and the mouth.) For matching, we utilize a fast variant of the iterative closest point to match the ridge image of a given probe image to the archived ridge images in the database. The main advantage of this approach is reducing the computational complexity by two orders of magnitude by relying on the ridge lines. For the 2-D modality, we model the face by an attributed relational graph (ARG), where each node of the graph corresponds to a facial feature point. At each facial feature point, a set of attributes is extracted by applying Gabor wavelets to the 2-D image and assigned to the node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of geometrical features that defines the mutual relations between the edges is extracted from the Delaunay triangles and stored in the ARG model. The similarity measure between the ARG models that represent the probe and gallery images is used for 2-D face recognition. Finally, we fuse the matching results of the 3-D and the 2-D modalities at the score level to improve the overall performance of the system. Different techniques for fusion, such as the Dempster-Shafer theory of evidence and weighted sum of scores are employed and tested using the facial images in the third experiment dataset of the Face Recognition Grand Challenge version 2.0.

Feature dimensionality reduction for the verification of handwritten numerals

A novel method based on multi-modal discriminant analysis is proposed to reduce feature dimensionality. First, each class is divided into several clusters by the k-means algorithm. The optimal discriminant analysis is implemented by multi-modal mapping. Our method utilizes only those training samples on and near the effective decision boundary to generate a between-class scatter matrix, which requires less CPU time than other nonparametric discriminant analysis (NDA) approaches [Fukunaga and Mantock in IEEE Trans PAMI 5(6):671---677, 1983; Bressan and Vitria in Pattern Recognit Lett 24(5):2473---2749, 2003]. In addition, no prior assumptions about class and cluster densities are needed. In order to achieve a high verification performance of confusing handwritten numeral pairs, a hybrid feature extraction scheme is developed, which consists of a set of gradient-based wavelet features and a set of geometric features. Our proposed dimensionality reduction algorithm is used to congregate features, and it outperforms the principal component analysis (PCA) and other NDA approaches. Experiments proved that our proposed method could achieve a high feature compression performance without sacrificing its discriminant ability for classification. As a result, this new method can reduce artificial neural network (ANN) training complexity and make the ANN classifier more reliable.

Lip geometric features for human–computer interaction using bimodal speech recognition: comparison and analysis

Bimodal speech recognition is a novel extension of acoustic speech recognition for which both acoustic and visual speech information are used to improve the recognition accuracy in noisy environments. Although various bimodal speech systems have been developed, a rigorous and detailed comparison of the possible geometric visual features from speakers' faces has not been given yet in the previous papers. Thus, in this paper, the geometric visual features are compared and analyzed rigorously for their importance in bimodal speech recognition. The relevant information of each possible single visual feature is used to determine the best combination of geometric visual features for both visual-only and bimodal speech recognition. From the geometric visual features analyzed, lip vertical aperture is the most relevant; and the set formed by the vertical and horizontal lip apertures and the first order derivative of the lip corner angle gives the best results among the possibilities of reduced set of geometric features that were analyzed. Also, in this paper, the effect of the modelling parameters of hidden Markov models (HMM) on each single geometric lip feature's recognition accuracy is analyzed. Finally, the accuracy of acoustic-only, visual-only, and bimodal speech recognition methods are experimentally determined and compared using the optimized HMMs and geometric visual features. Compared to acoustic and visual-only speech recognition, the bimodal speech recognition scheme has a much improved recognition accuracy using the geometric visual features, especially in the presence of noise. The results obtained showed that a set of as few as three labial geometric features are sufficient to improve the recognition rate by as much as 20% (from 62%, with acoustic-only information, to 82%, with audio-visual information at a signal to noise ratio (SNR) of 0 dB).

Mobile vehicle navigation in unknown environments: a multiple hypothesis approach

The paper describes an algorithm for sensor-based map building and navigation for an autonomous mobile vehicle. The algorithm is based on the use of an extended Kalman filter to obtain estimates of the location and identity of geometric features in an unknown environment. A multitarget tracking methodology is applied to the evaluation of multiple hypotheses about the locations of geometric features in the environment. The algorithm does not require any a priori information about the environment. It is capable of initiating new geometric features and identifying the type of a geometric feature from the given set of geometric features, utilising the data provided by a set of sonar sensors. The algorithm is also capable of deleting geometric features from the map of the environment when they are no longer detected by the sensors. The implementation of the algorithm is discussed, and results using real sonar data are presented.

A hierarchical framework for robust extraction and delineation of geometric features

The problem of the delineation of a set of geometric features in a hierarchical environment is addressed. A brief summary of a feature extraction and bottom-up propagation technique is given. A top-down process is proposed toward feature delineation based on a general model for hierarchical dependencies. The main idea is to make explicit the compromise between local and global consistency in the context of hierarchical reasoning by means of consecutive levels of interaction. Several examples in multi-thresholding of gray images, line detection and delineation, and range image segmentation are presented.

Face recognition: features versus templates

Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching. >