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Occlusion handling is an old but important problem for the computer vision and pattern recognition community. Features from different objects may twist with each other, and any matched feature points may belong to different objects for many traditional object recognition algorithms. To recognize occlusions, we should not only match objects from different view points but also match features extracted from the same object. In this paper, we propose a method to consider these two perspectives simultaneously by encoding various types of features, such as geometry, color and texture relationships among feature points into a matrix and find the best quadratic feature correlation model to fit them. Experiments on our own built dataset and the publicly available PASCAL VOC dataset shows that, our method can robustly classify objects and handle occluded objects under large occlusions, and the performance is among the state-of-the-art. [ABSTRACT FROM AUTHOR]

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation. [ABSTRACT FROM AUTHOR]

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method of reconstructing and recognizing broken handwritten digits. Constrained dilation algorithms are used to bridge small gaps and smooth some spurious points. The contours of broken handwritten digits are smoothed and linearized, and a set of structural points of digits are detected along the outer contours of digits. These structural points are used to describe the morphological structure of broken digits. The broken digits are skeletonized with an improved thinning algorithm. Spurious segments introduced during the extraction of digit fields are detected and deleted based on the structure analysis of digit fields, segment recognition, segment extension, skeleton structure and geometrical features. The broken points of the digits are preselected based on the minimum distance between the "end" points of skeletons of two neighboring regions. The correction rules of the preselected broken points are also based on the structure analysis and comparison of broken digits. Experimental results showing the effectiveness of the method are given. [ABSTRACT FROM AUTHOR]

Brownboost is an adaptive, continuous time boosting algorithm based on the Boost-by-Majority (BBM) algorithm. Though it has been little studied at the time of writing, it is believed that it should prove especially robust with respect to noisy data sets. This would make it a very useful boosting algorithm for real-world applications. More familiar algorithms such as Adaboost, or its successor Logitboost, are known to be especially susceptible to overfitting the training data examples. This can lead to a poor generalization error in the presence of class noise, since weak hypotheses induced at later iterations to fit the noisy examples will tend to be given undue influence in the final combined hypothesis. Brownboost allows us to specify an expected base-line error rate in advance, corresponding to our prior beliefs about the proportion of noise in the training data, and thus avoid overfitting. The original derivation of Brownboost is restricted to binary classification problems. In this paper we propose a natural multiclass extension to the basic algorithm, incorporating error-correcting output codes and a multiclass gain measure. We test two-class and multiclass versions of the algorithm on a number of real and simulated data sets with artificial class noise, and show that Brownboost consistently outperforms Adaboost in these situations. [ABSTRACT FROM AUTHOR]

Face recognition, a kind of biometric identification, researched in several fields such as computer vision, image processing, and pattern recognition is a natural and direct biometric method. Face Recognition Technology has diverse potential over applications in the fields of information security, law enforcement and surveillance, smart cards, access control and more. Face recognition is one of the diverse techniques used for identifying an individual. Generally the image variations because of the change in face identity are less than the variations among the images of the same face under different illumination and viewing angle. Illumination and pose are the two major challenges, among the several factors that influence face recognition. After pose and illumination, the main factors that affect the face recognition performance are occlusion and expression. So in order to overcome these issues, we proposed an efficient face recognition system based on partial occlusion and expression. The similar blocks in the face image are identified and occlusion can be recovered using the block matching technique. This is combined with expression normalized by calculating the Empherical Mode Decomposition feature. Finally, the face can be recognized by using the PCA. From the implementation result, it is evident that our proposed method based on the PCA technique recognizes the face images effectively. [ABSTRACT FROM AUTHOR]