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In this work, we propose a novel multilingual word spotting framework based on Hidden Markov Models that works on corpus of multilingual handwritten documents and documents that contain more than one handwritten script. The system deals with large multilingual vocabularies without need for word or character segmentation. A keyword is represented by concatenating its character models. We propose and...
In likelihood ratio based decision methods, often a variable number of input evidences is used. A decision based on many such inputs can result in nearly the same likelihood ratio as one based on few inputs. We consider methods for distinguishing between such situations. One of these is to provide confidence intervals together with the decisions and another is to combine the inputs using weights....
In this paper, we propose an approach for automated mitosis detection, which provides critical information during performing breast cancer prognosis. Essentially, the problem of mitotic detection involves irregular shape object classification. It is a very challenging task. In this paper, a novel algorithm, named eXclusive Independent Component Analysis (XICA) is proposed. The XICA is an extension...
Sparse representation based classification (SRC) has been widely used for face recognition (FR). Although SRC algorithm is also adopted in human action recognition, the evaluations of different regular terms have not been given. In this paper, we will discuss and evaluate the role of different regular terms of SRC in human action recognition, after that, we propose human action recognition algorithm...
This paper addresses the problem of binary classifier learning when the training data is imbalanced, i.e. the samples of the two classes have significantly different cardinality. We investigate two different cost-sensitive approaches in the conditional mutual information (CMI) based weak classifier selection procedure using histogram descriptors. The first method uses CMI for classifier selection,...
This paper investigates the problem of gait-based gender classification in unconstrained environments. Different from existing human gait analysis and recognition methods which assume that humans walk in controlled environments, we aim to recognize human gender from uncontrolled gaits in which people can walk freely and the walking direction of human gaits may be time-varying in a singe video clip...
The codebooks play a decisive role in the Hough Transform based object detection. We propose a novel approach to generate the codebooks in the manner of parametric regression and integrate inside semantic information drawn from objects and background. Clustering is a popular method for deriving codebooks, but it generally relies on some parameters, which heavily affect the performance of the approaches...
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