Pekka Paalanen

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Statistical methods have certain advantages which advocate their use in pattern recognition. One central problem in statistical methods is estimation of class conditional probability density functions based on examples in a training set. In this study maximum likelihood estimation methods for Gaussian mixture models are reviewed and discussed from a(More)
We present a novel method for localizing faces in person identification scenarios. Such scenarios involve high resolution images of frontal faces. The proposed algorithm does not require color, copes well in cluttered backgrounds, and accurately localizes faces including eye centers. An extensive analysis and a performance evaluation on the XM2VTS database(More)
Several novel and particularly successful object and object category detection and recognition methods based on image features, local descriptions of object appearance, have recently been proposed. The methods are based on a localization of image features and a spatial constellation search over the localized features. The accuracy and reliability of the(More)
In this study a confidence measure for probability density functions (pdfs) is presented. The measure can be used in one-class classification to select a pdf threshold for class inclusion. In addition, confidence information can be used to verify correctness of a decision in a multi-class case where for example the Bayesian decision rule reveals which class(More)
Several novel methods based on locally extracted object features and spatial constellation models have recently been introduced for invariant object detection and recognition. The accuracy and reliability of the methods depend on the success of both tasks: evidence extraction and spatial constellation model search. In this study an accurate and efficient(More)
In this paper we apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probability model. In contrast to the recent semi- supervised methods we do not utilise interest point detectors, but apply a supervised approach where local image features (landmarks) are(More)
Interest towards image mosaicing has existed since the dawn of photography. Many automatic digital mosaicing methods have been developed, but unfortunately their evaluation has been only qualitative. Lack of generally approved measures and standard test data sets impedes comparison of the works by different research groups. For scientific evaluation, mosaic(More)