In our multi-view face and eye detection, we use a cascaded classifier trained by gentle AdaBoost algorithm, one of the appearance-based pattern learning method. Specifically, in order to detect multi-view face, we propose a special cascaded classifier using coarse-to-fine search, simple-to-complex search, and parallel-to-separated search. In order to detect eye, we propose a four-step eye dete...

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over predefined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order st...

Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important fo...

Large-area electronics (LAE) enables the formation of a large number of sensors capable of spanning dimensions on the order of square meters. An example is X-ray imagers, which have been scaling both in dimension and number of sensors, today reaching millions of pixels. However, processing of the sensor data requires interfacing thousands of signals to CMOS ICs, because implementation of comple...

In object recognition problems a two-stage system is usually adopted composed of a fast and simple detector and a more complex classifier. This paper studies a design of the second stage classifier based on the recently proposed trainable similarity measure which is specifically designed for supervised classification of images. Common global measures such as correlation suffer from uninformativ...

This paper introduces an automatic real-time gender classification system. The system consists of mainly three modules, face detection, normalization and gender classification. The LUT-type weak classifier based Adaboost learning method is proposed for training both face detector and gender classifier, and a Simple Direct Appearance Model (SDAM) based method is developed to detect the facial la...

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train t...

This paper presents a multimodal person identification system based on combination of audio and visual classifiers. The audio classifier was built by using mel-frequency cepstrum coefficient features and Gaussian mixture models. The visual classifier was implemented by Haar-like features and AdaBoost algorithm for face detection, and principal component analysis for identification. A new method...

In this work, we describe the protein secondary structure prediction module of a distributed bio-informatics system. Protein databases contain over a million of sequenced proteins, however there is structuring information for at most 2% of that number. The challenge is to reliably predict the structure based on classifiers. Our contribution is the evaluation of architectures of multiple classif...

In this work, pruning techniques for the AdaBoost classifier are evaluated specially aimed for a continuous learning framework in sensors mining applications. To assess the methods, three pruning schemes are evaluated using standard machine-learning benchmark datasets, simulated drifting datasets and real cases. Early results obtained show that pruning methodologies approach and sometimes out-p...