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In this paper, we document the face detection competition that we have organized in conjunction with the ISDA 2010 conference. The objective was to compare different face detection engines performance on new unpublished datasets. We believe researchers can benefit from this competition by identifying strong and weak areas in their algorithms relative to others. We have also identified, based on the...
This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired...
Cervix cancer is the most common gynecological malignancy and second most common cancer among female in Malaysia after breast cancer. The objective of this study is to extract the size of nucleus and cytoplasm, as well as gray level values of cervical cells from ThinPrep images so that accurate value of those parameters can easily be obtained. An alternative approach of extracting features for Pap...
In this paper, we propose a rule-based system for semantically understanding and analyzing the motion of the trajectories of the human activity. The proposed system can be used as a preprocessing phase for enhancing the object detection process. Detected trajectories are classified into three categories; normal, semi-normal and abnormal trajectories according to the distances between their adjacent...
Internet contains a tremendous amount of valuable web applications that can be used in many systems. To use this kind of applications with other systems, the interaction needs to be in a standard structured format such as XML web service. In this paper, we present a method to convert the current web applications into standard XML web services. The system design and implementation are presented. We...
In BCI research community, EEG based self-paced brain-computer interfaces (SBCI) have been widely researched in the past several years. SBCI systems allow individuals to control outside device using EEG signals at their own pace. But the performance of current SBCI technology is not suitable for most applications due to the difficult in detection of the non-periodic intentionally brain state changing...
Applications with street navigation have been recently introduced on mobile phone devices. A major part of existing systems use integrated GPS as input for indicating the location. However, these systems often fail or make abrupt shifts in urban environment due to occlusion of satellites. Furthermore, they only give the position of a person and not the object of his attention, which is just as important...
This paper presents insights gained from applying biological vision mechanisms in the context of computer vision algorithm design, implementation and initial evaluation. In this paper a software-based space variant log(z) retina tessellation is used to sample the underlying image, maintaining the high resolution at the central foveal region and an increasingly sparse sampling density at the surrounding...
Image thresholding is a very important phase in the image analysis process. However, different images have different characteristics making the traditional process of thresholding by one algorithm a very challenging task. That is because any thresholding method may be perform well for some images but for sure it will not be suitable for all images. In this paper, intelligent thresholding by training...
Place recognition is a vital methodology for modeling environments and localizing autonomous mobile robots topologically. It can also be integrated in a hierarchical framework where it guides a fast and more precise metric position estimation. Especially for those hierarchical frameworks, it is crucial that the place recognition modules be highly accurate. In this paper, an information-theoretic approach...
This paper studies the suitability of Extreme Learning Machines (ELM) for resolving bioinformatic and biomedical classification problems. In order to test their overall performance, an experimental study is presented based on five gene microarray datasets found in bioinformatic and biomedical domains. The Fast Correlation-Based Filter (FCBF) was applied in order to identify salient expression genes...
The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory...
In this paper we propose the method that extracts the semantic keyword from digital images automatically using color and texture features. The image semantic keyword is widely used in research area like image retrieval, categorization, annotation, management. The method consists of two steps: feature extraction and classification module. In order to extract feature, the image color and PACT (Principal...
Bag of visual patches (BOP) image representation has been the main research topic in computer vision literature for scene and object recognition tasks. Building visual vocabularies from local image feature vectors extracted automatically from images have direct effect on producing discriminative visual patches. Local image features hold important information of their locations in the image which are...
This paper presents a semi-automatic system for home video annotation that searches into the video contents and retrieves video shots for a specific person. The proposed system is composed of four phases; 1) shot detection phase that detects shots boundaries and divides the original video into shots, 2) face detection and recognition phase that detects faces in video shots based on Haar-like features...
It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult...
Having an accurate Signature Detection Classification (SDC) Model has become highly demanding for Intrusion Detection Systems (IDS) to secure networks, especially when dealing with large and complex security audit data set. Selecting appropriate network features is one of the factors that influence the accuracy of SDC model. Past research has shown that the Hidden Marcov Chain, Genetic Algorithm,...
Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper...
Studies on content-based music retrieval (CBMR) which search music by analyzing their acoustic features and defining their similarity, have been conducted actively. However, it is desirable that the similarity evaluation be adaptive to each user's demand, because the search criteria differs user by user. In this paper, we propose a framework of CBMR that tries to satisfy the various demands of different...
In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company “SemEval 2007” corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 %...
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