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Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background...
In this paper, we propose a system that is capable of automatically differentiating between normal and abnormal heartbeats of patients using signals acquired from electrocardiography (ECG). The components of the ECG signals, that are PQRST intervals, were studied to acquire features for classification. Different time intervals of p-wave, QRS complex and t-wave were used as features. These features...
Classification is one of the most researched issues in Machine Learning. In this study, the Lorentzian Support Vector Machine (LSVM) method is proposed that performs classification in Lorentzian space. This proposed new classifier forms a hyperplane separating the classes based on the Lorentzian metric and maximize margins between nearest points to the hyperplane according to the Lorentzian distance...
A robust model is sought for the identification of electroencephalographic (EEG) signals including movements of three distinct parts of the user's arm, namely hand, elbow and shoulder. This study investigates the classification performances of the same upper limb motor movements using various kernel functions of the support vector machine (SVM). Polynomial, linear and radial basis (RBF) functions...
In this work, we develop a video surveillance system to detect the disappearance of the objects selected by an operator. Proposed method is optimized to minimize the effects of shadows, partial occlusions and changes in illumination to minimize false alarms. The system is trained by extracting local features from the object to produce an alarm in the case of the disappearance.
Malwares can create new malware samples which have different size, structure and operation mode but same functionality in each metamorphic code generation via malicious code obfuscation methods. So they can bypass traditional signature-based malware detection systems. In this study, a pattern recognition based system that detects metamorphic malware by using summary structure of Malware Analysis Intermediate...
In this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed...
Pattern recognition for multispectral data aims to identify land cover thematics for environmental monitoring and disaster risk reduction. Multispectral images contain data acquired from different channels within the frequency spectrum. They represent a mixture of latent signals. This paper represents a pattern recognition contribution for remote sensing. We propose a new classification framework...
In this paper, we report on our ongoing efforts to build a cue identifier for mobile robot navigation using a simple one-plane LIDAR laser scanner and machine learning techniques. We used simulated scans of environmental cues to which we applied various levels of Gaussian distortion to test a number of models the effectiveness of training and the response to noise in input data. We concluded that...
The ARCADIA project aims at developing automatic image analysis and machine learning methods to facilitate the interpretation and promotion of an archaeological ceramic heritage. It consists in implementing a 3D digitalization chain of the discovered shards to extract the decoration imprinted by the potters using a carved wood wheel. The characterization of the hollow pattern from a binary map given...
Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive states to be modeled as distributed patterns of neural activity and classified according to stimulus conditions. In practice, building a robust, generalizable classification model can be challenging because the number...
Classification is a very important part in the domain of pattern recognition. However, single classifier has many defects, such as very finite applicability and low accuracy. Combining multiple classifiers can overcome the defects. Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic...
In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve...
Automatic adult video detection is a problem of interest to many organizations around the world. The aim is to restrict the easy access of underage youngsters to such potentially harmful material. Most of the existing techniques are mere extensions of image categorization approaches. In this paper we propose a video genre classification technique tuned specifically for adult content detection by considering...
This paper shows the possibility of classifying the surface of locomotion of a modular snake-like robot only from torque and current sensors in the servo-motors. Locomotion in modular snake-like robots is made from gaits that involve the entire body structure, in this particular work we use a modular snake-like robot consisting of 16 modules located 90 degrees rotated one with respect to the previous,...
One of the most important problems of the nearest neighbor and related classifiers is the distance measure. The distance measure is the fundamental part to compute the neighbors of a test instance. Using the nearest neighbors as the training instances of another classifier is a usual form of localizing a classifier such as SVM. In this paper, a method is proposed to adapt the distance measure by weighting...
In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods...
Recently, technology providing to information about road infrastructure has been developed. The information was obtained to acquire image through camera. It prevents an accident using technology measure the distances from front vehicle in advance. Also, it was used recognize lane and prevent to drop out of lane using measured distance from wheel to lane. We also get more information about road sign...
In this paper, we explored the use of certain image features, block-wise histograms of local orientations. They are used in many current object recognition algorithms, for the task of locating cephalometric landmarks on X-ray images. After reviewing existing cephalometric landmark detection systems, we show experimentally that grids of Histograms of Oriented Gradients (HOG) descriptors significantly...
Camera motion classification is an important research topic in video content analysis and retrieval. In this paper, we propose a nonparametric classification method of camera motion, which employs support vector machine to learn motion vector file and then train classifiers to categorize camera motion. Libsvm is used as support vector machine tool to validate the method and experiments demonstrate...
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