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Cancer is nowadays considered as one of the most dangerous diseases in the world. Especially, breast cancer represents for women the second most common type of cancer and is a main cause of cancer dead. This paper presents a novel method for breast cancer detection from mammographic images based on Local Binary Patterns (LBP). This approach successfully uses LBP based features with a classifier and...
In this paper, we propose a new divide-and-conquer based method, called fusion of multiple binary age-grouping-estimation systems, for human facial age estimation. Under a specific constraint, such as a given facial feature or classification/regression method, what is the better framework for age estimation? First we employ multiple binary-grouping systems for age group classification. Each face image...
Blind steganalysis techniques are able to detect the presence of secret messages embedded in digital media files, such as images, video, and audio, with an unknown steganography algorithm. This paper present an image steganalysis method based on Evidential K-Nearest Neighbors (EV-knn). Originality of this work is the use of theoretical framework of Belief functions on different subspaces of features...
Smile detection in the wild is an interesting and challenging problem. This paper presents an efficient approach with hierarchical visual feature to handle this problem. In our approach, Gabor filters with multi-scale, multi-orientation are first applied to extract facial textures namely Gabor faces from the input face image. After this, Histograms of Oriented Gradients (HOG) are employed to encode...
Passive Millimeter Wave Images (PMMWI) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render difficult this task. In this paper we propose a method that combines image processing and statistical machine learning techniques to solve this localization/detection problem...
This research presents an abnormal beat detection scheme from lead II Electrocardiogram (ECG) signals along with some improvements on feature extraction. A set of 16 features representing positions, durations, amplitudes and shapes of P, Q, R, S and T waves is proposed in this work for heart beat classification. These features carry important medical information for normal and abnormal beat detection...
Cyclic alternating patterns (CAPs) occur during normal sleep, but higher CAP rates are associated with abnormal conditions, such as epilepsy. Efficient automatic classification of CAP A-phase sub-types would be of remarkable importance for the consideration of CAP as a disease bio-marker. This paper reports a multi-step methodology for the classification of A-phases subtypes. The methodology encompasses:...
One of the challenges faced by automatic facial emotion recognition nowadays is the ability to deal with complicated environmental conditions such as noisy environments. In order to solve this problem, this paper aims to examine facial emotion recognition under noisy environment using empirical mode decomposition (EMD). EMD is a multiresolution technique which is adaptively decomposed nonstationary...
A method of fish category recognition is proposed. Our novelty is in feature extraction technique, which is based on determining the curvature of the contour of fish image. The category recognition of test fish is done by a suitable linear multi-class SVM classifier. To apply our algorithm, we have developed a fish image database containing six different categories of fish. The overall accuracy of...
Online signature verification has been a research topic of great interest, in line with the increasingly high security requirements of a networked society. In this paper, an online signature verification system based on the nearest template matching is designed. A set of function features are employed for comparing the dissimilarity between the test signature and the template database. The difference...
In this paper, a robust technique to construct feature vector for gender classification has been proposed. Discrete Wavelet transform is used in concatenation with Discrete Cosine transform to form the feature vector. Initially, multi-level Discrete Wavelet transform is applied to images to obtain the approximation coefficients of image. Discrete Cosine transform are then calculated for the obtained...
Recent times have been marked with the increasing demand for more intelligent human computer interfaces. By adding emotion recognition abilities, voice based interfaces can be made more human centric. As natural languages do not share similar acoustic-phonetic features and vary in production of speech sound, the emotion recognition accuracy gets affected with respect to the user's language. This work...
A query image based scene/image retrieval system is a system that analyzes the properties of a query image and identifies the class in which the image belongs and retrieves a number of images which are most alike and relevant to the query image. A scene/image classifier provides the first stage for this system. Scene classification is the process that analyzes the properties of various image features...
This research aims to detect uncertainty based on facial expression in a learning context with the use of the Facial Action Coding System (FACS). Although FACS has been used to categorize facial uncertainty, very few studies have worked in this field. Most studies rather focus on uncertainty detection using voice, even though uncertainty is more apparent through facial cues compared to vocal cues,...
Usually, music is generally classified on the basis of its genre which indicates its musical style or musical form based on some sort of shared history. On the contrary, this paper aims to classify a given track into a mood such as happy, sad, peaceful and angry rather than based on its genre because more often than not, the listener prefers to hear songs similar to each other both in terms of the...
In this paper, we proposed a new algorithm based on independent keypoints databases for indoor place recognition. In analogy with set operation, a new kind of operations for keypoints sets are defined to describe the process of independent keypoints database establishment and place classification. To obtain the databases, keypoints are firstly extracted from sample images whose class are known, and...
Affective states classification has become an important part of the Brain-Computer Interface (HCI) study. In recent years, affective computing systems using physiological signals, such as ECG, GSR and EEG has shown very promising results. However, like many other machine learning studies involving physiological signals, the bottle neck is always around the database acquisition and the annotation process...
In this paper, we propose a feature selection and representation combination method to generate discriminative features for speech emotion recognition. In feature selection stage, a Multiple Kernel Learning (MKL) based strategy is used to obtain the optimal feature subset. Specifically, features selected at least n times among 10-fold cross validation are collected to build a new feature subset named...
From past to present, individuals' appreciation of taste has always been wondered. Moreover, there is an increasing research interest in measuring taste appreciation. Most of the previous work in this area are psychological studies that rely on manual coding of facial actions and/or emotional expressions. Consequently, these studies depend on human observations. We propose a preliminary study for...
Convolutional Neural Network (CNN) has attracted much attention for feature learning and image classification, mostly related to close range photography. As a benchmark work, we trained a relatively large CNN to classify SAR image patches into five different categories, where the image patches tiled and annotated from a typical TerraSAR-X spotlight scene of Wuhan, China. The neural network designed...
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