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Supervised Learning (SL) is a machine learning research area which aims at developing techniques able to take advantage from labeled training samples to make decisions over unseen examples. Recently, a lot of tools have been presented in order to perform machine learning in a more straightforward and transparent manner. However, one problem that is increasingly present in most of the SL problems being...
A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike moments, Circular-Mellin features, Haralick features, Fourier Power Spectrum, Wavelets, Gabor Filters, and a set features extracted from HSV color space are extracted. Adaboost learning...
The `displacement expert' has recently proven popular for rapid tracking applications. In this paper, we note that experts are typically constrained only to produce approximately correct parameter updates at training locations. However, we show that incorporating constraints on the gradient of the displacement field within the learning framework results in an expert with better convergence and fewer...
In this paper, a two-stage off-line signature verification system based on dissimilarity representation is proposed. In the first stage, a set of discrete left-to-right HMMs trained with different number of states and codebook sizes is used to measure similarity values that populate new feature vectors. Then, these vectors are input to the second stage, which provides the final classification. Experiments...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Conditional random field methods (CRFs) have gained popularity for image labeling tasks in recent years. In this paper, we describe an alternative discriminative approach, by extending the large margin principle to incorporate spatial correlations among neighboring pixels. In particular, by explicitly enforcing the sub modular condition, graph-cuts is conveniently integrated as the inference engine...
In this paper, a novel human articulated pose estimation method based on AdaBoost algorithm is presented. The human articulated pose is estimated by locating major human joint positions. We learn the classifiers on a normalized image for classifying each pixel position into a certain category. Two different kinds of classifiers, bottom-up joint position classifier and top-down skeleton classifier,...
This paper aims at detecting preceding vehicles in a variety of distance. A sub-region up-scaling scheme significantly raises far distance detection capability. Three frame pipeline structures involving object predictors are explored to further enhance accuracy and efficiency. It claims a 140-meter detecting distance along proposed methodology. 97.1% detection rate with 4.2% false alarm rate is achieved...
Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to...
This paper presents an experimental evaluation of three different traffic sign detection approaches, which detect or localize various types of traffic signs from real-time videos. Specifically, the first approach exploits geometric features to identify traffic signs, while the other two are developed based on SVM (Support Vector Machine) and AdaBoost learning mechanisms. We describe each of the three...
Document Image Binarization techniques have been studied for many years, and many practical binarization techniques have been developed and applied successfully on commercial document analysis systems. However, the current state-of-the-art methods, fail to produce good binarization results for many badly degraded document images. In this paper, we propose a self-training learning framework for document...
It is well known that the color of many natural and man-made objects is often very similar to that of human skin, such as sand, brick, to name a few. For the task of skin detection, it is often a very challenge task to identify the right skin locations while being robust against the distraction of these objects. In this paper, we present an on-line learning approach to model human skin by utilizing...
The work in this paper deals with the learning of gradual rules in the framework of data classification. Gradual rules are well suited to express constraints between numerical quantities. They are here used to constrain the shape of classes to be modeled. More precisely, it is proposed to represent convex polygon-shaped classes by means of "If-Then" classification gradual rules. The latter,...
In this paper, we propose a new method for modeling appearance variances in generic object tracking task. Although object tracking has been studied by many researchers for a long time, there are still many challenging problems, which is mainly due to the complex variances of object's appearance. While most of traditional methods using a global or pixel-wise approach, we proposed a part-based tracking...
In many image processing applications, image thresholding is considered to be an important task. Opposition-Based Learning (OBL) was recently introduced and used to enhance different computation algorithms. In this paper, a new thresholding algorithm is proposed by utilizing the concept of opposite fuzzy sets. The algorithm is applied on general set of images and compared with the previous opposition-based...
This paper proposes a novel hardware structure and FPGA implementation method for real-time detection of multiple human faces with robustness against illumination variations and Rotated faces. These are designed to greatly improve face detection in various environments, using the Adaboost learning algorithm and MCT techniques, Rotation Transformation, which is robust against variable illumination...
Nowadays, learners use not only PC but also their own mobile devices for e-learning. For this, author should make various versions of learning contents for device specification and learning management system provides suitable contents for device specification. To select suitable contents for learner's devices, previous researches propose contents selection method that describe suitability score for...
In this paper, a new image interpolation algorithm is proposed due to the inspiration of knowledge-based learning and dynamical control strategy which are based on computational verb theory. This algorithm takes gray level profiles and contour shapes as two processing factors. Experiments show that, with respect to the performance near edges of digital images, our algorithm is better than nearest...
This paper describes a new robust appearance-based method for representing and recognizing human behaviours using the eigenspace technique. This method has three main advantages over the existing appearance-based methods. First, the centering of the human-body blob, in each background-subtracted video frame, together with the use of an incremental procedure for compression, have made the extraction...
A multi-view based active learning method (AMDWVE) is proposed as a means to optimally construct the training set for supervised classification of hyperspectral data, thereby reducing the effort required to acquire ground reference data. The method explores the intrinsic multi-view information embedded in hyperspectral data. By adaptively and quantitatively measuring the disagreement level of different...
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