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We propose and verify a method for color-based cluster segmentation of the various tissues from ectocervix. That method uses a simplified compartment-like analysis, aiming for a Gaussian Mixture Model (GMM)-tailored segmentation. The tissues of interest are the cervical canal (CC), the columnar epithelium (CE), the squamous epithelium (SE) and the transformation zone (TZ) the latter known as area...
We provide a non-iterative channel impulse response (CIR) estimation algorithm for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. The non-iterative channel estimate is an approximation to the Best Linear Unbiased Estimate (BLUE) of the CIR, achieving almost similar performance, with much lower complexity. We first...
De-interlacing is one of the key technologies in modern displays and multimedia personal computers. Various methods have been proposed including motion compensated (MC) methods and non motion compensated methods. Hybrid methods that combine different de-interlacing techniques are widely used to take advantages from individual algorithms. The combination is normally based on the quality criterion of...
An efficient update algorithm is presented which tracks the left and right singular vectors and singular values of a transfer matrix, using input and output vectors and without explicitly computing the matrix itself. Such an algorithm has many potential applications in multiple-input multiple-output wireless communication systems in which the channel parameters change slowly with time. Examples of...
In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative models through Bayes's rule. Sometimes, however, it is convenient to express the models as probabilistic models, since they are generative in nature and can handle the representation...
In this paper we propose an Iterative Re-Weighted Least Square procedure in order to solve the Support Vector Machines for regression and function estimation. Furthermore, we include a new algorithm to train Support Vector Machines, covering both the proposed approach instead of the quadratic programming part and the most advanced methods to deal with large training data sets. Finally, the performance...
Classifying instances in evolving data stream is a challenging task because of its properties, e.g., infinite length, concept drift, and concept evolution. Most of the currently available approaches to classify stream data instances divide the stream data into fixed size chunks to fit the data in memory and process the fixed size chunk one after another. However, this may lead to failure of capturing...
We study basic-level categories for describing visual concepts, and empirically observe context-dependant basic level names across thousands of concepts. We propose methods for predicting basic-level names using a series of classification and ranking tasks, producing the first large scale catalogue of basic-level names for hundreds of thousands of images depicting thousands of visual concepts. We...
Traditional correlation filters for object detection are efficient and provide good localization, but require scalar valued image features and only perform well on objects with consistent appearance. Some newer filters work with feature spaces that introduce some invariance to small deformations, but more difficult detection problems require more than one filter. We introduce a method for jointly...
Achieving sub-pixel accuracy with face alignment algorithms is a difficult task given the diversity of appearance in real world facial profiles. To capture variations in perspective, occlusion, and illumination with adequate precision, current face alignment approaches rely on detecting facial landmarks and iteratively adjusting deformable models that encode prior knowledge of facial structure. However,...
The entropy measurement function is a central element of decision forest induction. The Shannon entropy and other generalized entropies such as the R?nyi and Tsallis entropy are designed to fulfill the Khinchin-Shannon axioms. Whereas these axioms are appropriate for physical systems, they do not necessarily model well the artificial system of decision forest induction. In this paper, we show that...
Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than...
In this paper we extend the agglomerative hierarchical kernel spectral clustering (AH-KSC [1]) technique from networks to datasets and images. The kernel spectral clustering (KSC) technique builds a clustering model in a primal-dual optimization framework. The dual solution leads to an eigen-decomposition. The clustering model consists of kernel evaluations, projections onto the eigenvectors and a...
This paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid...
A new type of air-driven multi-fingered dexterous hand, named ZJUT Hand, is proposed. ZJUT Hand has good passive flexibility and can make up for the deficiency of existing dexterous hands in some ways. A new method for grasping model identification based ANFIS is proposed. Simulation experimental results show that the method can easily establish the model of the target objects, and has very good recognition...
Cross-validation is a commonly used method for evaluating the effectiveness of Support Vector Machines (SVMs). However, existing SVM cross-validation algorithms are not scalable to large datasets because they have to (i) hold the whole dataset in memory and/or (ii) perform a very large number of kernel value computation. In this paper, we propose a scheme to dramatically improve the scalability and...
Classification of data points in a data stream is a fundamentally different set of challenges than data mining on static data. While streaming data is often placed into the context of "Big Data" (or more specifically "Fast Data") wherein one-pass algorithms are used, true data streams offer additional hurdles due to their dynamic, evolving, and non-stationary nature. During the...
The proposed work describes a distribution-based approach for recognizing people in images. The methodology involves pattern classification using first and second order statistics in Principal Component Analysis (PCA) - based clustering framework. Unknown distributions of pedestrian and non-pedestrian patterns are approximated by learning the first and second order statistics of the sample images...
Building accurate classifiers is difficult when using data that is skewed or imbalanced which is typical of real world data sets. Two popular approaches that have been applied for improving classification accuracy and statistical comparisons of imbalanced data sets are: synthetic minority over-sampling technique (SMOTE) and propensity score matching (PSM). A novel sampling approach is introduced referred...
We propose an alternative parameterization of Logistic Regression (LR) for the categorical data, multi-class setting. LR optimizes the conditional log-likelihood over the training data and is based on an iterative optimization procedure to tune this objective function. The optimization procedure employed may be sensitive to scale and hence an effective pre-conditioning method is recommended. Many...
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