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This paper presents a novel multi-level approach for bleeding detection in Wireless Capsule Endoscopy (WCE) images. In the low-level processing, each cell of K×K pixels is characterized by an adaptive color histogram which optimizes the information representation for WCE images. A Neural Network (NN) cell-classifier is trained to classify cells in an image as bleeding or non-bleeding patches. In the...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
This paper presents a novel approach to single-frame pedestrian classification and orientation estimation. Unlike previous work which addressed classification and orientation separately with different models, our method involves a probabilistic framework to approach both in a unified fashion. We address both problems in terms of a set of view-related models which couple discriminative expert classifiers...
The classification of affective semantics in images is a very challenging research direction that gains more and more attention in the research community. However, as an emerging topic, contributions remain relatively rare, and a lot of issues need to be addressed particularly concerning the three following fundamentals problems: emotion representation, image features used to represent emotions and...
The k-nearest neighbor (K-NN) framework was successfully used for tasks of computer vision. In image categorization, k-NN is an important and significant rule. However, two major problems usually affect this rule: the NN classifier vote and the metric employed to compute the distance between neighbors. This paper deals with both. First, a boosting k-NN algorithm learns the coefficients of weak classifiers,...
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently. Existing methods for supervised dimensionality reduction often presume that the data is densely sampled...
This paper presents a method to extract features by PCA (Principal component analysis) from a series of woods surfaces' images. The method introduces a principal component subspace and can reserve original information while extraction mainly information. The emulated results show that we can fuse the image series and extract features from the four images of a same surface by using this method. After...
This paper proposes a new learning method, which integrates feature selection with classifier construction for human detection via solving three optimization models. Firstly, the method trains a series of weak-classifiers by the proposed L1-norm Minimization Learning (LML) and min-max penalty function models. Secondly, the proposed method selects the weak-classifiers by using the integer optimization...
Image alignment in the presence of non-rigid distortions is a challenging task. Typically, this involves estimating the parameters of a dense deformation field that warps a distorted image back to its undistorted template. Generative approaches based on parameter optimization such as Lucas-Kanade can get trapped within local minima. On the other hand, discriminative approaches like Nearest-Neighbor...
Automatic inspection on line plays an important role in industrial quality management nowadays. This paper proposes a new computer vision system for automatic steel surface inspection. The system analyzes the images sequentially acquired from steel bar to detect different kinds of defects on the steel surface. Several image processing strategies are used to detect and outline the defects. The detected...
This paper presents a method for pavement crack detection, classification and evaluation using the Radon transform. The detection part of the algorithm is built upon the wavelet transform and the evaluation part is considered in the Radon transform domain. Since cracks have specific linear features in the space domain, the Radon transform can effectively be used on a binary image to classify and evaluate...
This paper presents the comparative study of face recognition using discrete orthogonal moment namely Krawtchouk moments (KMs) and Tchebichef moments (TMs). Both these moments do not require numerical approximation and coordinate space normalization. The complex computation of radial polynomial as order becomes larger is not an issue and this makes KMs and TMs superior compared to continuous orthogonal...
In Malaysia, the screening coverage for cervical cancer is poor, which was at 2% in 1992, 3.5% in 1995, and at 6.2% in 1996, due to the shortage in pathologist workforce being one of the major cause. Study has been done before to overcome this by developing a diagnosis system based on neural networks, so that diagnosis can be done by an automated system with pathologist-like knowledge. Cell's features...
Video image segmentation is essential for image analysis and the target recognition. In this study, a Bayesian theory and neural networks based image processing method was applied to video image segmentation. Firstly, a neural network with an incremental input node was designed for approximating to the posterior probability, which avoided the difficulty of estimation of class-conditional probability...
With the development of DNA microarrys technology, it is very important to classify the different tumor types correctly in cancer diagnosis and drug discovery. In this paper, we discuss how to use the nonnegative matrix factorization (NMF) to extract features and illustrate how to adopt classification model to improve the classification accuracy. For the DNA microarrys, the gene expression data is...
This paper proposes the fractal features classification for liver biopsy images using probabilistic neural network (PNN). Fractal set has the properties of self-similarity and self-affinity. It can be used to estimate the fractal dimension (FD) from two-dimensional (2D) images, including the normal and cancerous liver tissue images. PNN is based on the probability density function (PDF) to implement...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques and allows computer to learn from past examples and detect patterns from large data sets, which is particularly well-suited to assist medical practitioners in diagnosis of disease based on a variety of test results. Therefore, in this research, we deemed further...
Tumour classification and quantification in positron emission tomography (PET) imaging at early stage of illness are important for radiotherapy planning, tumour diagnosis, and fast recovery. Analysing large medical volumes using traditional techniques requires a decent amount of time, and in some approaches poor accuracy is achieved. Artificial intelligence (AI) technologies can provide better accuracy...
This paper address the dust aerosol detection problem based on a probabilistic multispectral image analysis. Two classifiers are designed. First the Maximum Likelihood classifier is adapted to mode different types of atmospheric components. The second is a Probabilistic Neural Network (PNN) model. The data sets are MODIS multispectral bands from NASA Terra satellite. Findings indicate that the PNN...
A human face does not only identify an individual but also communicates useful information about a person's emotional state. No wonder automatic face expression recognition has become an area of immense interest within the computer science, psychology, medicine and human-computer interaction research communities. Various feature extraction techniques based on statistical to geometrical data have been...
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