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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...
In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Image fusion deals with creating an image in which all the objects are in focus. Thus it plays an important role to perform other tasks of image processing...
A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement,...
In this study, the theoretical and experimental analysis of feature space concatenation operation is introduced. This operation is widely used for data fusion and ensemble learning. Following the analysis, a new performance measure which is called Vectorization Measure (VM) is introduced. VM enables the estimation of the separability capacity of the fusion space by analyzing the sample margin distributions...
In this study, we analyze head gestures and facial expressions in face video streams. Facial landmark trajectories, which are the tracked coordinates of the landmarks in x and y directions, are extracted via an automatic and robust facial landmark tracking algorithm. Both raw features and features intuitively selected to reflect mimics are used. Examples of the latter category are mutual distances,...
Color and color difference are important information for a lesion in dermatological diagnosis. This paper presents various supervised ANN models for plaque classification using RGB indices. Images are taken from selected skin at the dermatological clinic which the images are captured using digital camera with controlled environment. The analysis of dermatological digital images is performed by measurements...
Liver with cirrhosis emerges when the cells of liver begin to die and the tissues become a functional knot from these. In the diagnosis of fibrosis, the needle biopsy is a golden standard. Although this technique is a good technique in reaching accurate diagnosis, its being an invasive method arises disadvantage. The developments in medical image processing and artificial intelligence techniques have...
In this paper a new knitted garment defect detection and classification model based on 2D Gabor wavelet transform and Elman neural network is introduced. A new modified Elman network is proposed to classify the type of fabric defects which have proportional (P), integral (I), derivative (D) properties. The proposed inspecting model in this study is more feasible and applicable in fabric defect detection...
Land-use change is an important area of global change research, rapid and accurate access to land-use temporal and spatial variation information is a key technology to study land-use change. In this paper proposed a method which utilizes the improved model of fuzzy ARTMAP network - simplified fuzzy ARTMAP neural network for remote sensing land-use classification, and Take the TM remote sensing image...
Support vector machine (SVM) is discussed to use for recognizing cucumber leaf diseases in this paper. Considering that it is a small number of samples, a new experimental program has been proposed which takes each spot of leaves as a sample instead of taking each leaf as a sample. In the experiments Radial Basis Function (RBF), polynomial and Sigmoid kernel function were also used to carry out comparative...
One of the main problems in wood species recognition systems is the lack of discriminative features of the texture images. In order to overcome this, we use Gabor filter in the pre-processing stage of the wood texture image to multiply the number of features for a single image, thus providing more information for feature extractor to capture. The textural wood features are extracted using two feature...
This paper deals with the problem of automatic target classification or Through-the-Wall radar imaging. The proposed scheme considers stationary objects in enclosed structures and works on the SAR image rather than the raw data. It comprises segmentation, feature extraction based on superquadrics, and classification. We present a recursive splitting tree to obtain optimum parameters for feature extraction...
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