10,432 research outputs found
Face Recognition using Fuzzy Neural Network
Face recognition is a biometric tool for authentication and verification, has great emphasis in both research and practical applications. Increased requirement on security, fully automated biometrics on personal identification and verification has received extensive attention over the past few years. In this paper we propose a novel face recognition using Fuzzy Neural network, which is used to extract features from face images by dividing the images into two phase one is of training phase by neural network second is extracting phase done by fuzzy inference system. At first the Complex Wavelet Transform is a tool applied here that uses a dual tree of wavelet filters to find the real and imaginary parts of complex wavelet coefficients. The DT-CWT is, however, less redundant and computationally efficient. Dual Tree methods are based on image at different resolution. Here the DT-CWT is used to convert the entire image into 2-D form and also here Principal Component Analysis which is a linear dimensionality reduction technique is used, that attempt to represent data in lower dimensions, i.e., used to perform the face recognition which means simply it reduces the 2-D form to 1-D form. Finally we have to extract face by comparing features using fuzzy neural networks. At present many methods for image recognition are available but most of them include feature to any type of images. The proposal is divided into two phases: the training phase and the extraction or processing related to type of image. In this paper these two parts of the network one is neural network for training, second is fuzzy inference system which helps us improve the performance result in face recognition. Fuzzy logic has proved to be a tool that can improve the performance of the existing system
Wavelet based segmentation of hyperspectral colon tissue imagery
Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data
Hubungan gaya pembelajaran dengan pencapaian akademik pelajar aliran vokasional
Analisis keputusan Sijil Pelajaran Malaysia (SPM) 2011 menunjukkan penurunan
pencapaian bagi Sekolah Menengah Vokasional. Oleh itu, kajian ini dilaksanakan
bertujuan untuk mengkaji hubungan di antara gaya pembelajaran dengan pencapaian
akademik pelajar. Kajian ini juga ingin mengenalpasti gaya pembelajaran paling
dominan yang diamalkan oleh pelajar serta melihat perbezaan gaya pembelajaran
dengan jantina pelajar. Seramai 131 orang Pelajar Tingkatan Empat Kursus
Vokasional Di Sekolah Menengah Vokasional Segamat di Johor telah terlibat dalam
kajian ini. Soal selidik Index of Learning Style (ILS) yang dibangunkan oleh Felder
dan Silverman (1991) yang mengandungi 44 soalan telah digunakan untukh
menjalankan kajian ini. Gaya pembelajaran pelajar dapat dilihat melalui empat
dimensi gaya pembelajaran yang terdiri dari dua sub-skala yang bertentangan iaitu
dimensi pelajar Aktif dan Reflektif, dimensi pelajar Konkrit dan Intuitif, dimensi
pelajar Verbal dan Visual, serta dimensi pelajar Tersusun dan Global. Data yang
diperolehi dianalisis dengan menggunakan perisian Statistical Package for Social
Science for WINDOW release 20.0 (SPSS.20.0). Ujian Korelasi Pearson digunakan
untuk menganalisis data dalam mengkaji hubungan gaya pembelajaran dengan
pencapaian akademik pelajar. Nilai pekali p yang diperolehi di antara gaya
pembelajaran dengan pencapaian pelajar adalah (p=0.1 hingga 0.4). Ini menunjukkan
tidak terdapat hubungan yang signifikan di antara dua pembolehubah tersebut. Kajian
ini juga mendapati bahawa gaya pembelajaran yang menjadi amalan pelajar ialah
gaya pembelajaran Tersusun. Hasil kajian juga mendapati bahawa tidak terdapat
perbezaan yang signifikan di antara gaya pembelajaran dengan jantina pelajar
Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks
The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using
infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms
Iris Recognition Using Scattering Transform and Textural Features
Iris recognition has drawn a lot of attention since the mid-twentieth
century. Among all biometric features, iris is known to possess a rich set of
features. Different features have been used to perform iris recognition in the
past. In this paper, two powerful sets of features are introduced to be used
for iris recognition: scattering transform-based features and textural
features. PCA is also applied on the extracted features to reduce the
dimensionality of the feature vector while preserving most of the information
of its initial value. Minimum distance classifier is used to perform template
matching for each new test sample. The proposed scheme is tested on a
well-known iris database, and showed promising results with the best accuracy
rate of 99.2%
Hyperspectral colon tissue cell classification
A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
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