Zone-Features based Nearest Neighbor Classification of Images of Kannada Printed and Handwritten Vowel and Consonant Primitives

The characters of any languages having scripts are formed by basic units called primitives. It is necessary to practice writing the primitives and their appropriate combinations while writing different characters. In order to automate character generation, primitives201F; recognition becomes important. In this paper, we propose a zone-features based nearest neighbor classification of Kannada printed and handwritten vowel and consonant primitives. The normalized character image is divided into 49 zones, each of size 4x4 pixels. The classifier based on nearest neighbor using Euclidean distances is deployed. Experiments are performed on images of printed and handwritten primitives of Kannada vowels and consonants. We have considered 9120 images of printed and 3800 images of handwritten 38 primitives. A K-fold cross validation method is used for computation of results. We have observed average recognition accuracies are in the range [90%, 93%] and [93% to 94%] for printed and handwritten primitives respectively. The work is useful in multimedia teaching, animation; Robot based assistance in handwriting, etc

Autoregressive Modeling of Visual Evoked Potentials and Its Applications to Optic Nerve Diseases-Ischemic Optic Neuropathy and Optic Neuritis

It is important to differentiate the diagnosis of ischemic optic neuropathy (ION) and optic neuritis (ON) for prognostic and therapeutic reasons. In most cases, differentiation is accomplished by assessing the disc appearance, the presence or absence of retrobulbar pain, the age of the patient, the mode of onset and other features of clinical and laboratory evaluation. However, in certain groups of patients, diagnosis may be difficult because of overlapping clinical profiles in these two disorders. In this paper, an attempt is made to overcome clinically overlapping profiles and to evolve indices to classify and delineate clearly ION and ON groups by differential diagnosis of the visual evoked potentials (VEP) using autoregressive (AR) modeling. In the present work of AR modeling, the data sequence x(n) as the output of a linear system has been carried out using digitized VEP waveform. An appropriate optimal order p for the AR model is chosen based on the Akaike information criterion (AIC). Accordingly, AR model has eight coefficients for each data sequence. These AR model coefficients are computed using Burg’s algorithm. These AR coefficients with different combinations were plotted in the feature plane representations, for distinction between the ION and ON group of patients. It was found that, the feature plane plot of a2 verses a7 has a potential to distinguish clearly the ION and ON patients with respect to normal subjects. This novel technique using the AR feature plane representation is more efficient and thus, enables the neurologist in early therapy planning

An edge texture features based methodology for bulk paddy variety recognition

The paper presents a method for recognition of paddy varieties from their bulk grain sample edge images based on Haralick texture features extracted from grey level co-occurrence matrices. The edge images were obtained using Canny and maximum gradient edge detection methods. The average paddy variety recognition performances of the two categories of edge images were evaluated and compared. A feature set of thirteen texture features was considered and the feature set was reduced based on contribution of each feature to the paddy variety recognition accuracy. The average paddy variety recognition accuracy of 87.80% was obtained for the reduced eight texture features extracted from maximum gradient edge images. The work is useful in developing a machine vision system for agriculture produce market and developing multimedia applications in agriculture sciences

Varieties Classification into Plain, Patterned and Un-patterned from Fabric Images

The presented work gives a methodology to classify fabric images as plain, patterned and un-patterned. Discrete Wavelet Transform is applied and wavelet features are extracted. Feed Backward Selection Technique is used in the feature selection phase. Two prediction models, namely, Support Vector Machine and Artificial Neural Network are used. The overall classification rates of 81% and 86.5% are obtained for fabric types using Support Vector Machine and Artificial Neural Network classifiers. The classification rates for varieties of non-plain fabric images are found to be 84% and 88% respectively