Y. H. Sharath Kumar

Learn More
This study refers to the prediction of liquefaction potential of alluvial soil by artificial neural network models. To meet the objective 160 data sets from field and laboratory tests were collected for the development of ANN models. Initially these data sets were used to determine liquefaction parameters like cyclic resistance ratio and cyclic stress ratio(More)
In this paper a novel approach for identification of whorl part of flowers useful for flower classification is presented. The problem is challenging because of the sheer variety of flower classes, intra-class variability, variation within a particular flower, and variability of imaging conditions like lighting, pose, foreshortening etc. A flower image is(More)
In this work, we propose a method for the classification of animal in images. Initially, a graph cut based method is used to perform segmentation in order to eliminate the background from the given image. The segmented animal images are partitioned in to number of blocks and then the color texture moments are extracted from different blocks. Probabilistic(More)
In this work, we have developed a supervised and unsupervised based classification system to classify the animals. Initially, the animal images are segmented using maximal region merging segmentation algorithm. The Gabor features are extracted from segmented images. Further, the extracted features are reduced based on supervised and unsupervised methods. In(More)
In this work, we propose a Triangle based approach to classify flower images. Initially, flowers are segmented using whorl based region merging segmentation. Skeleton of a flower is obtained from the segmented flower using a skeleton pruning method. The Delaunay triangulation is obtained from the endpoints and junction points detected on the skeleton. The(More)