Abstract The large number of herbal plants and the lack public knowledge about types medicines make it difficult for people to distinguish medicinal plants. So, provide information public, an identification system that could identify recognize a plant is needed. Leaves identificiation based on Morphological method with K-Nearest Neighbor algorithm. Leafs data will be used in this study Basil Le...

For Optical Character Recognition (OCR) of bilingual or multilingual document containing text words in regional language and numerals in English, it is necessary to identify different script forms before running an individual OCR of the scripts. In this paper, an attempt is made for separation of English numerals at word level from bilingual and trilingual documents representing Kannada, Devnag...

The sa-tree is an interesting metric space indexing structure that is inspired by the Voronoi diagram. In essence, the sa-tree records a portion of the Delaunay graph of the data set, a graph whose vertices are the Voronoi cells, with edges between adjacent cells. An improvement is presented on the original search strategy for the sa-tree. This consists of details on the intuition behind the im...

In this paper, classification efficiency of the conventional K-nearest neighbor algorithm is enhanced by exploiting fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbor algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the gener...

Algorithms that use point-cloud models make heavy use of the neighborhoods of the points. These neighborhoods are used to compute the surface normals for each point, mollification, and noise removal. All of these primitive operations require the seemingly repetitive process of finding the k nearest neighbors of each point. These algorithms are primarily designed to run in main memory. However, ...

Algorithms that use point-cloud models make heavy use of the neighborhoods of the points. These neighborhoods are used to compute the surface normals for each point, mollification, and noise removal. All of these primitive operations require the seemingly repetitive process of finding the k nearest neighbors (kNNs) of each point. These algorithms are primarily designed to run in main memory. Ho...

In classification problem, the most important factor is training dataset which is effect accuracy rate of classification. However, we encounter with imbalanced data set in real-world applications. In this dataset, the number of images in some classes is rather less than the number of images in other classes. So estimation of classification is tent to majority class and minority classes will be ...

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with op...

Many studies have been carried out to find an appropriate method to classify the remote sensing data.Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Instead of pixels, pixel groups and object oriented techniques offer the suitable analysed to classify satel...