No abstract

A scalable object location service can enable users to search for various objects in an environment where many small, networked devices are attached to objects. We investigate two hierarchical, self-configuring or unattended approaches for an eficient object location service. Each approach has its advantages and disadvantages based on the anticipated load. The first approach, SCOUT-AGG, is based on aggregation of object names. The second approach, SCOUT-MAE is based on indirection, where information about an object is stored at the locator sensor for the object. The relative efJiciency of SCOUT-AGG and SCOUT-MAP can be characterized by the query to mobilit>l update rate of the system. SCOUT-AGG performs better for low query to update rate but its performance deteriorates in general relative to SCOUT-MAP as the query to update rate increases. The rate of performance deterioration depends on query specijicity (i.e., queries for a specific object or for any object of a particular t>lpe). SCOUT-MAP generally exhibits better load balancing than SCOUT-AGG for various scenarios. We support the above results through simple analytical modeling and siniulation.

In OCR applications, the feature extraction methods used to recognize document images play an important role. The feature extraction methods may be statistical, structural or transforms and series expansion. The structural features are very difficult to extract particularly in handwritten applications. The structural behavior of the strokes existing in the handwritten expressions can be estimated through statistical methods too. In this paper, a feature extraction method is proposed that measures the distribution of black and white pixels representing various strokes in a character image by computing the weights on all the four corners on a pixel due to its neighboring black pixels. The feature is named as Neighborhood Pixels Weights (NPW). Its recognition performance is compared with some feature extraction methods, which have been generally used as secondary feature extraction methods for the recognition of many scripts in literature, on noisy and non-noisy handwritten character images. The experiments have been conducted using 17000 Devanagari handwritten character images. The experiments have been made using two classifiers i.e. Probabilistic Neural Network and k-Nearest Neighbor Classifier. NPW feature is better as compared to other features, studied here, in noisy and noiseless situation.

No abstract

Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e.  late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.