A new species of Pseudostomella (Gastrotricha: Macrodasyida: Thaumastodermatidae) from a sandy beach of Kerala, India

During an interstitial faunal survey along the south-west coast of Kerala, India, gastrotrich fauna were found in abundance. Together with species of the genera Xenotrichula, Halichaetonotus and Tetranchyroderma, were present several undescribed thaumastodermatid gastrotrichs belonging to the buccal palp bearing genus Pseudostomella. Adults of the new species are characterized by the following traits: total body length of about 300 μm; cuticular armature made up of medium sized pentancres covering the entire dorsolateral surface; pre-buccal, grasping palps bearing five, large papillae dorsally and 4-6 smaller papillae ventrally; adhesive apparatus made up of six anterior, 22-24 ventrolateral, two dorsolateral and six posterior adhesive tubes; caudal organ pear-shaped; frontal organ spherical. Pseudostomella cheraensis sp. nov. is the fourth taxon of the genus known from India; however, all the previous species reported hitherto from India have tetrancres instead of pentancres.

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.