Face Recognition using local Patterns

Deriving an effective face representation is very essential task for automatic face recognition application. In this paper we used a feature descriptor called the Local Directional Number Pattern (LDN), which allows individual’s face recognition under different lightning’s, pose and expressions. Face recognition deals with different challenging problems in the field of image analysis and human computer interface. To deal with attention in our proposed work we use local patterns, a local directional number pattern (LDN) method, a six bit compact code for face recognition and understanding. By using LDN method we encode the directional information of the face images by convolving the face image with the compass mask. This compass mask extracts the edge response values in eight directions in the neighborhood. For each pixel we get the maximum and the minimum directional values which generate a LDN code i.e. generating an LDN image. Later LDN image is divided into number of blocks for each block histogram is computed and finally adds these histogram from each block to form the feature vector which acts as face descriptor to represent the face images. We perform different experiments under various illumination, pose and expression conditions

An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP

A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the bccr-segset dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection. © 2020 The Author(s) 2020