Segmentation of Colour Images by Modified Mountain Clustering

Segmentation of colour images is an important issue in various machine vision and image processing applications. Though clustering techniques have been in vogue for many years, these have not been very effective because of problems like selection of the number of clusters. This problem has been tackled by having a validity measure coupled with the new clustering technique. This method treats each point in the dataset, which is the map of all possible colour combinations in the given image, as a potential cluster centre and estimates its potential wrt other data elements. First, the point with the maximum value of potential is considered to be a cluster centre and then its effect is removed from other points of the dataset. This procedure is repeated to determine different cluster centres. At the same time, the compactness and the minimum separation is computed amongst all the cluster centres, and also the validity function as the ratio of these quantities. The validity function can be used to choose the number of clusters. This technique has been compared to the fuzzy C-means technique and the results have been shown for a sample colour image

Offline Signature Verification using CNN

This paper presents the convolutional neural network for feature extraction and Support vector machine for theverification of offline signatures. The cropped signatures are used to train CNN forr extracting features. The Extracted features are classified into two classes genuine or forgery using SVM. The the new signature is tested on GPDS signature data base using the trained SVM. The dabase contains signatures of 960 users and for each user there are 24 genuine signatures and 30 forgeries. The CNN network is trained with 300 users and signatures of 400 users are used for feature learning. These 400x20x25 signatures are used 90%to train and 10% to test SVM classifier

Color image encryption and decryption using Hill Cipher associated with Arnold transform

Image security over open network transmission is a big concern nowadays. This paper proposes another methodology for color image encoding and decoding using two stage Hill Cipher method which is connected with Arnold Transformation. The forgoing created a strategy for encryption and decryption of color image information and touched on just the premise of keys. In this plan, keys and the agreement of Hill Cipher (HC) are basic. Moreover, keys multiplication (pre or post) over an RGB image information framework is inevitable to know to effectively decrypt the first image information. We have given a machine simulation with a standard example and the result is given to support the stalwartness of the plan. This paper gives a detailed comparison between prior proposed methods and this methodology. The system has potential utilization in computerized RGB image transforming and security of image information

Biometrics in Cyber Security

Computers play an important role in our daily lives and its usage has grown manifolds today. With ever increasing demand of security regulations all over the world and large number of services provided using the internet in day to day life, the assurance of security associated with such services has become a crucial issue. Biometrics is a key to the future of data/cyber security. This paper presents a biometric recognition system which can be embedded in any system involving access control, e-commerce, online banking, computer login etc. to enhance the security. Fingerprint is an old and mature technology which has been used in this work as biometric trait. In this paper a fingerprint recognition system based on no minutiae features: Fuzzy features and Invariant moment features has been developed. Fingerprint images from FVC2002 are used for experimentation. The images are enhanced for improving the quality and a region of interest (ROI) is cropped around the core point. Two sets of features are extracted from ROI and support vector machine (SVM) is used for verification. An accuracy of 95 per cent is achieved with the invariant moment features using RBF kernel in SVM

FABRIC IMAGE DEFECT DETECTION BY USING GLCM AND ROSETTA

ABSTRACT Automated visual inspections of industrial goods for Quality control plays an ever-increasing role in production process as the global market pressures put higher and higher demand on quality at lower cost. In most cases, the quality inspection through visual inspection is still carried out by humans. However, the reliability of manual inspection is limited due to fatigue and inattentiveness. The author did the literature survey on textile industry and the most highly trained inspectors can only detect about 70% of the fabric defects

Innovative Criteria for Input Selection

In this paper an attempt is made to derive new criteria for input selection of dynamic systems using the fuzzy curve approach. The Approximate Fuzzy Data Model (AFDM), the output of which is the fuzzy curve, is shown to be a special case of the Generalized Fuzzy Model (GFM). Moreover, AFDM is proved to be an unconditional expectation of the output thus linking fuzzy rules with probability. The validity of the criteria for input selection has been studied on GFM by means of significance of inputs, which is determined from the ratio of change in the output of AFDM to the range of the actual output. The complexity of the criteria has been proved to be of the order of O(n), which is a significant achievement in comparison to the complexity of the existing criteria

Text-independent speaker recognition for Ambient Intelligence applications by using Information Set Features

Biometric systems are enabling technologies for a wide set of applications in Ambient Intelligence (AmI) environments. In this context, speaker recognition techniques are of paramount importance due to their high user acceptance and low required cooperation. Typical applications of biometric recognition in AmI environments are identification techniques designed to recognize individuals in small datasets. Biometric recognition methods are frequently deployed on embedded hardware and therefore need to be optimized in terms of computational time as well as used memory. This paper presents a text-independent speaker recognition method particularly suitable for identification in AmI environments. The proposed method first computes the Mel Frequency Cepstral Coefficients (MFCC) and then creates Information Set Features (ISF) by applying a fuzzy logic approach. Finally, it estimates the user's identity by using a hierarchical classification technique based on computational intelligence. We evaluated the performance of the speaker recognition method using signals belonging to the NIST-2003 switchboard speaker database. The achieved results showed that the proposed method reduced the size of the template with respect to traditional approaches based on Gaussian Mixture Models (GMM) and achieved better identification accuracy