Collaborative analysis of multi-gigapixel imaging data using Cytomine

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications

A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Biometric recognition is gaining attention as most of the organization is seeking for a more secure verification method for user access and other security application. There are a lot of biometric systems that exist which are iris, hand geometry and fingerprint recognition. In biometric system, iris recognition is marked as one of the most reliable and accurate biometric in term of identification. However, the performance of iris recognition is still doubted whether the iris recognition can generate higher accuracy when involving twin iris data. So, specific research by using twin data only needs to be done to measure the performance of recognition. Besides that, a comparative study is carried out using two template matching technique which are Hamming Distance and Euclidean Distance to measure the dissimilarity between the two iris template. From the comparison of the technique, better template matching technique also can be determined. The experimental result showed that iris recognition can distinguish twin as it can distinguish two different, unrelated people as the result obtained showed the good separation between intra and interclass and both techniques managed to obtain high accuracy. From the comparison of template matching technique, Hamming Distance is chosen as better technique with low False Rejection Rate, low False Acceptance Rate and high Total Success Rate with the value of 2.5%, 8.75% and 96.48% respectively

Recognition of Non Circular Iris Pattern of the Goat by Structural, Statistical and Fourier Descriptors

AbstractThe present paper has described a comparative study to find the iris pattern of the goat which has nearly rectangular or square type appearance. For detecting the structural descriptors, the deviation of the iris pattern shape and size from a standard circular (annular) shaped have been thoroughly studied. Statistical feature extraction has mainly dealt with the various types of moments e.g. – mean variance skewness and kurtosis1, 2. Fourier descriptors have been extracted by 2D Fourier Transformation of the entire data set comprising patterns. It has been found that Fourier Descriptors are not directly insensitive to possible geometrical changes of the iris location like translation, rotation and scale change occurring due to eye ball movement and blinking of the eye lids. The result shows that the structural descriptors based pattern recognition rate produce a recognition rate of 97.85% with 4.5% of false acceptance rate and 2.2% false rejection rate. The images during the study were acquired from real life with 16 megapixel camera resolution