A fuzzy logic based approach for enchanging depth perception in computer graphics

Ankara : The Department of Computer engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 78-83.Rapid progress in 3D rendering and display technologies brings the problem of better visualization of 3D content. Providing correct depth information and enabling the user to perceive the spatial relationship between the objects is one of the main concerns during the visualization of 3D content. In this thesis, we introduce a solution that can either be used for automatically enhancing the depth perception of a given scene, or as a component that suggests suitable rendering methods to application developers. In this novel solution, we propose a framework that decides on the suitable depth cues for a given 3D scene and the rendering methods which provide these cues. First, the system calculates the importance of each depth cue using a fuzzy logic based algorithm which considers the user's tasks in the application and the spatial layout of the scene. Then, a knapsack model is constructed to keep the balance between the rendering costs of the graphical methods that provide these cues and their contribution to depth perception. This cost-pro t analysis step selects the proper rendering methods for the given scene. In this work, we also present several objective and subjective experiments which show that our automated depth perception enhancement system is statistically (p < 0.05 ) better than the other method selection techniques that are tested.Çipiloğlu, ZeynepM.S

Vehicle detection using improved region convolution neural network for accident prevention in smart roads

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data, which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances.publishedVersio

Knowledge visualization: From theory to practice

Visualizations have been known as efficient tools that can help users analyze com- plex data. However, understanding the displayed data and finding underlying knowl- edge is still difficult. In this work, a new approach is proposed based on understanding the definition of knowledge. Although there are many definitions used in different ar- eas, this work focuses on representing knowledge as a part of a visualization and showing the benefit of adopting knowledge representation. Specifically, this work be- gins with understanding interaction and reasoning in visual analytics systems, then a new definition of knowledge visualization and its underlying knowledge conversion processes are proposed. The definition of knowledge is differentiated as either explicit or tacit knowledge. Instead of directly representing data, the value of the explicit knowledge associated with the data is determined based on a cost/benefit analysis. In accordance to its importance, the knowledge is displayed to help the user under- stand the complex data through visual analytical reasoning and discovery

A Survey on Biometrics and Cancelable Biometrics Systems

Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results

Performance comparison of intrusion detection systems and application of machine learning to Snort system

This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort

Vehicle detection using improved region convolution neural network for accident prevention in smart roads

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data, which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances