Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Preprocessing reference sensor pattern noise via spectrum equalization

Although sensor pattern noise (SPN) has been proven to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared amongst cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this work, we propose a novel preprocessing approach for attenuating the influence of the nonunique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with 6 SPN extraction or enhancement methods, our proposed Spectrum Equalization Algorithm (SEA) is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. Experimental results indicate that the proposed procedure outperforms, or at least performs comparably to, the existing methods in terms of the overall ROC curve and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks

Learning Multimodal Structures in Computer Vision

A phenomenon or event can be received from various kinds of detectors or under different conditions. Each such acquisition framework is a modality of the phenomenon. Due to the relation between the modalities of multimodal phenomena, a single modality cannot fully describe the event of interest. Since several modalities report on the same event introduces new challenges comparing to the case of exploiting each modality separately. We are interested in designing new algorithmic tools to apply sensor fusion techniques in the particular signal representation of sparse coding which is a favorite methodology in signal processing, machine learning and statistics to represent data. This coding scheme is based on a machine learning technique and has been demonstrated to be capable of representing many modalities like natural images. We will consider situations where we are not only interested in support of the model to be sparse, but also to reflect a-priorily known knowledge about the application in hand. Our goal is to extract a discriminative representation of the multimodal data that leads to easily finding its essential characteristics in the subsequent analysis step, e.g., regression and classification. To be more precise, sparse coding is about representing signals as linear combinations of a small number of bases from a dictionary. The idea is to learn a dictionary that encodes intrinsic properties of the multimodal data in a decomposition coefficient vector that is favorable towards the maximal discriminatory power. We carefully design a multimodal representation framework to learn discriminative feature representations by fully exploiting, the modality-shared which is the information shared by various modalities, and modality-specific which is the information content of each modality individually. Plus, it automatically learns the weights for various feature components in a data-driven scheme. In other words, the physical interpretation of our learning framework is to fully exploit the correlated characteristics of the available modalities, while at the same time leverage the modality-specific character of each modality and change their corresponding weights for different parts of the feature in recognition

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Efficient Anonymous Biometric Matching in Privacy-Aware Environments

Video surveillance is an important tool used in security and environmental monitoring, however, the widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. To identify these individuals for protection, the most reliable approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this dissertation, an Anonymous Biometric Access Control (ABAC) procedure is proposed based on biometric signals for privacy-aware video surveillance. The ABAC procedure uses Secure Multi-party Computational (SMC) based protocols to verify membership of an incoming individual without knowing his/her true identity. To make SMC-based protocols scalable to large biometric databases, I introduce the k-Anonymous Quantization (kAQ) framework to provide an effective and secure tradeoff of privacy and complexity. kAQ limits systems knowledge of the incoming individual to k maximally dissimilar candidates in the database, where k is a design parameter that controls the amount of complexity-privacy tradeoff. The relationship between biometric similarity and privacy is experimentally validated using a twin iris database. The effectiveness of the entire system is demonstrated based on a public iris biometric database. To provide the protected subjects with full access to their privacy information in video surveillance system, I develop a novel privacy information management system that allows subjects to access their information via the same biometric signals used for ABAC. The system is composed of two encrypted-domain protocols: the privacy information encryption protocol encrypts the original video records using the iris pattern acquired during ABAC procedure; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of my framework

Android Applications for Automation Purposes

In recent years, the number of network-enabled smartphones everywhere has been increasing fast. With the rapid expansion of the Internet, people have been trying to reduce manual intervention as much as possible. A variety of sensors are embedded in today’s smartphones which make interfacing with the outside world, easy. The majority of the smartphone users have Android as the operating system. So in the world of smartphone Android has the largest platform as compared to other operating systems. So in this project work Android is used to automate some of the simple day to day manual activities. The thesis represents the design and development of simple android applications which are used to automate simple tasks. All the applications are compatible with Android 2.1 onwards. The designs of the proposed applications are on top of a Web interface which uses RESTful API as the communication protocol between client applications and web service. The applications take much advantage of sensors and techniques pre-installed in Android smartphones. The proposed applications follow optimizations according to the best practices recommended by Google, to increase user experience and reduce power consumption. The first one is PVSys, an Android application which gives the details of equipment required for solar pump installation at user’s backyard or a full solar panel installation at user’s house. The second one is BizCard, which automates the task of storing user’s business cards in digital form and retrieving the contacts when required. The third application, Auto Attendance Manager, automates the task of taking attendance for the teachers and lecturers without the aid of any external device. The Auto Attendance Manager can be integrated easily with the present application of the Institute.Automation of simple things with Android becomes easy. Moreover, a layman can work on Android interface since it is easy to implement and design the layouts. In today’s world when every device is trying to communicate its information to the internet, Android interface with Bluetooth or Wi-Fi can be used for the sam