MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular the biomedical literature suggests that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail.

Iris recognition: an emerging biometric technology

A method of maintaining digital medical records, comprising a step of receiving a medical transaction record (102), encrypted with a key in accordance with a patient-file association. Also comprising a step of accessing the encrypted medical transaction record according to a patient association with the record (111). And further comprising a step of re-encryption of the encrypted accessed medical transaction record with a key associated with an intended recipient of the medical record. The system and method according to the present invention presents a new business model for creation, maintenance, transmission, and use of medical records. The invention also allows financial burdens to be reallocated optimally and equitably, resulting in decreased overall societal cost and providing a successful business model for a database proprietor. Secure entrusted medical records are held in trust by an independent third party on behalf of the patient (113), and serve the medical community at large. Separately encrypted record elements may be aggregated as an information polymer.

Information record infrastructure, system and method

In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. As technology advances and information and intellectual properties are wanted by many unauthorized personnel. As a result many organizations have being searching ways for more secure authentication methods for the user access. In network security there is a vital emphasis on the automatic personal identification. Due to its inherent advantages biometric based verification especially iris identification is gaining a lot of attention. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.

/pdf/iris-recognition-an-emerging-biometric-technology-2gemlovuqu.pdf

This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It ends with a discussion of open research problems in the area.

/pdf/image-alignment-and-stitching-a-tutorial-57yf6bt9xq.pdf

Image Alignment and Stitching: A Tutorial

Iris recognition is achieved by iris acquisition that permits a user to self-position his or her eye (216) into an imager's (200) field of view without the need for any physical contact, spatially locating the data defining that portion of a digitized video image of the user's eye that defines solely the iris thereof without any initial spatial condition of the iris being provided, and pattern matching the spatially located data defining the iris of the user's eye with stored data defining a model iris by employing normalized spatial correlation for first comparing, at each of a plurality of spatial scales, each of distinctive spatial characteristics of the respective irises that are spatially registered with one another to quantitatively determine, at each of the plurality of spatial scales, a goodness value of match at that spatial scale, and then judging whether or not the pattern which manifests solely the iris of the user's eye matches the digital data which manifests solely the model iris in accordance with a certain combination of the quantitatively-determined goodness values of match at each of said plurality of spatial scales.

Automated, non-invasive iris recognition system and method

A system for automatically generating a mosaic from a plurality of input images. The system sequentially executes an image alignment process, an editing process, and a combining process such that, from a sequence of images, the system automatically produces a seamless mosaic for various applications. Illustrative applications for the mosaic include: (1) a mosaic based display system including an image printing system, (2) a surveillance system and (3) a mosaic based compression system. The mosaic based display system permits a system user to display, manipulate and alter a mosaic. The mosaic based compression system exploits the temporal and spatial redundancy in image sequences and efficiently compresses the image information. The compression system can be used for compressing image information for storage in a storage device or can be used for compressing image information for transmission through a band-limited transmission channel. The surveillance system combines the mosaic construction system, the compression system and the display system into a comprehensive system for remote motion detection.

System for automatically aligning images to form a mosaic image

Recently, there has been a growing interest in the use of mosaic images to represent the information contained in video sequences. The paper systematically investigates how to go beyond thinking of the mosaic simply as a visualization device, but rather as a basis for efficient representation of video sequences. We describe two different types of mosaics called the static and the dynamic mosaic that are suitable for different needs and scenarios. We discuss a series of extensions to these basic mosaics to provide representations at multiple spatial and temporal resolutions and to handle 3D scene information. We describe techniques for the basic elements of the mosaic construction process, namely alignment, integration, and residual analysis. We describe several applications of mosaic representations including video compression, enhancement, enhanced visualization, and other applications in video indexing, search, and manipulation. >

Mosaic based representations of video sequences and their applications

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endeavour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body its appearance is amenable to remote examination with the aid of a computer vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study where the system exhibits flawless performance in the evaluation of 520 iris images. >

A system for automated iris recognition

A system and method for accurately mapping between image coordinates and geo-coordinates, called geo-spatial registration. The system utilizes the imagery and terrain information contained in the geo-spatial database to precisely align geodetically calibrated reference imagery with an input image, e.g., dynamically generated video images, and thus achieve a high accuracy identification of locations within the scene. When a sensor, such as a video camera, images a scene contained in the geo-spatial database, the system recalls a reference image pertaining to the imaged scene. This reference image is aligned very accurately with the sensor's images using a parametric transformation. Thereafter, other information that is associated with the reference image can easily be overlaid upon or otherwise associated with the sensor imagery.

Stephen Charles Hsu

Papers

Automated, non-invasive iris recognition system and method

System for automatically aligning images to form a mosaic image

Mosaic based representations of video sequences and their applications

A system for automated iris recognition

Method and apparatus for performing geo-spatial registration of imagery