14923326
Co-histogram and Image Degradation Evaluation
The tool for image degradation evaluation addressed in this paper is called co-histogram, which is a statistic graph generated by counting the corresponding pixel pairs of two images. The graph is a two-dimensional joint probability distribution of the two images. A co-histogram shows how the pixels are distributed among combinations of two image pixel values. By means of co-histogram, we can have a visual understanding of PSNR, and the symmetry of a co-histogram is also significant for objective evaluation of image degradation. Our experiments with image degradation models of image compression, convolution blurring and geometric distortion perform the importance of the co-histogram.
34951978
A Top-Down Fingerprint Image Enhancement Method Based on Fourier Analysis
We present a top-down fingerprint image enhancement method which is based on Fourier analysis Our method takes advantage of two filtering methods in Fourier domain proposed previously and employs a top-down iteration filtering technique, which enables the enhancement procedure not to rely severely on an accurate estimation of ridge frequency and orientation and also enables us to make use of more than the local information in local fingerprint filtering Consequently, our method is robust to handle low quality fingerprint images Results of enhancement are presented for some representative fingerprint images.
30275744
Robust Active Shape Model Construction and Fitting for Facial Feature Localization
Active Shape Model (ASM) has proved to be a powerful tool for interpreting face images. However, it may fail in the presence of non-Gaussian noise, or outliers. In this paper, we present a framework for both automatic model construction and efficient model fitting with outliers. In model construction, the training face samples are automatically labeled by local image search using Gabor wavelet features. Then robust principle component analysis (RPCA) is applied to capture the statistics of shape variations. In model fitting, an error function is introduced to deal with the outlier problem, which provides a connection to robust M-estimation. Gauss-Newton algorithm is adopted to efficiently optimize the robust energy function. Extensive experiments demonstrate the efficiency and robustness of our approach over previous methods.
83568
Fingerprint Reference Point Detection Based on Local Axial Symmetry
Reference point detection is an important process for fingerprint analysis. In this paper, we propose a novel feature which is named local axial symmetry (LAS) and present an algorithm to calculate reference point of a fingerprint based on this kind of feature. Experimental results demonstrate its feasibility, validity and the ability to detect reference points of all classes of fingerprints including arch-type fingerprints which is difficult to locate a stable reference point with other methods
8628384
Fingerprint Indexing Based on LAS Registration
Fingerprint indexing is an efficient technique that greatly improves the performance of fingerprint based person authentication systems by reducing the number of comparison. In this paper, we propose an indexing method based on fingerprint registration with a novel feature called local axial symmetry (LAS). The location and direction estimation of reference point are achieved in a straightforward way after the LAS field is achieved. Then the registered orientation field is utilized as a feature vector to perform the following indexing. A new scheme of the experiment is introduced and satisfactory experimental results are achieved on FVC2000 DB2 that the average search space is only 2.34% of all fingers in the condition of equal-sized training set and testing set.
10875476
Affinity relation discovery in image database clustering and content-based retrieval
In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
9616437
Image database retrieval utilizing affinity relationships
Recent research effort in Content-Based Image Retrieval (CBIR) focuses on bridging the gap between low-level features and high-level semantic contents of images as this gap has become the bottleneck of CBIR. In this paper, an effective image database retrieval framework using a new mechanism called the Markov Model Mediator (MMM) is presented to meet this demand by taking into consideration not only the low-level image features, but also the high-level concepts learned from the history of user's access pattern and access frequencies on the images in the database. Also, the proposed framework is efficient in two aspects: 1) Overhead for real-time training is avoided in the image retrieval process because the high-level concepts of images are captured in the off-line training process. 2) Before the exact similarity matching process, Principal Component Analysis (PCA) is applied to reduce the image search space. A training subsystem for this framework is implemented and integrated into our system. The experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results from image databases.
9847925
A multimedia semantic model for RTSP-based multimedia presentation systems
An abstract multimedia semantic model called the multimedia augmented transition network (MATN) model is proposed to model the RTSP-based (real-time streaming protocol) multimedia presentation systems. RTSP provides a set of methods to support the functionality of "VCR-like" remote control on the client side so that users are allowed to control playback of the streaming media. The MATN model is used to model RTSP actions as well as temporal relations and synchronization control of the media streams. The advantages of using the MATN model are its simplicity, effectiveness, and ease of modification. Furthermore, a formal definition of the MATN model and modeling of RTSP actions using the MATN model are presented in detail.
21282948
Information-Based Cooperation in Multiple Agent Systems
In this paper, we divide the cooperation in multiple agent systems into task-based cooperation and information-based cooperation. We describe information-based cooperation as information synthesizing and decision making. To implement information synthesizing, agents' beliefs are decomposed into two levels. The higher level represents the possible information, and the lower level subsequently estimates a number function for the belief by synthesizing the possible information.
9240156
TeraScope: distributed visual data mining of terascale data sets over photonic networks
TeraScope is a framework and a suite of tools for interactively browsing and visualizing large terascale data sets. Unique to TeraScope is its utilization of the Optiputer paradigm to treat distributed computer clusters as a single giant computer, where the dedicated optical networks that connect the clusters serve as the computer's system bus. TeraScope explores one aspect of the Optiputer architecture by employing a distributed pool of memory, called LambdaRAM, that serves as a massive data cache for supporting parallel data mining and visualization algorithms.