1,253 research outputs found
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition
Iris recognition algorithms, especially with the
emergence of large-scale iris-based identification systems, must
be tested for speed and accuracy and evaluated with a wide
range of templates – large size, long-range, visible and different
origins. This paper presents the acquisition of eye-iris images
of dark-skinned subjects in Africa, a predominant case of verydark-
brown iris images, under near-infrared illumination. The
peculiarity of these iris images is highlighted from the
histogram and normal probability distribution of their
grayscale image entropy (GiE) values, in comparison to Asian
and Caucasian iris images. The acquisition of eye-images for
the African iris dataset is ongoing and will be made publiclyavailable
as soon as it is sufficiently populated
An information-driven framework for image mining
[Abstract]: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how low-level, pixel representation contained in a raw image or
image sequence can be processed to identify high-level spatial objects and relationships. To meet
this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval
techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful
patterns/knowledge from each level
Improved image classification with neural networks by fusing multispectral signatures with topological data
Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent
Image mining: issues, frameworks and techniques
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an
interdisciplinary endeavor that draws upon expertise in
computer vision, image processing, image retrieval, data
mining, machine learning, database, and artificial
intelligence. Despite the development of many
applications and algorithms in the individual research
fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper
Impact of Feature Representation on Remote Sensing Image Retrieval
Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task. Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process
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