4,606 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
Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm
Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited – such as underwater, or in hostile regions – this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 o , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m
Density Invariant Contrast Maximization for Neuromorphic Earth Observations
Contrast maximization (CMax) techniques are widely used in event-based vision
systems to estimate the motion parameters of the camera and generate
high-contrast images. However, these techniques are noise-intolerance and
suffer from the multiple extrema problem which arises when the scene contains
more noisy events than structure, causing the contrast to be higher at multiple
locations. This makes the task of estimating the camera motion extremely
challenging, which is a problem for neuromorphic earth observation, because,
without a proper estimation of the motion parameters, it is not possible to
generate a map with high contrast, causing important details to be lost.
Similar methods that use CMax addressed this problem by changing or augmenting
the objective function to enable it to converge to the correct motion
parameters. Our proposed solution overcomes the multiple extrema and
noise-intolerance problems by correcting the warped event before calculating
the contrast and offers the following advantages: it does not depend on the
event data, it does not require a prior about the camera motion, and keeps the
rest of the CMax pipeline unchanged. This is to ensure that the contrast is
only high around the correct motion parameters. Our approach enables the
creation of better motion-compensated maps through an analytical compensation
technique using a novel dataset from the International Space Station (ISS).
Code is available at \url{https://github.com/neuromorphicsystems/event_warping
Mobile Robots
The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations
Rocket: Efficient and Scalable All-Pairs Computations on Heterogeneous Platforms
All-pairs compute problems apply a user-defined function to each combination
of two items of a given data set. Although these problems present an abundance
of parallelism, data reuse must be exploited to achieve good performance.
Several researchers considered this problem, either resorting to partial
replication with static work distribution or dynamic scheduling with full
replication. In contrast, we present a solution that relies on hierarchical
multi-level software-based caches to maximize data reuse at each level in the
distributed memory hierarchy, combined with a divide-and-conquer approach to
exploit data locality, hierarchical work-stealing to dynamically balance the
workload, and asynchronous processing to maximize resource utilization. We
evaluate our solution using three real-world applications (from digital
forensics, localization microscopy, and bioinformatics) on different platforms
(from a desktop machine to a supercomputer). Results shows excellent efficiency
and scalability when scaling to 96 GPUs, even obtaining super-linear speedups
due to a distributed cache
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