Scarf : Semi-automatic colorization and reliable image fusion
- Authors: Ul-Haq, Anwaar , Gondal, Iqbal , Murshed, Manzur
- Date: 2010
- Type: Text , Conference paper
- Relation: 2010 Digital Image Computing: Techniques and Applications p. 435-440
- Full Text: false
- Reviewed:
- Description: Nighttime imagery poses significant challenges to its enhancement due to loss of color information and limitation of single sensor to capture complete visual information at night. To cope with this challenge, multiple sensors are used to capture reliable nighttime imagery which presents additional demands for reliable visual information fusion. In this paper, we present a system, Scarf, which proposes reliable image fusion using advanced feature extraction techniques and a novel semi-automatic colorization based on optimization conformal to human visual system. Subjective and objective quality evaluation proves the effectiveness of proposed system.
Attack-resistant sensor localization under realistic wireless signal fading
- Authors: Iqbal, Anindya , Murshed, Manzur
- Date: 2010
- Type: Text , Conference paper
- Relation: 2010 IEEE Wireless Communications and Networking Conference p. 1-6
- Full Text: false
- Reviewed:
- Description: In a decentralized sensor network, localization process relies on the integrity of participating sensors. Existence of malicious beacon nodes in the vicinity of non-beacon nodes affects this process. This paper presents a trilateration-based secure localization technique, which is capable of estimating the location of a sensor with high accuracy so long four neighbouring beacon nodes are benign, irrespective of the number of neighbouring liars and without assuming any trust model. In realistic scenarios of wireless environment where transmitted signals attenuate randomly due to fading, the liar-tolerance level of this attack-resistant technique has to be relaxed accordingly. Superiority of this technique against the state-of-the-art has been established with extensive simulation results in terms of location estimation accuracy and liar-filtering probability.