Aboul Ella Hassanien (Abo)

Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.

We propose an image morphing algorithm which uses Navier elastic body splines to generate warp functions for interpolating scattered data points. The spline is based on a partial differential equation proposed by Navier that describes the equilibrium displacement of an elastic body subjected to forces. The spline maps can be expressed as the linear combination of an affine transformation and a Navier interpolation spline. The proposed algorithm generates a smooth warp that reflects feature point correspondences. It is efficient in time complexity and smoothly interpolated morphed images with only a remarkably small number of specified feature points. The algorithm allows each feature point in the source image to be mapped to the corresponding feature point in the destination image. Once the images are warped to align the positions of features and their shapes, the in-between facial animation from two given facial images can be defined by cross dissolving the positions of correspondence features and their shapes and colors. We describe an efficient cross dissolve algorithm for generating the in-between images

This paper presents an automatic image annotation approach that integrates the random forest classifier with particle swarm optimization algorithm for classes’ scores weighting.
The proposed hybrid approach refines the output of multiclass classification that is based on the usage of random forest classifier for automatically labeling images with a number of
words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. Images feature vectors are clustered into K clusters and a random forest classifier is trained for each cluster. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal weighting for classes’ scores from random forest classifiers. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance
of other approaches, considering annotation accuracy, for the
experimented dataset.

In this paper, a system for feature selection based
on chicken swarm optimization (CSO) algorithm is proposed.
Datasets ordinarily includes a huge number of attributes, with
irrelevant and redundant attribute. Commonly wrapper-based
approaches are used for feature selection but it always requires
an intelligent search technique as part of the evaluation function.
Chicken swarm optimization (CSO)is a new bio-inspired
algorithm mimicking the hierarchal order of the chicken
swarm and the behaviors of chicken swarm, including roosters,
hens and chicks, CSO can efficiently extract the chickens’
swarm intelligence to optimize problems. Therefore, CSO was
employed to feature selection in wrapper mode to search
the feature space for optimal feature combination maximizing
classification performance, while minimizing the number of
selected features. The proposed system was benchmarked
on 18 datasets drawn from the UCI repository and using
different evaluation criteria and proves advance over particle
swarm optimization (PSO) and genetic algorithms (GA) that
commonly used in optimization problems

Thermal imaging is a technology with property of
seeing objects in the darkness. Such property makes this technology
very important tool for security and surveillance applications.
In this paper, a thermal image authentication technique using
hash function is proposed. In this technique, the thermal images
are used as cover images and bits from secret data (i.e. messages
or images) are then hidden in the cover images. This is achieved
by using the hash function and IntegerWavelet Transform (IWT).
1, 2 and 3 bits per bytes have been hidden in both horizontal
and vertical components of wavelet transform. The proposed
technique has been evaluated based on mean square error (MSE),
peak signal to noise ratio (PSNR), image fidelity (IF) and standard
deviation (SD). The results have shown better performance of the
proposed technique comparing with the most related work.