Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution

Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a reference frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma de Mallorca, Spai

Fuzzy image segmentation using location and intensity information

The segmentation results of any clustering algorithm are very sensitive to the features used in the similarity measure and the object types, which reduce the generalization capability of the algorithm. The previously developed algorithm called image segmentation using fuzzy clustering incorporating spatial information (FCSI) merged the independently segmented results generated by fuzzy clustering-based on pixel intensity and pixel location. The main disadvantages of this algorithm are that a perceptually selected threshold does not consider any semantic information and also produces unpredictable segmentation results for objects (regions) covering the entire image. This paper directly addresses these issues by introducing a new algorithm called fuzzy image segmentation using location and intensity (FSLI) by modifying the original FCSI algorithm. It considers the topological feature namely, connectivity and the similarity based on pixel intensity and surface variation. Qualitative and quantitative results confirm the considerable improvements achieved using the FSLI algorithm compared with FCSI and the fuzzy c-means (FCM) algorithm for all three alternatives, namely clustering using only pixel intensity, pixel location and a combination of the two, for a range of sample of images

HICOD2000 - Integrated system for coding, protection and trading of earth observation products in JPEG2000

This paper describes a system, called HICOD2000, derived from a project developed in collaboration between two Portuguese companies and the European Space Agency that exploited the possibilities offered by the emerging image encoding standard - JPEG2000 - and its applicability to Earth Observation Products. The work described on this paper has been completed and it's currently being integrated with ESA own systems and EO products portal. Other important characteristic of the system consists in the usage of a Digital Rights Management platform to control the access and usage of EO products coded in JPEG2000 format.info:eu-repo/semantics/acceptedVersio

3D face recognition based on machine learning

3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity

Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms

The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic repre¬sentation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classifica¬tion tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifier

Computation of curvatures over discrete geometry using biharmonic surfaces

The computation of curvature quantities over discrete geometry is often required when processing geometry composed of meshes. Curvature information is often important for the purpose of shape analysis, feature recognition and geometry segmentation. In this paper we present a method for accurate estimation of curvature on discrete geometry especially those composed of meshes. We utilise a method based on fitting a continuous surface arising from the solution of the Biharmonic equation subject to suitable boundary conditions over a 1-ring neighbourhood of the mesh geometry model. This enables us to accurately determine the curvature distribution of the local area. We show how the curvature can be computed efficiently by means of utilising an analytic solution representation of the chosen Biharmonic equation. In order to demonstrate the method we present a series of examples whereby we show how the curvature can be efficiently computed over complex geometry which are represented discretely by means of mesh models

Spectral Test via Discrete Tchebichef Transform for Randomness

Random key plays essential roles in cryptography. NIST statistical test suite for randomness is the most comprehensive set of random tests. It has been popular and used as a benchmark test for randomness. One of the random tests is spectral test. There has been some serious problem in spectral test as pointed out by few researchers. In this paper, an alternative test shall be proposed to replace the spectral test. The distribution of discrete orthonormal Tchebichef transform has been obtained based on computational observation being made on random noise. A recommendation on the new random test setting for short cryptographic keys shall also be made