Echo hiding based stereo audio watermarking against pitch-scaling attacks

In audio watermarking, the robustness against pitch-scaling attack, is one of the most challenging problems. In this paper, we propose an algorithm, based on traditional time-spread(TS) echo hiding based audio watermarking to solve this problem. In TS echo hiding based watermarking, pitch-scaling attack shifts the location of pseudonoise (PN) sequence which appears in the cepstrum domain. Thus, position of the peak, which occurs after correlating with PN-sequence changes by an un-known amount and that causes the error. In the proposed scheme, we replace PN-sequence with unit-sample sequence and modify the decoding algorithm in such a way it will not depend on a particular point in cepstrum domain for extraction of watermark. Moreover proposed algorithm is applied to stereo audio signals to further improve the robustness. Experimental results illustrate the effectiveness of the proposed algorithm against pitch-scaling attacks compared to existing methods. In addition to that proposed algorithm also gives better robustness against other conventional signal processing attacks.

Stereo vision and wavelets/multiwavelets analysis : 3d reconstruction using wavelets/multiwavelets theory and stereo vision

During the image formation process of the camera, explicit 3D information about the scene or objects in the scene are lost. Therefore, 3D structure or depth information has to be inferred implicitly from the 2D intensity images. This problem is com- monly referred to as 3D reconstruction. In this work a complete 3D reconstruction algorithm is presented, capable of reconstructing dimensionally accurate 3D models of the objects, based on stereo vision and multi-resolution analysis. The developed system uses a reference depth model of the objects under observation to improve the disparity maps, estimated. Only a few features are extracted from that reference model, which are the relative location of the discontinuities and the z-dimensional extremes of objects depth. The maximum error deviation of the estimated depth along the surfaces is less than 0.5mm and along the discontinuities is less than 1.5mm. The developed system is invariant to illuminative variations, and orientation, location and scaling of the objects under consideration, which makes the developed system highly robust.

Generating comprehensible summaries of rushes sequences based on robust feature matching

This paper describes our first attempt at tackling a pilot task in Trecvid: video summarization of rushes data [3]. Our method is based on the tight clustering produced via SIFT matching. In this first attempt, we try to examine how our approach performs without complex implementation in terms of concept detection and excerpt assembly (i.e, no picture-in-picture, split screen and special transitions). Although we do not perform very well in terms of concept inclusion, we rank very well in terms of the summary being easy to understand and relevancy of included segments.

Linking identities and viewpoints in home movies based on robust feature matching

The identification of useful structures in home video is difficult because this class of video is distinguished from other video sources by its unrestricted, non edited content and the absence of regulated storyline. In addition, home videos contain a lot of motion and erratic camera movements, with shots of the same character being captured from various angles and viewpoints. In this paper, we present a solution to the challenging problem of clustering shots and faces in home videos, based on the use of SIFT features. SIFT features have been known to be robust for object recognition; however, in dealing with the complexities of home video setting, the matching process needs to be augmented and adapted. This paper describes various techniques that can improve the number of matches returned as well as the correctness of matches. For example, existing methods for verification of matches are inadequate for cases when a small number of matches are returned, a common situation in home videos. We address this by constructing a robust classifier that works on matching sets instead of individual matches, allowing the exploitation of the geometric constraints between matches. Finally, we propose techniques for robustly extracting target clusters from individual feature matches.

Symbolic representation and distributed matching strategies for schematics

This paper describes object-centered symbolic representation and distributed matching strategies of 3D objects in a schematic form which occur in engineering drawings and maps. The object-centered representation has a hierarchical structure and is constructed from symbolic representations of schematics. With this representation, two independent schematics representing the same object can be matched. We also consider matching strategies using distributed algorithms. The object recognition is carried out with two matching methods: (1) matching between an object model and observed data at the lowest level of the hierarchy, and (2) constraints propagation. The first is carried out with symbolic Hopfield-type neural networks and the second is achieved via hierarchical winner-takes-all algorithms

Graph matching : fast candidate elimination using machine learning techniques

Graph matching is an important class of methods in pattern recognition. Typically, a graph representing an unknown pattern is matched with a database of models. If the database of model graphs is large, an additional factor in induced into the overall complexity of the matching process. Various techniques for reducing the influence of this additional factor have been described in the literature. In this paper we propose to extract simple features from a graph and use them to eliminate candidate graphs from the database. The most powerful set of features and a decision tree useful for candidate elimination are found by means of the C4.5 algorithm, which was originally proposed for inductive learning of classication rules. Experimental results are reported demonstrating that effcient candidate elimination can be achieved by the proposed procedure.

Vision, ambiguity and perceptual possibility : why pictures matter in the critique of stereo-immersive VR

The acquisition of three-dimensional immersive space through advanced digitial imaging technology, suggests a profound shift from the relatively impoverished representational stratgies of two-dimensional pictorial imagery.  This entails an epistemological shift from pictorial representation, to the presentation of actual three-dimensional space through stereoscopic (3D) imagery.  Moreover, it suggests that visuality rather than 'virtuality' is the core issue in understanding the nature of the epistemological shift associated with stereo-immersive VR.

A shift in visual epistemology from 'flat' pictorial representation to three-dimensional stereo-immersion suggests a gainful move toward a visuality imbued with spatial possibilities.  In quantitative terms, these visual-spatial gains may seem self-evident.  However, certain aspects peculiar to pictorial representation are missing from stereo-immersive imaging.  That is lost in stereo-immersive imaging, and how it can be measured?

This thesis proposes that the inherent ambiguity of two-dimensional representations of three-dimensional spatial structure in pictures, invokes a perceptual response in which pictorial spaces elicit 'perceptual possibility'.  The robust three-dimentional spatiality of stereo-immersvie VR foreclosures such possibility.  Through examining stereo-immersive VR in terms of its visuality, the thesis develops a new appraoch to understanding VR that solves some of the issues associated with the problematic concept of 'virtuality'.  In addition, the thesis finds that by placing stereo-immersive VR and pictures within the shared paradigm of 'the visual', an important dimension of pictures that has been overlooked in past analyses re-emerges : the thesis proposes the concept of 'artifactuality' to account for the way pictures are fundamentally, and in the first instance, aesthetic objects for visual perception.  It is in their manner of appearing as pictures, that they are perceived as pictures of something.  It is from this fundamental basis that their many levels of meaning and signification - their manifold 'realisms' - can arise.

This thesis therefore addresses two intersecting problems within the paradigm of the visual: it proposes 1) that analyses of 'the virtual' be grounded in the 'artifactuality' of pictorial perception, and 2) that the spatiality of stereo-immersive VR be reinvigorated by purposefully 'under-contraining' its key percept - the robust, 'solid' stereoscopic structuring of visual space.  This approach opens up the discourse of stereo-immersive VR to new visual paradigms.  The thesis proposes that these be modelled not on the impossibility of 'the virtual', but on the possibilities of visual ambiguity drawn from the analysis of pictorial perception.

Generation of 3D sparse feature models using multiple stereo views

Augmented Reality (AR) renders virtual information onto objects in the real world. This new user interface paradigm presents a seamless blend of the virtual and real, where the convergence of the two is difficult to discern. However, errors in the registration of the real and virtual worlds are common and often destroy the AR illusion. To achieve accurate and efficient registration, the pose of real objects must be resolved in a quick and precise manner.

Modeling of a stereo vision system using a genetic algorithm based fuzzy linear regression

In this paper a fuzzy linear regression (FLR) model integrated with a genetic algorithm (GA) is proposed. The proposed GA-FLR model is applied to modeling of a stereo vision system. A set of empirical data from stereo vision object measurement is collected based on the full factorial design technique. Three regression models, namely ordinary least-squares regression (OLS), FLR, and GA-FLR, are developed, and with their performances compared. The results show that the proposed GA-FLR model performs better than OLS and FLR in modeling of a stereo vision system.