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In this paper, a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3D object. The descriptor is based on taking "snapshots" of the surface over each point using a virtual camera oriented perpendicularly to the surface. This representation has the advantage of imposing minimal loss of information be robust to self-occlusions and also be very efficient to compute. Then, we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap. Index Terms--Surface matching, object recognition, partially overlapping surfaces.

A novel scheme for error-resilient image transmission is proposed by employing a product coder consisting of low-density parity check (LDPC) codes and Reed-Solomon codes in order to deal effectively with bit errors. The results have shown the superiority of the proposed system in comparison to other techniques for image transmission.

An image transmission scheme is proposed for the communication of JPEG2000 images over wireless channels. The proposed system recognizes the compressed JPEG2000 stream in a product-code scheme consisting of Turbo codes and Reed-Solomon codes.

A novel image transmission scheme for communicating set partitioning in hierarchical trees image streams over wireless channels is presented. Turbo codes and Reed-Solomon codes are employed by the new scheme for dealing effectively with burst errors.

An approach to knowledge-assisted semantic video object detection based on a multimedia ontology infrastructure is presented. Semantic concepts in the context of the examined domain are defined in an ontology, enriched with qualitative attributes (e.g., color homogeneity), low-level features (e.g., color model components distribution), object spatial relations, and multimedia processing methods (e.g., color clustering). Semantic Web technologies are used for knowledge representation in the RDF(S) metadata standard. Rules in F-logic are defined to describe how tools for multimedia analysis should be applied, depending on concept attributes and low-level features, for the detection of video objects corresponding to the semantic concepts defined in the ontology. This supports flexible and managed execution of various application and domain independent multimedia analysis tasks. Furthermore, this semantic analysis approach can be used in semantic annotation and transcoding systems, which take into consideration the users environment including preferences, devices used, available network bandwidth and content identity. The proposed approach was tested for the detection of semantic objects on video data of three different domains. Index Terms--Knowledge-assisted analysis, multimedia ontologies, video analysis.

This paper presents a novel object-based method for the generation of a stereoscopic image sequence from a monoscopic video, using bidirectional two-dimensional motion estimation for the recovery of rigid motion and structure and a Bayesian framework to handle occlusions. The latter is based on extended Kalman filters and an efficient method for reliably tracking object masks. Experimental results show that the layered object scene representation, combined with the proposed algorithm for reliably tracking object masks throughout the sequence, yields very accurate results. Index Terms--Bayesian classification, object tracking, recursive filtering, stereoscopic video generation, structure reconstruction.

A complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data based on a low-cost 3-D sensor capable of real-time acquisition of 3-D and color images is developed. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images and the experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information.

The performance of nonlinear locally optimal blind watermark detection schemes for DCT-transformed images is examined. The findings show that the use of nonlinearities can lead to performance as good as, or even better than, that of the optimal generalized Gaussian detector.

The widespread use of digital multimedia data has increased the need for effective means of copyright protection. Watermarking has attracted much attention, as it allows the embedding of a signature in a digital document in an imperceptible manner. In practice, watermarking is subject to various attacks, intentional or unintentional, which degrade the embedded information, rendering it more difficult to detect. A very common, but not malicious, attack is quantization, which is unavoidable for the compression and transmission of digital data. The effect of quantization attacks on the nearly optimal Cauchy watermark detector is examined in this paper. The quantization effects on this detection scheme are examined theoretically, by treating the watermark as a dither signal. The theory of dithered quantizers is used in order to correctly analyze the effect of quantization attacks on the Cauchy watermark detector. The theoretical results are verified by experiments that demonstrate the quantization effects on the detection and error probabilities of the Cauchy detection scheme. Index Terms--Cauchy detector, dither theory, quantization attacks, watermarking.

A novel unsupervised video object segmentation algorithm is presented, aiming to segment a video sequence to objects: spatiotemporal regions representing a meaningful part of the sequence. The proposed algorithm consists of three stages: initial segmentation of the first frame using color, motion, and position information, based on a variant of the K-Means-with-connectivity-constraint algorithm; a temporal tracking algorithm, using a Bayes classifier and rule-based processing to reassign changed pixels to existing regions and to efficiently handle the introduction of new regions; and a trajectory-based region merging procedure that employs the long-term trajectory of regions, rather than the motion at the frame level, so as to group them to objects with different motion. As shown by experimental evaluation, this scheme can efficiently segment video sequences with fast moving or newly appearing objects. A comparison with other methods shows segmentation results corresponding more accurately to the real objects appearing on the image sequence. Index Terms--Image sequence analysis, temporal tracking, trajectory-based merging, video segmentation.

In this paper, a novel algorithm is presented for the real-time, compressed-domain, unsupervised segmentation of image sequences and is applied to video indexing and retrieval. The segmentation algorithm uses motion and color information directly extracted from the MPEG-2 compressed stream. An iterative rejection scheme based on the bilinear motion model is used to effect foreground/background segmentation. Following that, meaningful foreground spatiotemporal objects are formed by initially examining the temporal consistency of the output of iterative rejection, clustering the resulting foreground macroblocks to connected regions and finally performing region tracking. Background segmentation to spatiotemporal objects is additionally performed. MPEG-7 compliant low-level descriptors describing the color, shape, position, and motion of the resulting spatiotemporal objects are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. This, combined with a relevance feedback mechanism, allows the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) and the retrieval of relevant video segments. Desired spatial and temporal relationships between the objects in multiple-keyword queries can also be expressed, using the shot ontology. Experimental results of the application of the segmentation algorithm to known sequences demonstrate the efficiency of the proposed segmentation approach. Sample queries reveal the potential of employing this segmentation algorithm as part of an object-based video indexing and retrieval scheme. Index Terms--Compressed-domain segmentation, object-based video indexing, ontologies, real-time segmentation, relevance feedback, spatiotemporal video segmentation, support vector machines.

A novel image sequence segmentation method which combines both spatial and temporal information is presented in this paper. The first step is an intensity segmentation scheme based on the edgeflow method. The temporal information is introduced through three-dimensional (3-D) motion estimation parameters. In the second step, regions obtained from the first step are clustered according to their 3-D motion models. In order to reduce the noise sensitivity of the motion estimation process, we introduce a robust method which produces accurate motion parameters and facilitates the correct clustering that follows. This ensures that rigid objects with luminance discontinuities can be segmented correctly. The method has been successfully tested in real imagery and typical examples are presented in this paper. Index Terms--Point correspondences, region merging, spatio-temporal segmentation, three-dimensional (3-D) motion estimation.