Unsupervised Segmentation of Stereoscopic Video Objects: Constrained Segmentation Fusion Versus Greedy Active Contours

In this paper, we present a novel memory access reduction scheme (MARS) for two-dimension fast cosine transform (2-D FCT). It targets programmable DSPs with high memory-access latency. It reduces the number of memory accesses by: 1) reducing the number of weighting factors and 2) combining butterflies in vector-radix 2-D FCT pruning diagram from two stages to one stage with an efficient structure. Hardware platform based on general purpose processor is used to verify the effectiveness of the proposed method for vector-radix 2-D FCT pruning implementation. Experimental results validate the benefits of the proposed method with reduced memory access, less clock cycle and fewer memory space compared with the conventional implementation.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.     

3D measures exploitation for a monocular semi-supervised fall detection system

Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. Thus, the development of robust home surveillance systems is of great importance. In this article, such a system is presented, which tries to address the fall detection problem through visual cues. The proposed methodology utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object. At the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning approach. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.     

Multiagent reason maintenance and group adaptability

This paper describes research on an algorithm for improving the efficiency of the algorithm of Huhns and Bridgeland (1991) for justification-based, distributed reason maintenance (DRM). Improvements are made by ordering the relabel attempts by relative group adaptability, a measure of the relative difficulty of achieving a consistent relabeling of shared beliefs among a group of agents. Estimates of group adaptability can be made by retaining a history of relabeling efforts, using these along with a problem-solving principle of recency, similar to the virtual-memory principle of locality. An implementation has been accomplished and an example of this technique is presented     

Adaptable deep learning structures for object labeling/tracking under dynamic visual environments

Multimedia Tools and Applications - In this paper, we propose a self-adaptive deep neural network architecture suitable for object tracking and labelling. In particular, an adaptation mechanism is...     

Exploiting semantic proximities for content search over p2p networks

In this paper, we address the issue of content search over peer-to peer networks. We use the concept of semantic proximity that exploits the commonalities of interests exhibited among peer users so as to decompose the network into semantic clusters. We initially define search entropy, as a metric indicating the average number of packets required to locate the requested content. Then, spectral clustering is used to organize the peer nodes into semantic clusters so that (a) the probability that a node locates content within its own cluster is maximized, while simultaneously; (b) the respective probability of finding this content outside this cluster is minimized. The proposed semantic partitioning algorithm is then extended into a hierarchical two-tier scheme, in which practical issues arising for the deployment of a peer-to-peer (p2p) application can be more easily addressed. After the system has been initialized, a dynamic algorithm places new users that join the p2p network into appropriately selected clusters and also handles peer departures without the need for matrix eigen decomposition process which is necessary for the assessment of the initial static partitioning. Our experimental results validate that (a) our static partitioning outperforms traditional and novel search techniques and (b) our dynamic algorithm is able to efficiently track the system’s progression maintaining the search entropy close to the initially assessed levels.