Search results for "visual"
showing 10 items of 7386 documents
Interactive Image Retrieval Using Smoothed Nearest Neighbor Estimates
2010
Relevance feedback has been adopted by most recent Content Based Image Retrieval systems to reduce the semantic gap that exists between the subjective similarity among images and the similarity measures computed in a given feature space. Distance-based relevance feedback using nearest neighbors has been recently presented as a good tradeoff between simplicity and performance. In this paper, we analyse some shortages of this technique and propose alternatives that help improving the efficiency of the method in terms of the retrieval precision achieved. The resulting method has been evaluated on several repositories which use different feature sets. The results have been compared to those obt…
Non-linear Invertible Representation for Joint Statistical and Perceptual Feature Decorrelation
2000
The aim of many image mappings is representing the signal in a basis of decorrelated features. Two fundamental aspects must be taken into account in the basis selection problem: data distribution and the qualitative meaning of the underlying space. The classical PCA techniques reduce the statistical correlation using the data distribution. However, in applications where human vision has to be taken into account, there are perceptual factors that make the feature space uneven, and additional interaction among the dimensions may arise. In this work a common framework is presented to analyse the perceptual and statistical interactions among the coefficients of any representation. Using a recen…
Biologically Inspired Vision Architectures: a Software/Hardware Perspective
2007
Even tough the field of computer vision has seen huge improvement in the last few decades, computer vision systems still lack, in most cases, the efficiency of biological vision systems. In fact biological vision systems routinely accomplish complex visual tasks such as object recognition, obstacle avoidance, and target tracking, which continue to challenge artificial systems. The study of biological vision system remains a strong cue for the design of devices exhibiting intelligent behaviour in visually sensed environments but current artificial systems are vastly different from biological ones for various reasons. First of all, biologically inspired vision architectures, which are continu…
P2PStudio - Monitoring, Controlling and Visualization Tool for Peer-to-Peer Networks Research
2006
Peer-to-Peer Studio has been developed as a monitoring, controlling and visualization tool for peer-to-peer networks. It uses a centralized architecture to gather events from a peer-to-peer network and can be used to visualize network topology and to send different commands to individual peer-to-peer nodes. The tool has been used with Chedar Peer-to-Peer network to study the behavior of different peer-to-peer resource discovery and topology management algorithms and for visualizing the results of NeuroSearch resource discovery algorithm produced by the Peer-to-Peer Realm network simulator. This paper presents the features, the architecture and the protocols of Peer-to-Peer Studio and the ex…
A Dual Taxonomy for Defects in Digitized Historical Photos
2009
Old photos may be affected by several types of defects. Manual restorers use their own taxonomy to classify damages by which a photo is affected, in order to apply the proper restoration techniques for a specific defect. Once a photo is digitally acquired, defects become part of the image, and their aspect change. This paper wants to be a first attempt to correlate real defects of printed photos, and digital defects of their digitized versions. A dual taxonomy is proposed, for real and digital defects, and used to classify an image dataset, for a posteriori comparative study. Furthermore, a set of digital features is analyzed for digitized images, to identify which of them could be useful f…
Sectors on sectors (SonS): A new hierarchical clustering visualization tool
2011
Clustering techniques have been widely applied to extract information from high-dimensional data structures in the last few years. Graphs are especially relevant for clustering, but many graphs associated with hierarchical clustering do not give any information about the values of the centroids' attributes and the relationships among them. In this paper, we propose a new visualization approach for hierarchical cluster analysis in which the above-mentioned information is available. The method is based on pie charts. The pie charts are divided into several pie segments or sectors corresponding to each cluster. The radius of each pie segment is proportional to the number of patterns included i…
Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency
2018
In this work, we propose a convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes without having access to the reference. The proposed CNN architecture is fed by small patches selected carefully according to their level of saliency. To do so, the visual saliency of the 3D mesh is computed, then we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Afterward, the obtained views are split to obtain 2D small patches that pass through a saliency filter to select the most relevant patches. Experiments are conducted on two MVQ assessment databases, and the results show that the trained CNN achieves good rates in terms of corre…
Analyse des Visuellen Klassifikationssystems Durch Detektionsexperimente
1977
Summary Experiments on recognizing statistically distorted patterns show that the human visual system operates as a linear classifier. The spatial frequency range, within which features are extracted, is determined by the coupling in the area of sharpest vision (2°). The relevant features for classifying patterns are not produced by isotropic filtering
Advances in the statistical methodology for the selection of image descriptors for visual pattern representation and classification
1995
Recent advances in the statistical methodology for selecting optimal subsets of features (image descriptors) for visual pattern representation and classification are presented. The paper attempts to provide a guideline about which approach to choose with respect to the a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. References to more detailed material about each one of the methods are given and experimental results supporting the main conclusions are briefly outlined.
Stereoscopic 3D visualization of particle fields reconstructed from digital inline holograms
2010
Abstract Holography is a powerful tool as it codes information, e.g. on 3D positions in a particle field in a single 2D hologram. In digital holography, the holograms are recorded on a digital image sensor. It is a particular challenge to visualize a digital hologram's depth information, such that it can be understood intuitively while retaining the advantages of a numerical reconstruction. In this contribution it is suggested and demonstrated how a numerically constructed volume can be used to calculate stereoscopic views, which even in the case of non-diffuse illumination allow for an intuitive visualization of particles’ positions in 3D space.