Search results for "visual"
showing 10 items of 7386 documents
A combined analysis to extract objects in remote sensing images
1999
Abstract This paper describes an object recognition system to extract shape information from remote sensing images. One of the goals is to determine if towers and power lines can be seen on one-meter imagery and how much ground conditions can influence the resolution power of the recognition algorithms. To this end, an integrated analysis system has been implemented inside the Remote Sensing Imaging System (RSIS). The methodology consists in the combination of statistical and structural information. It has been tested on real images and it will be integrated in an automatic system for the assessment of post storm damage.
Seam-Based Edge Blending for Multi-Projection Systems
2016
Perceptual seamlessness of large-scale tiled displays is still a challenge. One way to avoid Bezel effects from contiguous displays is to blend superimposed parts of the image over the edges. This work proposes a new approach for edge blending. It is based on intensity edge blending adapted on the seam description of the image content. The main advantage of this method is to reduce visual artifacts thanks to context adaptation and smooth transitions. We evaluate the quality of the method with a perceptual experiment where it is compared with state-of-the-art methods. The new method shows most improvement in low frequency areas compared to the other techniques. This method can be inserted in…
A novel Bayesian framework for relevance feedback in image content-based retrieval systems
2006
This paper presents a new algorithm for image retrieval in content-based image retrieval systems. The objective of these systems is to get the images which are as similar as possible to a user query from those contained in the global image database without using textual annotations attached to the images. The main problem in obtaining a robust and effective retrieval is the gap between the low level descriptors that can be automatically extracted from the images and the user intention. The algorithm proposed here to address this problem is based on the modeling of user preferences as a probability distribution on the image space. Following a Bayesian methodology, this distribution is the pr…
Real-time people counting system using a single video camera
2008
This is the copy of journal's version originally published in Proc. SPIE 6811. Reprinted with permission of SPIE: http://spie.org/x10.xml?WT.svl=tn7 There is growing interest in video-based solutions for people monitoring and counting in business and security applications. Compared to classic sensor-based solutions the video-based ones allow for more versatile functionalities, improved performance with lower costs. In this paper, we propose a real-time system for people counting based on single low-end non-calibrated video camera. The two main challenges addressed in this paper are: robust estimation of the scene background and the number of real persons in merge-split scenarios. The latter…
Resolving ambiguities in a grounded human-robot interaction
2009
In this paper we propose a trainable system that learns grounded language models from examples with a minimum of user intervention and without feedback. We have focused on the acquisition of grounded meanings of spatial and adjective/noun terms. The system has been used to understand and subsequently to generate appropriate natural language descriptions of real objects and to engage in verbal interactions with a human partner. We have also addressed the problem of resolving eventual ambiguities arising during verbal interaction through an information theoretic approach.
A convolutional neural network framework for blind mesh visual quality assessment
2017
In this paper, we propose a new method for blind mesh visual quality assessment using a deep learning approach. To do this, we first extract visual representative features by computing locally curvature and dihedral angles from each distorted mesh. Then, we determine from these features a set of 2D patches which are learned to a convolutional neural network (CNN). The network consists of two convolutional layers with two max-pooling layers. Then, a multilayer perceptron (MLP) with two fully connected layers is integrated to summarize the learned representation into an output node. With this network structure, feature learning and regression are used to predict the quality score of a given d…
Towards Advanced Visualisation Techniques in Case
1999
The complexity of information systems has resulted in more sophisticated CASE tools which integrate multifaceted design information using metamodeling and hypertext technologies. A designer can use this vast amount of tightly coupled information efficiently only if it is presented based on his needs and cognitive capabilities. In this paper we discuss how representations in CASE can be improved using advanced visualisation techniques.
Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data
2018
The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimen- sionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in par- ticular when computational resources are limited such as in mobile terminals. In this paper we present the use of a "conceptual" space, automatically induced from data, to perform automa…
An Unsupervised Method for Suspicious Regions Detection in Mammogram Images
2015
Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform …
Multimodal 2D Image to 3D Model Registration via a Mutual Alignment of Sparse and Dense Visual Features
2018
International audience; Many fields of application could benefit from an accurate registration of measurements of different modalities over a known 3D model. However, aligning a 2D image to a 3D model is a challenging task and is even more complex when the two have a different modality. Most of the 2D/3D registration methods are based on either geometric or dense visual features. Both have their own advantages and their own drawbacks. We propose, in this paper, to mutually exploit the advantages of one feature type to reduce the drawbacks of the other one. For this, an hybrid registration framework has been designed to mutually align geometrical and dense visual features in order to obtain …