For many years, a lot of museums and countries organize the high definition digitalization of their own collections.
In consequence, they generate massive data for each object. In this paper, we only focus on art painting
collections. Nevertheless, we faced a very large database with heterogeneous data. Indeed, image collection
includes very old and recent scans of negative photos, digital photos, multi and hyper spectral acquisitions,
X-ray acquisition, and also front, back and lateral photos. Moreover, we have noted that art paintings suffer
from much degradation: crack, softening, artifact, human damages and, overtime corruption. Considering
that, it appears necessary to develop specific approaches and methods dedicated to digital art painting analysis.
Consequently, this paper presents a complete framework to evaluate, compare and benchmark devoted to
image processing algorithms.
To help the tourist to discover a city, a region or a park, many options are provided by public tourism travel
centers, by free online guides or by dedicated book guides. Nonetheless, these guides provide only mainstream
information which are not conform to a particular tourist behavior. On the other hand, we may find several
online image databases allowing users to upload their images and to localize each image on a map. These
websites are representative of tourism practices and constitute a proxy to analyze tourism flows. Then, this
work intends to answer this question: knowing what I have visited and what other people have visited, where
should I go now? This process needs to profile users, sites and photos. our paper presents the acquired data and
relationship between photographers, sites and photos and introduces the model designed to correctly estimate
the site interest of each tourism point. The third part shows an application of our schema: a smart travel
guide on geolocated mobile devices. This android application is a travel guide truly matching the user wishes.
In the last decade, we have seen a tremendous emergence of genome sequencing analysis systems. These systems
are limited by the ability to phenotype numerous plants under controlled environmental conditions. To avoid
this limitation, it is desirable to use an automated system designed with plants control growth feature in mind.
For each experimental sequence, many parameters are subject to variations: illuminant, plant size and color,
humidity, temperature, to name a few. These parameters variations require the adjustment of classical plant
detection algorithms. This paper present an innovative and automatic imaging scheme for characterising the
plant's leafs growth. By considering a plant growth sequence it is possible, using the color histogram sequence,
to detect day color variations and, then, to compute to set the algorithm parameters. The main difficulty is to
take into account the automaton properties since the plant is not photographed exactly at the same position
and angle. There is also an important evolution of the plant background, like moss, which needs to be taken
into account. Ground truth experiments on several complete sequences will demonstrate the ability to identify
the rosettes and to extract the plant characteristics whatever the culture conditions are.
Universities, Governmental administrations, photography agencies and many other companies or individuals need framework to manage their multimedia documents and the copyright or authenticity attached to their images. We purpose a web-based interface able to realize many operations: storage, image navigation, copyright insertion, authenticity verification. When a photography owner wants to store and to publish the document on the Internet, he will use the interface
to add his images and set the internet sharing rules. The user can choose for example watermarking method or resolution viewing. He set the parameters visually in way to consider the best ratio between quality and protection. We propose too an authenticity module which will allow online verification of documents. Any user on internet, knowing the key encoding, will be able to verify if an watermarked image have been altered or not. Finally, we will give some practical
examples of our system. In this study, we merge the last technology in image protection and navigation to offer a complete scheme able to manage the images published. It allows to use only one system to supply the security and the publication of their images.
During the last few years, image by content retrieval is the aim of
many studies. A lot of systems were introduced in order to achieve image indexation. One of the most common method is to compute a segmentation and to extract different parameters from regions. However, this segmentation step is based on low level knowledge, without taking into account simple perceptual aspects of images, like the blur. When a photographer decides to focus only on some objects in a scene, he certainly considers very differently these objects from the rest of the scene. It does not represent the same amount of information. The blurry regions may generally be considered as the context and not as the information container by image retrieval tools. Our idea is then to focus the comparison between images by restricting our study only on the non blurry regions, using then these meta data. Our aim is to introduce different features and a machine learning approach in order to reach blur identification in scene images.
Many difficulties of color image processing may be resolved using specific color spaces. The problematic when discussing about image database is the same: in which color space a method will be the most effective. We present classical color spaces, and a tool able to represent images in these spaces in order to analyze which color space is the most relevant on the studied images. Secondly we will introduce hybrid color spaces. The basic idea of hybrid color spaces is to combine several color components from different color spaces in order to increase the effectiveness of color components to discriminate color data, and to reduce correlation rate between color components. Generally computed from an unique image we propose an extension of hybrid computation to generate Hybrid color space from image database. The main idea is to use a set of images as a unique image, and to realize statistical computation on this “virtual” image. Finally, we will present a system able to manage hybrid color space generation on images set, using Icobra and ColorSpace tools.
Query by example is a common model developed for content-based image retrieval. The purpose of such a tool is to extract from a large database the most similar images to a request one. In practice, the meaningful characteristics of each image are first extracted. Then, each region is described with a vector composed with classical statistical features or spatial relationships. Finally, the system proposes to the user the images that minimize a certain similarity distance computed on each vector.
Nevertheless, query by example depends on a criterion determined by the user. Objectively, this last step of any content-based retrieval system then suffers from a large difficulty to express the real hope of the user. Thus, the results are always constrained to the similarity distance definition. In actual fact, it is not sufficient to compute good descriptors, a robust and adequate distance to compare them is also necessary.
Our purpose is more precisely to evaluate different similarity "blob-to-blob" distances. In fact, each image is first described locally using a coarse segmentation and the meaningful regions are extracted using a selection process based on color homogeneity. Among all these parameters, different distances are discussed using different approaches: spatial, shape, color and texture similarities.
The purpose of our visual information retrieval tool is to extract from a database images that are similar to an image query. Color features are generally used to define a measure of similarity between images, as they are usually very robust to noise, image degradation, changes in size, resolution or orientation. Nevertheless, the most often features suffer objectively from the lack of color spatial knowledge. Then, our purpose is to merge two classical methods : the color pyramid and the interest points detection, well-known for grey level image analysis. The pertinence of this new method is demonstrated by an evaluation and a comparison with others keypoints detectors. We show the interest for image indexation with concrete tests on our large images database, using the icobra system.