Abstract not found

In this document, we present an image retrieval system that is based on a segmented representation of the visual content. This representation leads to a comparison of the image content that is more ”semantic” than a classical global comparison. The system compares regions using fuzzy similarity measures that have been show to be psychologically intuitive and easy to aggregate. We then exploit the aggregation between regional similarity measures to let the user build four different types of original visual requests. Our system can handle these requests easily, thanks to its open architecture that let the expert user modify its parameters and compose new aggregation operators on them.

Abstract. Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues related to background updating for visual surveillance are discussed. Experimental results on surveillance examples, such as unloaded packages and unattended objects, are presented by showing those objects as short-term background layers. 1

Abstract. Image segmentation plays an important role in image analysis and image understanding. In this paper, an image segmentation method based on ensemble of SOM neural networks is proposed, which clusters the pixels in an image according to color and spatial features with many SOM neural networks, and then combines the clustering results to give the final segmentation. Experimental results show that the proposed method performs better than some existing clustering-based image segmentation methods. Key words. Image segmentation, SOM, neural networks, neural network ensemble, ensemble learning, unsupervised learning

This paper presents a new color document image segmentation system suitable for historical Arabic manuscripts. Our system is composed of a hybrid method which couple together background light intensity normalization algorithm and k-means clustering with maximum likelihood (ML) estimation, for foreground / background separation. Firstly, the background normalization algorithm performs separation between foreground and background. This foreground is used in later steps. Secondly, our algorithm proceeds on luminance and distort the contrast. These distortions are corrected with a gamma correction and contrast adjustment. Finally, the new enhanced foreground image is segmented to foreground/background on the basis of ML estimation. The initial parameters for the ML method are estimated by k-means clustering algorithm. The segmented image is used to produce a final restored document image. The techniques are tested on a set of Arabic historical manuscripts documents from the National Tunisian Library. The performance of the algorithm is demonstrated on by real color manuscripts distorted with show-through effects, uneven background color and localized spot.

This paper presents an efficient color blob segmentation concept, which combines an ordering relationship based on analyzing Bhattacharyya distances with a modified version of the Maximally Stable Extremal Region (MSER) detector. After definition of the region-of-interest by a one-time user input, connected regions are detected within the input image with low computational effort. Single image and video sequence analysis results are presented, which prove the applicability of the concept. Additionally, the possible extension to 3D segmentation is shown by an application that analyzes the 3D microstructure of a sheet of paper. Index Terms — Image Segmentation, Color 1.

based on regional content

Abstract. In this paper, we present a vision system for object recognition in aerial images, which enables broader mission profiles for Micro Air Vehicles (MAVs). The most important factors that inform our design choices are: real-time constraints, robustness to video noise, and complexity of object appearances. As such, we first propose the HSI color space and the Complex Wavelet Transform (CWT) as a set of sufficiently discriminating features. For each feature, we then build tree-structured belief networks (TSBNs) as our underlying statistical models of object appearances. To perform object recognition, we develop the novel multiscale Viterbi classification (MSVC) algorithm, as an improvement to multiscale Bayesian classification (MSBC). Next, we show how to globally optimize MSVC with respect to the feature set, using an adaptive feature selection algorithm. Finally, we discuss context-based object recognition, where visual contexts help to disambiguate the identity of an object despite the relative poverty of scene detail in flight images, and obviate the need for an exhaustive search of objects over various scales and locations in the image. Experimental results show that the proposed system achieves smaller classification error and fewer false positives than systems using the MSBC paradigm on challenging real-world test images. 1

Abstract: In this paper we propose a set of measures to model the concept of homogeneity in path-based image segmentation. We introduce the idea of path homogeneity as the aggregation of resemblances between consecutive pixels in the path. This resemblance is obtained from a measure of resemblance between neighbour pixels. In order to aggregate these resemblance values we propose the use of certain families of t-norms that verify a set of intuitive properties. We have studied the performance and behaviour of these functions through a set of experiments. Finally, we have applied these proposals to obtain fuzzy segmentations from real images.

We present a new method for the segmentation of color images for extracting information from terrestrial, aerial or satellite images. It is a supervised method for solving a part of the automatic extraction problem. The basic technique consists in fusing information coming from three different sources for the same image. The first source uses the information stored in each pixel, by means of the Mahalanobis distance. The second uses the multidimensional distribution of the three bands in a window centred in each pixel, using the Bhattacharyya distance. The last source also uses the Bhattacharyya distance, in this case coocurrence matrices are compared over the cube texture built around each pixel. Each source represent a different order of statistic. The Dempster- Shafer theory of evidence is applied in order to fuse the information from these three sources. This method shows the importance of applying context and textural properties for the extraction process. The results prove the potential of the method for real images starting from the three RGB bands only. Finally, some examples about the extraction of linear cartographic features, specially roads, are shown. 1.