6927574
A Charged Active Contour Based on Electrostatics
We propose a novel active contour model by incorporating particle based electrostatic interactions into the geometric active contour framework. The proposed active contour, embedded in level sets, propagates under the joint influence of a boundary attraction force and a boundary competition force. Unlike other contour models, the proposed vector field dynamically adapts by updating itself when a contour reaches a boundary. The model is then more invariant to initialisation and possesses better convergence abilities. Analytical and comparative results are presented on synthetic and real images.
7804224
Level-set based geometric colour snake with region support
A novel method is introduced to force a geometric-based snake be more tolerant towards weak edges and noise in images. The method integrates gradient flow forces with region constraints obtained from diffused region segmentation forces. The diffusion is obtained from the region map vector flow field. This extra region force gives the snake a global view of the boundary information within the image. We present results on both graylevel and colour images.
12048322
Inspecting Colour Tonality on Textured Surfaces
We present a multidimensional histogram method to inspect tonality on colour textured surfaces, e.g. ceramic tiles. Comparison in the noise dominated chromatic channels is error prone. We perform vector- ordered colour smoothing and generate a PCA-based reconstruction of a query tile based on a reference tile eigenspace. Histograms of local feature vectors are then compared for tonality defect detection. The proposed method is compared and evaluated on a data set with groundtruth.
10241359
Localising surface defects in random colour textures using multiscale texem analysis in image eigenchannels
A novel method is presented to detect defects in random colour textures which requires only a very few normal samples for unsupervised training. We decorrelate the colour image by generating three eigenchannels in each of which the surface texture image is divided into overlapping patches of various sizes. Then, a mixture model and EM is applied to reduce groupings of patches to a small number of textural exemplars, or texems. Localised defect detection is achieved by comparing the learned texems to patches in the unseen image eigenchannels.
1308804
Texture Exemplars for Defect Detection on Random Textures
We present a new approach to detecting defects in random textures which requires only very few defect free samples for unsupervised training. Each product image is divided into overlapping patches of various sizes. Then, density mixture models are applied to reduce groupings of patches to a number of textural exemplars, referred to here as texems, characterising the means and covariances of whole sets of image patches. The texems can be viewed as implicit representations of textural primitives. A multiscale approach is used to save computational costs. Finally, we perform novelty detection by applying the lower bound of normal samples likelihoods on the multiscale defect map of an image to localise defects.
18031163
Browsing large pictures under limited display sizes
Pictures have become increasingly common and popular in mobile communications. However, due to the limitation of mobile devices, there is a need to develop new technologies to facilitate the browsing of large pictures on the small screen. In this paper, we propose a set of novel approaches which are able to aid or automate common image browsing tasks on mobile devices. All of these approaches are based on an image attention model which is employed to illustrate the information structure within an image. An efficient algorithm to generate the optimal browsing path based on the information foraging theory is presented. Experimental evaluations of the proposed mechanism indicate that our approach is an effective way for viewing large images on small displays
14354903
A visual attention model for adapting images on small displays
Abstract.Image adaptation, one of the essential problems in adaptive content delivery for universal access, has been actively explored for some time. Most existing approaches have focused on generic adaptation with a view to saving file size under constraints in client environment and have hardly paid attention to user perceptions of the adapted result. Meanwhile, the major limitation on the user’s delivery context is moving away from data volume (or time-to-wait) to screen size because of the galloping development of hardware technologies. In this paper, we propose a novel method for adapting images based on user attention. A generic and extensible image attention model is introduced based on three attributes (region of interest, attention value, and minimal perceptible size) associated with each attention object. A set of automatic modeling methods are presented to support this approach. A branch-and-bound algorithm is also developed to find the optimal adaptation efficiently. Experimental results demonstrate the usefulness of the proposed scheme and its potential application in the future.
37379077
Improving Web Browsing on Small Devices Based on Table Classification
Browsing large web pages on small screens is still very inconvenient due to their limited display sizes. A straightforward solution is eliminating the annoying horizontal scrolling requirement, i.e. present all the contents into a single narrow column, usually named one-column view. It is implemented by deleting the layout that will cause horizontal scrolling. However, after deleting the layout structure, some structural information becomes unreadable, like data tables. In this paper, we propose an approach to improve web browsing on small devices by classifying HTML tables into data tables and layout tables. For data tables, we try to preserve the original structural information in the final presentation. Therefore, the browsing experience can be improved for the one-column view. Experimental results show that our approach can achieve a satisfactory performance.
12125361
Hybrid index structures for location-based web search
There is more and more commercial and research interest in location-based web search, i.e. finding web content whose topic is related to a particular place or region. In this type of search, location information should be indexed as well as text information. However, the index of conventional text search engine is set-oriented, while location information is two-dimensional and in Euclidean space. This brings new research problems on how to efficiently represent the location attributes of web pages and how to combine two types of indexes. In this paper, we propose to use a hybrid index structure, which integrates inverted files and R*-trees, to handle both textual and location aware queries. Three different combining schemes are studied: (1) inverted file and R*-tree double index, (2) first inverted file then R*-tree, (3) first R*-tree then inverted file. To validate the performance of proposed index structures, we design and implement a complete location-based web search engine which mainly consists of four parts: (1) an extractor which detects geographical scopes of web pages and represents geographical scopes as multiple MBRs based on geographical coordinates; (2) an indexer which builds hybrid index structures to integrate text and location information; (3) a ranker which ranks results by geographical relevance as well as non-geographical relevance; (4) an interface which is friendly for users to input location-based search queries and to obtain geographical and textual relevant results. Experiments on large real-world web dataset show that both the second and the third structures are superior in query time and the second is slightly better than the third. Additionally, indexes based on R*-trees are proven to be more efficient than indexes based on grid structures.
12946117
Searching One Billion Web Images by Content: Challenges and Opportunities
Although content-based image retrieval has been studied for decades, most commercial image search engines are still text-based. However, there is a growing demand for techniques to support content-based image search at Web scale. In this paper, we propose an ambitious goal to searching one billion Web images by content, and discuss the major challenges and opportunities. We also present several important applications that can be greatly benefited by techniques to enable Web-scale image search by content. These applications include image copyright infringement detection, street-side photo search, and searchbased image annotation. We believe that the insights presented in the paper are enlightening to researchers in this field, and any breakthrough we make in this space will lead to many impactful applications in the future.