22043660
Colour tonality inspection using eigenspace features
In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. The image noise, introduced by the imaging system, is found mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparison in the noise dominated chromatic channels is error prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shade features, which results in a reference eigenspace. Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtruth.
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.
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.
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.
249149
RAGS: region-aided geometric snake
An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the region-aided geometric snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy 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.
31296346
Clustering-Based Navigation of Image Search Results on Mobile Devices
With the Internet information explosion as well as the rapid increasing of mobile users, web image search and browsing on mobile devices will become a big application in foreseeable future. Since mobile devices generally suffer from limited resources, like small screen factor and interaction facilities, etc, the interface provided by current image search engine is not suitable for search results navigation on mobile devices. In this paper, we propose a new method to increase user's experience on search results browsing and navigation. By clustering result images based on visual and semantic cues, user can catch the overview of the search results with only a few clicks; by using an optimal layout algorithm, the summary of each cluster can be presented on small screen with maximum scaling ratio; by employing the smart thumbnail of the presentation image, more information can be shown to users on limited screen. The navigation operations are designed to make the new interface easy to learn and simple to use. Experiment results demonstrate the effectiveness of the new presentation method.
42102591
Winner Determination in Discount Auctions
Discount auctions is a market mechanism for buying heterogeneous items in a single auction. The bidders are suppliers and a bid consists of individual cost for each of the items and a non-decreasing discount function defined over the number of items. The winner determination problem faced by the buyer is to determine the winning suppliers and their corresponding winning items. We show that this problem is ${\cal NP}$-hard upon reduction from the set covering problem. The problem has an embedded network structure, which is exploited to develop heuristics and an exact branch and bound algorithm. Computational experiments were performed to evaluate the proposed algorithms.
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.
5342027
Attention model based progressive image transmission
Progressive image transmission provides a convenient user interface when images are transmitted slowly. However, most of the existing PIT techniques only consider the objective quality of the reconstructed image. Here we present an attention model based progressive image transmission approach to improve the subjective quality of the transmission process. We use both bottom-up image features and top-down semantic information to extract the regions of interest and also propose a new ROI coding scheme based on JPEG2000 to control the trade-off between the transmission of ROI and background. Experiments have shown the efficiency of our approach
39255438
Design of progressive auctions for procurement based on Lagrangian relaxation
Auction mechanisms have been found successful in implementing e-procurement, with the buyer as the auctioneer and the sellers as the bidders. Single round auctions are prevalently used in e-procurement, in which the suppliers submit sealed bids in response to the buyer's request-for-quote. The bid evaluation problem faced by the buyer is an optimization problem that selects the winning bids to minimize the cost of procurement. Though these auction types are advantageous in many scenarios, progressive auctions (multiple rounds) are preferred in terms of information elicitation and bid preparation. This paper demonstrates the design of progressive auctions, from their single-round counterparts, using Lagrangian relaxation. In particular, we show that an appropriate algorithm for a judiciously selected Lagrangian relaxation of the bid evaluation problem, can help in designing a progressive auction. The procurement of multiple units of a single item is considered with linear and piecewise linear supply curves.