5,439 research outputs found

    Stereo Imaging Camera Model for 3D Shape Reconstruction of Complex Crystals and Estimation of Facet Growth Kinetics

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    The principle that the 3D shape of crystals that grow from a solution can be characterised in real-time using stereo imaging has been demonstrated previously. It uses the 2D images of a crystal that are obtained from two or more cameras arranged in defined angles as well as a mathematical reconstruction algorithm. Here attention is given to the development of a new and more robust 3D shape reconstruction method for complicated crystal structures. The proposed stereo imaging camera model for 3D crystal shape reconstruction firstly rotates a digitised crystal in the three-dimensional space and varies the size dimensions in all face directions. At each size and orientation, 2D projections of the crystal, according to the angles between the 2D cameras, are recorded. The contour information of the 2D images is processed to calculate Fourier descriptors and radius-based signature that are stored in a database. When the stereo imaging instrument mounted on a crystalliser captures 2D images, the images are segmented to obtain the contour information and processed to obtain Fourier descriptors and radius-based information. The calculated Fourier descriptors and radius-based signature are used to find the best matching in the database. The corresponding 3D crystal shape is thus found. Potash alum crystals that each has 26 habit faces were used as a case study. The result shows that the new approach for 3D shape reconstruction is more accurate and significantly robust than previous methods. In addition, the growth rates of {111}, {110} and {100} faces were correlated with relative supersaturation to derive models of facet growth kinetics

    A Novel Scheme for Intelligent Recognition of Pornographic Images

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    Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works

    Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis

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    The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists in two steps. In the first, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. In a second moment, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach.Comment: Chaos, Volume 21, Issue 4, 201

    Multi-Technique Fusion for Shape-Based Image Retrieval

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    Content-based image retrieval (CBIR) is still in its early stages, although several attempts have been made to solve or minimize challenges associated with it. CBIR techniques use such visual contents as color, texture, and shape to represent and index images. Of these, shapes contain richer information than color or texture. However, retrieval based on shape contents remains more difficult than that based on color or texture due to the diversity of shapes and the natural occurrence of shape transformations such as deformation, scaling and orientation. This thesis presents an approach for fusing several shape-based image retrieval techniques for the purpose of achieving reliable and accurate retrieval performance. An extensive investigation of notable existing shape descriptors is reported. Two new shape descriptors have been proposed as means to overcome limitations of current shape descriptors. The first descriptor is based on a novel shape signature that includes corner information in order to enhance the performance of shape retrieval techniques that use Fourier descriptors. The second descriptor is based on the curvature of the shape contour. This invariant descriptor takes an unconventional view of the curvature-scale-space map of a contour by treating it as a 2-D binary image. The descriptor is then derived from the 2-D Fourier transform of the 2-D binary image. This technique allows the descriptor to capture the detailed dynamics of the curvature of the shape and enhances the efficiency of the shape-matching process. Several experiments have been conducted in order to compare the proposed descriptors with several notable descriptors. The new descriptors not only speed up the online matching process, but also lead to improved retrieval accuracy. The complexity and variety of the content of real images make it impossible for a particular choice of descriptor to be effective for all types of images. Therefore, a data- fusion formulation based on a team consensus approach is proposed as a means of achieving high accuracy performance. In this approach a select set of retrieval techniques form a team. Members of the team exchange information so as to complement each other’s assessment of a database image candidate as a match to query images. Several experiments have been conducted based on the MPEG-7 contour-shape databases; the results demonstrate that the performance of the proposed fusion scheme is superior to that achieved by any technique individually

    Analysing the importance of different visual feature coefficients

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    A study is presented to determine the relative importance of different visual features for speech recognition which includes pixel-based, model-based, contour-based and physical features. Analysis to determine the discriminability of features is per- formed through F-ratio and J-measures for both static and tem- poral derivatives, the results of which were found to correlate highly with speech recognition accuracy (r = 0.97). Princi- pal component analysis is then used to combine all visual fea- tures into a single feature vector, of which further analysis is performed on the resulting basis functions. An optimal feature vector is obtained which outperforms the best individual feature (AAM) with 93.5 % word accuracy
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