393 research outputs found

    Content Authentication and Forge Detection using Perceptual Hash for Image Database

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    Popularity of digital technology is very high. Number of digital images are being created and stored every day. This introduces a problem for managing image databases and security of images. One cannot determine if an image already exists in a database without exhaustively searching through all the entries. Further complication arises from the fact that two images appearing identical to the human eye may have distinct digital representations, making it difficult to compare a pair of images. Also the security of database server is questionable. The proposed framework provides the content authentication and forges detection of image. This can be done by generating perceptual image hash using SIFT algorithm, Perceptual image hash also known as perceptual image signature. It has been proposed as a primitive method to solve problems of image content authentication. The perceptual image hash is generated by using the perceptual features that are in accordance with human’s visual characteristics. It allows tampering of images to permissible extent e.g. improving slight brightness or contrast in image. A perceptual image hash is expected to be able to survive unintentional distortion and reject malicious tampering within an acceptable extend .Therefore it provides a more efficient approach to analyzing changes of image perceptual content and make sure database server is authenticated or not

    Robust image hashing using ring partition-PGNMF and local features

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    Automatic visual recognition using parallel machines

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    Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

    Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis

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    We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution -- but complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct fine-scale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data

    A Review of Hashing based Image Copy Detection Techniques

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    Images are considered to be natural carriers of information, and a large number of images are created, exchanged and are made available online. Apart from creating new images, the availability of number of duplicate copies of images is a critical problem. Hashing based image copy detection techniques are a promising alternative to address this problem. In this approach, a hash is constructed by using a set of unique features extracted from the image for identification. This article provides a comprehensive review of the state-of-the-art image hashing techniques. The reviewed techniques are categorized by the mechanism used and compared across a set of functional & performance parameters. The article finally highlights the current issues faced by such systems and possible future directions to motivate further research work

    ImMesh: An Immediate LiDAR Localization and Meshing Framework

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    In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh

    スマート農業のための信頼できるドキュメンテーションシステム

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    九州工業大学博士学位論文 学位記番号:情工博甲第373号 学位授与年月日:令和4年12月27日1 Introduction|2 Traditional Documentation|3 Blockchain-based Trust Management|4 Adopting Blockchain Method for Cocoa Farming Documentation|5 Implementation of Blockchain Concept into the Real Problem through a Simulation Case of Cocoa Production|6 Conclusion and Future Work九州工業大学令和4年
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