In this paper, a new robust multiple description image coding method with a modified interleaving sampling and a modified interpolation method using block compressed sensing is proposed. In the encoding process, the original image is decomposed into several sub-images by using the modified interleaving sampling and the redundant bits are added to enhance the reconstruction accuracy. For each sub-image the description is obtained in the block compressed sensing (BCS). In the decoding process, the signal is reconstructed from the sparse measurements by using the optimization algorithm. Our analysis and simulation results showed that the proposed method is a balanced multiple description coding scheme with higher accuracy of reconstruction and higher efficiency of coding.
In general, visual trackers employ hand-crafted feature descriptors to track the object, which limits their performance. In this paper, a novel Restricted Boltzmann Machine based Tracker (RBMT) is proposed to enhance the robustness. RBMs are introduced to learn multiple feature descriptors for the different image cues which are transformed from the given images. A data augment method is introduced to online train the RBMs so as to make the learnt feature descriptors specific for different tracked objects. To make the proposed tracker adapted to drastic varying scenes, a feature selection method is also developed to fuse the multiple cues in feature level for the design of appearance-based classifiers. Our experimental results have shown that the proposed tracker can obtain promising performances compared with the other state-of-the-art approaches.
In general, visual trackers employ hand-crafted feature descriptors to track the object, which limits their performance. In this paper, a novel Restricted Boltzmann Machine based Tracker (RBMT) is proposed to enhance the robustness. RBMs are introduced to learn multiple feature descriptors for the different image cues which are transformed from the given images. A data augment method is introduced to online train the RBMs so as to make the learnt feature descriptors specific for different tracked objects. To make the proposed tracker adapted to drastic varying scenes, a feature selection method is also developed to fuse the multiple cues in feature level for the design of appearance-based classifiers. Our experimental results have shown that the proposed tracker can obtain promising performances compared with the other state-of-the-art approaches.
KEYWORDS: Digital watermarking, Distortion, Feature extraction, Image compression, Signal processing, Image quality, Discrete wavelet transforms, Digital filtering, Linear filtering, Information security
Digital watermarking is an efficient technique for copyright protection in the current digital and network era. In this paper, a novel robust watermarking scheme is proposed based on singular value decomposition (SVD), Arnold scrambling (AS), scale invariant feature transform (SIFT) and majority voting mechanism (MVM). The watermark is embedded into each image block for three times in a novel way to enhance the robustness of the proposed watermarking scheme, while Arnold scrambling is utilized to improve the security of the proposed method. During the extraction procedure, SIFT feature points are used to detect and correct possibly geometrical attacks, and majority voting mechanism is performed to enhance the accuracy of the extracted watermark. Our analyses and experimental results demonstrate that the proposed watermarking scheme is not only robust to a wide range of common signal processing attacks (such as noise, compression and filtering attacks), but also has favorable resistance to geometrical attacks.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.