PROJECT TITLE :

Features Classification Forest: A Novel Development That Is Adaptable To Robust Blind Watermarking Techniques - 2017

ABSTRACT:

A completely unique watermarking theme is proposed that could substantially improve current watermarking techniques. This theme exploits the options of micro pictures of watermarks to create association rules and embeds the foundations into a bunch image rather than the bit stream of the watermark, which is usually employed in digital watermarking. Next, similar micro images with the same rules are collected or even created from the host image to simulate an extracted watermark. This methodology, known as the features classification forest, will achieve blind extraction and is adaptable to any watermarking theme employing a quantization-primarily based mechanism. Furthermore, a larger size watermark can be accepted while not an adverse impact on the imperceptibility of the host image. The experiments demonstrate the successful simulation of watermarks and the applying to 5 totally different watermarking schemes. One among them is slightly adjusted from a reference to particularly resist JPEG compression, and also the others show native blessings to resist totally different image processing attacks.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :New Automatic Modulation Classifier Using Cyclic-Spectrum Graphs With Optimal Training Features - 2018ABSTRACT:A new feature-extraction paradigm for graph-based automatic modulation classification is proposed in
PROJECT TITLE :Background Modeling by Stability of Adaptive Features in Complex Scenes - 2018ABSTRACT:The one-feature-primarily based background model typically fails in complicated scenes, since a pixel is better described by
PROJECT TITLE :Robust, Efficient Depth Reconstruction with Hierarchical Confidence-Based Matching - 2017ABSTRACT:In recent years, taking photos and capturing videos with mobile devices became increasingly standard. Emerging applications
PROJECT TITLE : Blind image quality assessment based on Multichannel features fusion and label transfer - 2016 ABSTRACT: In this paper, we have a tendency to propose an efficient blind image quality assessment (BIQA) algorithm,
PROJECT TITLE : continuously adaptive data fusion and model relearning for particle filter tracking with multiple features - 2016 ABSTRACT: This paper presents a brand new technique for object tracking in an exceedingly camera

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry