Shot Cut Test Based on Mathematics Parameter and Chain Code

2013 ◽  
Vol 712-715 ◽  
pp. 2399-2402
Author(s):  
Jian Feng Wang ◽  
Xiao Rong Zhao
Keyword(s):  
Abrupt Change ◽  
Feature Vector ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Chain Code ◽  
Inverse Transform ◽  
Dct Domain ◽  
Dct Coefficients ◽  
Cut Detection

In this paper, An effective shot cut detection algorithm directly in DCT domain was proposed. This algorithm(including abrupt change and ) test the n -1 frame and n frame through the extracting the feature vector of each frame. When extract the features of frame, two-dimensional mathematics parameter m1-s from DCT coefficients without its inverse transform and 8 directional chain code was computed, Divide m1-s space into 30 unequal partitions (subspaces) and compute the numbers pr within the 30 subspaces (entries) as the feature vector to judge the different of the two frames. locating shot cuts is operated by comparison tests.

Efficient Scene Change Algorithm Based on Statistical Feature in the DCT Domain

2011 ◽  
Vol 204-210 ◽  
pp. 229-233
Author(s):  
Jian Feng Wang ◽  
Jian Min Jiang
Keyword(s):  
Processing Speed ◽  
Abrupt Change ◽  
Feature Vector ◽  
Detection Algorithm ◽  
Compressed Domain ◽  
Scene Change ◽  
Scene Change Detection ◽  
Statistical Feature ◽  
Dct Domain ◽  
Dct Coefficients

In this paper, we propose an effective scene change detection algorithm directly in compressed domain. The proposed scene change algorithm(including abrupt change and ) test the n frame and n+1 frame through the extracting the feature of each frame. When extract the features of frame, two-dimensional statistical feature m1-s from DCT coefficients without its inverse transform was computed, Divide m1-s space into 42 unequal partitions (subspaces) and count the numbers pr within the 42 subspaces (entries) as the feature vector to judge the different of the two frames. locating scene changes is operated by comparison tests. In comparison with existing representative techniques, the experimental results show the superiority of the proposed method in terms of precision and processing speed.

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

ENCRYPTION-DECRYPTION TECHNIQUES FOR PICTURES

1989 ◽  
Vol 03 (03n04) ◽  
pp. 497-503
Author(s):  
RANI SIROMONEY ◽  
K. G. SUBRAMANIAN ◽  
P. J. ABISHA
Keyword(s):  
Public Key ◽  
Two Dimensional ◽  
Chain Code ◽  
Dimensional Array ◽  
Public Key Cryptosystems ◽  
Encryption Decryption ◽  
A Chain

Language theoretic public key cryptosystems for strings and pictures are discussed. Two methods of constructing public key cryptosystems for the safe transmission or storage of chain code pictures are presented; the first one encrypts a chain code picture as a string and the second one as a two-dimensional array.

A DCT-Domain System for Digital Watermarking

2013 ◽  
Vol 321-324 ◽  
pp. 2609-2612
Author(s):  
Yan Liang ◽  
Gao Yan ◽  
Chun Xia Qi
Keyword(s):  
Signal Processing ◽  
Digital Watermarking ◽  
Copyright Protection ◽  
Random Sequence ◽  
Multimedia Data ◽  
Dct Domain ◽  
Digital Document ◽  
Dct Coefficients ◽  
Processing Techniques ◽  
Signal Processing Techniques

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associated to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new DCT-domain system for digital watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of scrambled image in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, cut, fuzzy, addition of noise, and sharpen.

scholarly journals Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Yingfeng Cai ◽  
Youguo He ◽  
Hai Wang ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Training Methods ◽  
Two Dimensional ◽  
Data Set ◽  
Deep Model ◽  
Unsupervised Training ◽  
Weakly Supervised

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

scholarly journals A New Approach to Reducing the Distortion of the Digital Image Natural Model in the DCT Domain When Embedding Information According to the QIM Method

2019 ◽  
Author(s):  
Олег Евсютин ◽  
Oleg Evsutin ◽  
Анна Мельман ◽  
Anna Melman ◽  
Роман Мещеряков ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Digital Content ◽  
The Internet ◽  
Embedding Capacity ◽  
New Approach ◽  
Additional Information ◽  
Dct Domain ◽  
Dct Coefficients ◽  
Information Confidentiality

One of the areas of digital image processing is the steganographic embedding of additional information into them. Digital steganography methods are used to ensure the information confidentiality, as well as to track the distribution of digital content on the Internet. Main indicators of the steganographic embedding effectiveness are invisibility to the human eye, characterized by the PSNR metric, and embedding capacity. However, even with full visual stealth of embedding, its presence may produce a distortion of the digital image natural model in the frequency domain. The article presents a new approach to reducing the distortion of the digital image natural model in the field of discrete cosine transform (DCT) when embedding information using the classical QIM method. The results of the experiments show that the proposed approach allows reducing the distortion of the histograms of the distribution of DCT coefficients, and thereby eliminating the unmasking signs of embedding.

Generating New Space-Filling Test Instances for Continuous Black-Box Optimization

2020 ◽  
Vol 28 (3) ◽  
pp. 379-404
Author(s):  
Mario A. Muñoz ◽  
Kate Smith-Miles
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Evolutionary Process ◽  
Black Box ◽  
Two Dimensional ◽  
Entire Space ◽  
Test Functions ◽  
New Space ◽  
Instance Space ◽  
Different Characteristics

This article presents a method to generate diverse and challenging new test instances for continuous black-box optimization. Each instance is represented as a feature vector of exploratory landscape analysis measures. By projecting the features into a two-dimensional instance space, the location of existing test instances can be visualized, and their similarities and differences revealed. New instances are generated through genetic programming which evolves functions with controllable characteristics. Convergence to selected target points in the instance space is used to drive the evolutionary process, such that the new instances span the entire space more comprehensively. We demonstrate the method by generating two-dimensional functions to visualize its success, and ten-dimensional functions to test its scalability. We show that the method can recreate existing test functions when target points are co-located with existing functions, and can generate new functions with entirely different characteristics when target points are located in empty regions of the instance space. Moreover, we test the effectiveness of three state-of-the-art algorithms on the new set of instances. The results demonstrate that the new set is not only more diverse than a well-known benchmark set, but also more challenging for the tested algorithms. Hence, the method opens up a new avenue for developing test instances with controllable characteristics, necessary to expose the strengths and weaknesses of algorithms, and drive algorithm development.

New Feature Vector from Freeman Chain Code for Handwritten Roman Character Recognition

2018 ◽  
Author(s):  
Dewi Nasien ◽  
Deni Yulianti ◽  
Fakhrul Syakirin Omar ◽  
M. Hasmil Adiya ◽  
Yenny Desnelita ◽  
...  
Keyword(s):  
Character Recognition ◽  
Feature Vector ◽  
Chain Code ◽  
New Feature
Sign in / Sign up

Export Citation Format

Share Document