scholarly journals A Novel Approach towards Video Compression for Mobile Internet using Transform Domain Technique

2012 ◽  
Vol 58 (10) ◽  
pp. 29-33 ◽  
Author(s):  
Dhaval R.Bhojani ◽  
Ved Vyas Dwivedi
Keyword(s):  
Video Compression ◽  
Mobile Internet ◽  
Transform Domain ◽  
Novel Approach

scholarly journals Sparse Approach in Filtering of Color Images Corrupted by Mixture Noises

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Noise Suppression ◽  
Impulsive Noise ◽  
Median Filter ◽  
Color Images ◽  
Subjective Perception ◽  
Transform Domain ◽  
Second Stage ◽  
Novel Approach ◽  
Impulsive Noises ◽  
Impulsive Noise Suppression

A novel filtering approach is presented in denoising in the color images contaminated by mixture of additive-impulsive noises. Novel framework consists of three principal stages: impulsive noise suppression that is performed detecting pixels corrupted by impulsive noise and then, filtering found spikes by a variant of median filter; during second stage, original additive noise suppression filter is employed in Wavelet transform domain via a sparse representation and 3D-filtering; finally, nondesirable effects obtained in an image during previous stages are processed to correct fine details. In case of multiplicative noise suppression, the designed denoising scheme uses 3D homomorphic sparse processing stage and post-filtering procedure. Evaluation of novel approach in denoising complex distortions has been performed using objective criteria (PSNR and SSIM measures) and subjective perception via human visual system confirming their better performance in comparison with state-of-theart techniques.

scholarly journals KAT4IA: K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xingche Guo ◽  
Yumou Qiu ◽  
Dan Nettleton ◽  
Cheng-Ting Yeh ◽  
Zihao Zheng ◽  
...  
Keyword(s):  
Plant Traits ◽  
Semisupervised Learning ◽  
Training Data ◽  
Transform Domain ◽  
Neural Network Learning ◽  
Plant Trait ◽  
Novel Approach ◽  
Field Phenotyping ◽  
High Throughput Phenotyping ◽  
Rich Data

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.

scholarly journals Moving Object Detection for Video Surveillance

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
K. Kalirajan ◽  
M. Sudha
Keyword(s):  
Object Detection ◽  
Video Compression ◽  
Video Surveillance ◽  
Statistical Correlation ◽  
Object Localization ◽  
Feature Points ◽  
Video Frames ◽  
Novel Approach ◽  
Illumination Changes ◽  
Promising Solution

The emergence of video surveillance is the most promising solution for people living independently in their home. Recently several contributions for video surveillance have been proposed. However, a robust video surveillance algorithm is still a challenging task because of illumination changes, rapid variations in target appearance, similar nontarget objects in background, and occlusions. In this paper, a novel approach of object detection for video surveillance is presented. The proposed algorithm consists of various steps including video compression, object detection, and object localization. In video compression, the input video frames are compressed with the help of two-dimensional discrete cosine transform (2D DCT) to achieve less storage requirements. In object detection, key feature points are detected by computing the statistical correlation and the matching feature points are classified into foreground and background based on the Bayesian rule. Finally, the foreground feature points are localized in successive video frames by embedding the maximum likelihood feature points over the input video frames. Various frame based surveillance metrics are employed to evaluate the proposed approach. Experimental results and comparative study clearly depict the effectiveness of the proposed approach.

Accelerating Color Space Conversion Using CUDA-Enabled Graphic Processing Units

2013 ◽  
Vol 716 ◽  
pp. 505-509 ◽  
Author(s):  
Hang Jun Yang ◽  
Jian Wang ◽  
Xiao Yong Ji
Keyword(s):  
Video Compression ◽  
Video Processing ◽  
Multicore Processors ◽  
Color Space ◽  
Graphics Hardware ◽  
Functional Requirements ◽  
Graphic Processing Units ◽  
Color Space Conversion ◽  
Novel Approach ◽  
Software Implementations

Color space conversion (CSC) is an important kernel in the area of image and video processing applications including video compression. CSC is a compute-intensive time-consuming operation that consumes up to 40% of processing time of a highly optimised decoder. Several hardware and software implementations for CSC have been found. Hardware implementations can achieve a higher performance compared with software-only solutions. However, the flexibility of software solutions is desirable for various functional requirements and faster time to market. Multicore processors, especially programmable GPUs, provide an opportunity to increase the performance of CSC by exploiting data parallelism. In this paper, we present a novel approach for efficient implementation of color space conversion. The proposed approach has been implemented and verified using computed unified device architecture (CUDA) on graphics hardware. Our experiments results show that the speedup of up to17×can been obtained.

scholarly journals Analyzing Network Protocols of Application Layer Using Hidden Semi-Markov Model

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jun Cai ◽  
Jian-Zhen Luo ◽  
Fangyuan Lei
Keyword(s):  
Markov Model ◽  
Reverse Engineering ◽  
Rapid Development ◽  
Mobile Internet ◽  
High Rate ◽  
Propagation Mechanism ◽  
Internet Protocols ◽  
Novel Approach ◽  
Protocol Reverse Engineering ◽  
New Applications

With the rapid development of Internet, especially the mobile Internet, the new applications or network attacks emerge in a high rate in recent years. More and more traffic becomes unknown due to the lack of protocol specifications about the newly emerging applications. Automatic protocol reverse engineering is a promising solution for understanding this unknown traffic and recovering its protocol specification. One challenge of protocol reverse engineering is to determine the length of protocol keywords and message fields. Existing algorithms are designed to select the longest substrings as protocol keywords, which is an empirical way to decide the length of protocol keywords. In this paper, we propose a novel approach to determine the optimal length of protocol keywords and recover message formats of Internet protocols by maximizing the likelihood probability of message segmentation and keyword selection. A hidden semi-Markov model is presented to model the protocol message format. An affinity propagation mechanism based clustering technique is introduced to determine the message type. The proposed method is applied to identify network traffic and compare the results with existing algorithm.

Sign in / Sign up

Export Citation Format

Share Document