scholarly journals An Innovative Approach for Denoising of the Digital Video Stream

2021 ◽  
Vol 23 (07) ◽  
pp. 342-351
Author(s):  
Anand B. Deshmukh ◽  
◽  
Dr. Sanjay V. Dudul ◽  
Keyword(s):  
Medical Imaging ◽  
Video Processing ◽  
Weather Forecasting ◽  
Video Stream ◽  
Satellite Imaging ◽  
Video Signals ◽  
Video Denoising ◽  
Shot Detection ◽  
Processing Step ◽  
Content Preparation

Everyday tones of video signals are generated, transmitted, and analyzed. The video contents are created for educational purposes, entertainment purposes, surveillance purposes, medical imaging purposes, weather forecasting, satellite imaging, and many other significant places. During the different phases of video content preparation, transmission, and analysis some unwanted signals get interfered with the true contents. Particularly, the medical imaging signals, since they are weak signals, are more prone to unwanted interferences. Such unwanted interference of the noise signals makes it difficult to analyze the critical information in the video contents and hence, the need for denoising process arises. A decent video denoising framework assures visual improvement in the video signals or it serves as the significant pre-processing step in the video processing steps like compression and analysis. Through this paper, we are about to disclose an efficient video denoising framework that takes the noisy video signal in the form of frames per second and performs the video denoising using shot detection, compensation, intensity calculations, and motion estimation process.

scholarly journals Vehicle Detection and Classification from High Resolution Satellite Images

2014 ◽  
Vol II-1 ◽  
pp. 1-8 ◽  
Author(s):  
L. Abraham ◽  
M. Sasikumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Vehicle Detection ◽  
Satellite Imaging ◽  
Suitable Alternative ◽  
High Resolution Satellite Images ◽  
Technological Developments ◽  
High Resolution Satellite Imagery

In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

Image Features from Morphological Scale-Spaces

2011 ◽  
pp. 32-79
Author(s):  
Sébastien Lefèvre
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Video Processing ◽  
State Of The Art ◽  
Image Features ◽  
Video Data ◽  
Multimedia Data ◽  
Multimedia Data Mining ◽  
Shot Detection ◽  
Scale Spaces

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

scholarly journals Design and Implementation of the HD Video Signal Converter Based on FPGA

2012 ◽  
Vol 6-7 ◽  
pp. 571-575
Author(s):  
Ling Fan Wu ◽  
Li Jun Yun ◽  
Jun Sheng Shi ◽  
Kun Wang ◽  
Zhi Hui Deng
Keyword(s):  
Video Processing ◽  
Digital Video ◽  
Video Signal ◽  
High Definition ◽  
Standard Definition ◽  
Video Signals ◽  
Design And Implementation ◽  
Design Requirements ◽  
Signal Converter ◽  
Actual Test

In this paper, based on the FPGA and with a video dedicated A / D converter chip, LVDS coding chip, the design and implementation of a SD(standard-definition) analog video signals to HD(high-definition) digital video signal converter. First, input SD analog video into digital video signals meet the ITU-BT656 standard. Then use the FPGA with the video processing chip and DDR do some corresponding processing to achieve high-definition digital video output. After the actual test, the converter output signal of the image quality is well, meets the design requirements, and to verify the effectiveness of the program.

scholarly journals Object Detection and Tracking in Real Time Video Stream

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
N. Jayanti ◽  
Keyword(s):  
Real Time ◽  
Forest Fires ◽  
Video Processing ◽  
Fire Detection ◽  
Video Stream ◽  
Image Subtraction ◽  
Color Recognition ◽  
Detection And Tracking ◽  
Video Feed ◽  
Fully Automatic

To achieve fully automatic surveillance of some specific color objects, an intelligent real-time detection method based on video processing is proposed. The main aim of this paper is to identify the colors and use them to achieve their applications. The proposed algorithm is used to detect a specific color and also to track it in the live video feed which could be eventually used for many different applications like surveillance cameras, fire detection in cases of forest fires, etc. For the color recognition part, several stages such as image subtraction, noise filtering, binary image, and blob extraction are used to recognize a specific color in the video feed. Then the corresponding pixels on the GUI are drawn to track where all the color has been. This might find application in various areas; one such area in which this has been used often is in the detection of forest fires.

scholarly journals METHODS OF IMAGE RECOGNITION IN A VIDEO STREAM

2021 ◽  
Vol 8 (1) ◽  
pp. 042-049
Author(s):  
D. I. Ivanov ◽  
Keyword(s):  
Video Processing ◽  
Moving Objects ◽  
Video Stream ◽  
Multiple Instance Learning ◽  
Learning Tools ◽  
Tracking Accuracy ◽  
Correlation Filters ◽  
Advantages And Disadvantages ◽  
Kernelized Correlation Filters ◽  
Tracking Objects

The article examines the problem of automatic object recognition using a video stream as a digital image. Algorithms for recognizing and tracking objects in the video stream are considered, methods used in video processing are analyzed, and the use of machine learning tools in working with video is described.The main approaches to solving the problem of recognizing moving objects in a video stream are investigated: the detection-based approach and the tracking-based approach. Arguments are made in favor of the tracking-based approach, and, in addition, modern methods of tracking objects in the video stream are considered. In particular, the algorhythms: Online Boosting Tracker - one of the first object tracking algorithms with high tracking accuracy, MIL Tracker (Multiple Instance Learning Tracker), which is a development of the idea of learning with a teacher and the Online Boosting algorithm and the KCF Tracker algorithm (Kernelized Correlation Filters Tracker) - a method that uses the mathematical properties of overlapping areas of positive examples.As a result, the advantages and disadvantages of the considered methods and algorithms for recognizing and tracking objects for various applications are highlighted.

Fast Video Shot Boundary Detection Technique based on Stochastic Model

2016 ◽  
Vol 6 (2) ◽  
pp. 1-17
Author(s):  
Mohammad A. Al-Jarrah ◽  
Faruq A. Al-Omari
Keyword(s):  
Stochastic Model ◽  
Video Processing ◽  
High Performance ◽  
Boundary Detection ◽  
Video Stream ◽  
Detection Algorithm ◽  
Shot Boundary Detection ◽  
Different Types ◽  
Video Shot ◽  
Shot Boundary

A video is composed of set of shots, where shot is defined as a sequence of consecutive frames captured by one camera without interruption. In video shot transition could be a prompt (hard cut) or gradual (fade, dissolve, and wipe). Shot boundary detection is an essential component of video processing. These boundaries are utilized on many aspect of video processing such as video indexing, and video in demand. In this paper, the authors proposed a new shot boundary detection algorithm. The proposed algorithm detects all type of shot boundaries in a high accuracy. The algorithm is developed based on a global stochastic model for video stream. The proposed stochastic model utilizes the joined characteristic function and consequently the joined momentum to model the video stream. The proposed algorithm is implemented and tested against different types of categorized videos. The proposed algorithm detects cuts fades, dissolves, and wipes transitions. Experimental results show that the algorithm has high performance. The computed precision and recall rates validated its performance.

scholarly journals Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6429
Author(s):  
Liqun Lin ◽  
Jing Yang ◽  
Zheng Wang ◽  
Liping Zhou ◽  
Weiling Chen ◽  
...  
Keyword(s):  
Saliency Detection ◽  
Video Quality ◽  
Video Stream ◽  
Support Vector ◽  
Compressed Video ◽  
Artifact Detection ◽  
Video Signals ◽  
Reference Information ◽  
Video Saliency ◽  
Video Quality Metric

Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.

Reducing False Alarms in Vision-Based Fire Detection

2017 ◽  
pp. 263-290
Author(s):  
Neethidevan Veerapathiran ◽  
Anand S.
Keyword(s):  
Video Processing ◽  
Detection System ◽  
Chemical Properties ◽  
Fire Detection ◽  
False Alarms ◽  
Video Signals ◽  
Detection Systems ◽  
Physical Sensors ◽  
Single Feature ◽  
Fire Detection System

Computer vision techniques are mainly used now a days to detect the fire. There are also many challenges in trying whether the region detected as fire is actually a fire this is perhaps mainly because the color of fire can range from red yellow to almost white. So fire region cannot be detected only by a single feature and many other features (i.e.) color have to be taken into consideration. Early warning and instantaneous responses are the preventing ideas to avoid losses affecting environment as well as human causalities. Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms. In order to reduce false alarms of conventional fire detection systems, system make use of vision based fire detection system. This chapter discuss about the fundamentals of videos, various issues in processing video signals, various algorithms for video processing using vision techniques.

scholarly journals Component-based software for dynamic configuration and control of computer assisted intervention systems

2011 ◽  
Author(s):  
Peter Kazanzides ◽  
Min Yang Jung ◽  
Anton Deguet ◽  
Balazs Vagvolgyi ◽  
Marcin Balicki ◽  
...  
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Vitreoretinal Surgery ◽  
Video Stream ◽  
Computer Assisted ◽  
Dynamic Configuration ◽  
Interactive Control ◽  
Multiple Processes ◽  
Source Level ◽  
And Control

This paper presents the rationale for the use of a component-based architecture for computer-assisted intervention (CAI) systems, including the ability to reuse components and to easily develop distributed systems. We introduce three additional capabilities, however, that we believe are especially important for research and development of CAI systems. The first is the ability to deploy components among different processes (as conventionally done) or within the same process (for optimal real-time performance), without requiring source-level modifications to the component. This is particularly relevant for real-time video processing, where the use of multiple processes could cause perceptible delays in the video stream. The second key feature is the ability to dynamically reconfigure the system. In a system composed of multiple processes on multiple computers, this allows one process to be restarted (e.g., after correcting a problem) and reconnected to the rest of the system, which is more convenient than restarting the entire distributed application and enables better fault recovery. The third key feature is the availability of run-time tools for data collection, interactive control, and introspection, and offline tools for data analysis and playback. The above features are provided by the open-source cisst software package, which forms the basis for the Surgical Assistant Workstation (SAW) framework. A complex computer-assisted intervention system for retinal microsurgery is presented as an example that relies on these features. This system integrates robotics, stereo microscopy, force sensing, and optical coherence tomography (OCT) imaging to transcend the current limitations of vitreoretinal surgery.

Video Representation and Processing for Multimedia Data Mining

2011 ◽  
pp. 1-31 ◽  
Author(s):  
Amr Ahmed
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Video Processing ◽  
State Of The Art ◽  
Detection Performance ◽  
Video Data ◽  
Multimedia Data ◽  
Multimedia Data Mining ◽  
Video Representation ◽  
Shot Detection

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

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