Description:
Distributed video coding (DVC) aims at achieving low-complexity encoding in contrast to the existing video coding standards' high complexity encoding. According to the Wyner-Ziv theorem this can be achieved, under certain conditions, by independent encoding of the frames while resorting to joint decoding. However, the performance of a Wyner-Ziv coding scheme significantly depends on its knowledge about the spatio-temporal correlation of the video. Unfortunately, correlation statistics in a video widely varies both along the spatial and temporal directions. Therefore, we argue that in a feedback free transform domain DVC scheme the decoder will fail to recover all the transform coefficients with a nonzero probability. Thus, we suggest to integrate a recovery method with the decoder that aims at recovering the undecoded coefficients by exploiting the spatio-temporal correlation of the video. Besides, we extend and modify a recovery scheme, recently proposed in the context of images, for DVC so that it exploits both spatial and temporal correlations in recovering the undecoded coefficients. The essential idea of this scheme is to formulate the recovery problem as a linear optimization problem which can be solved efficiently using linear programming. Our simulation results demonstrated that the proposed scheme can significantly improve the PSNR and visual quality of the erroneous video frames produced by a DVC decoder.
Description:
Color image fusion of visible and infra-red imagery can play an important role in multi-sensor night vision systems that are an integral part of modern warfare. Image fusion minimizes the amount of required bandwidth by transmitting the fused image rather than multiple sensor images. Color image fusion can be achieved by combining inputs from original colored sensors or by employing pseudo colorization and color transfer to grayscale images. Various quality measures have been proposed for multi-sensor grayscale image fusion techniques; but no appropriate quality measure has been devised for the quality evaluation of multi-sensor color image fusion. In this paper, we propose a novel color image fusion quality measure, Color Fusion Objective Index (CFOI) based on colorfulness, gradient similarity and mutual information techniques. Experimental results show the effectiveness of CFOI to evaluate the color and salient feature extraction introduced by color fusion techniques into the final fused imagery as well as its consistency with subjective evaluation.
Description:
The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
Description:
The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like \emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a \emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
Description:
Contextual information is important for interpreting human actions especially when actions exhibit interactive relationship with their context. Contextual clues become even more crucial when videos are captured in unfavorable conditions like extreme low light nighttime scenarios. These conditions encourage the use of multi-senor imagery and context enhancement. In this paper, we explore the importance of contextual knowledge for recognizing human actions in multi-sensor nighttime videos. Information fusion is utilized for encapsulating visual information about actions and their context. Space-time action information is contained using 3D fourier transform of fused action silhouette volume. In parallel, SIFT context images are extracted and fused using principal component analysis based feature fusion for each action class. Contextual dissimilarity is penalized by minimizing context SIFT flow energy. The action dataset comprises multi-sensor night vision video data from infra-red and visible spectrum. Experimental results show that fused contextual action information boost action recognition performance as compared to the baseline action recognition approac
Description:
Yang proposed the concept of borrow-and-return (BR) to leverage the unused server bandwidth when a group of popular videos being broadcast with the FSFC (first segment on the first channel) broadcasting schemes in order to improve the mean waiting time (MWT) of the viewers with the help of additional receiving bandwidth available at the high-end clients. The BR model borrows the bandwidth of the videos with no new-coming viewers during a timeslot to speed up the transmission of the first segments of some of the remaining videos. In this paper, we first address the relative advantage issue among various possible BR schemes by developing a parametric generic BR (GBR) scheme controlled externally by independent borrow parameters. Later, we propose a new BR (NBR) model by incorporating an efficient transmission strategy to reduce the MWT further. Finally, an optimal NBR scheme is developed by augmenting with the optimal borrow parameters, which significantly outperforms the existing and new BR schemes in terms of overall MWT.
Description:
Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin micro-textures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
Description:
Proceedings - International Computer Software and Applications Conference
Description:
Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin micro-textures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
Description:
Proceedings - International Computer Software and Applications Conference
Description:
Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.
Description:
Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.
Description:
Video coding technique with a background frame, extracted from mixture of Gaussian (MoG) based background modeling, provides better rate distortion performance by exploiting coding efficiency in uncovered background areas compared to the latest video coding standard. However, it suffers from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we present a novel adaptive weighted non-parametric (WNP) background modeling technique and successfully embed it into HEVC video coding standard. Being non-parametric (NP), the proposed technique naturally exhibits superior performance in dynamic background scenarios compared to MoG-based technique without a priori knowledge of video data distribution. In addition, the WNP technique significantly reduces noise-related drawbacks of existing NP techniques to provide better quality video coding with much lower computation time as demonstrated through extensive comparative studies against NP, MoG and HEVC techniques.
Description:
Video coding technique with a background frame, extracted from mixture of Gaussian (MoG) based background modeling, provides better rate distortion performance by exploiting coding efficiency in uncovered background areas compared to the latest video coding standard. However, it suffers from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we present a novel adaptive weighted non-parametric (WNP) background modeling technique and successfully embed it into HEVC video coding standard. Being non-parametric (NP), the proposed technique naturally exhibits superior performance in dynamic background scenarios compared to MoG-based technique without a priori knowledge of video data distribution. In addition, the WNP technique significantly reduces noise-related drawbacks of existing NP techniques to provide better quality video coding with much lower computation time as demonstrated through extensive comparative studies against NP, MoG and HEVC techniques.
Description:
Video coding techniques utilising background frames, provide better rate distortion performance by exploiting coding efficiency in uncovered background areas compared to the latest video coding standard. Parametric approaches such as the mixture of Gaussian (MoG) based background modeling has been widely used however they require prior knowledge about the test videos for parameter estimation. Recently introduced non-parametric (NP) based background modeling techniques successfully improved video coding performance through a HEVC integrated coding scheme. The inherent nature of the NP technique naturally exhibits superior performance in dynamic background scenarios compared to the MoG based technique without a priori knowledge of video data distribution. Although NP based coding schemes showed promising coding performances, they suffer from a number of key challenges - (a) determination of the optimal subset of training frames for generating a suitable background that can be used as a reference frame during coding, (b) incorporating dynamic changes in the background effectively after the initial background frame is generated, (c) managing frequent scene change leading to performance degradation, and (d) optimizing coding quality ratio between an I-frame and other frames under bit rate constraints. In this study we develop a new scene adaptive coding scheme using the NP based technique, capable of solving the current challenges by incorporating a new continuously updating background generation process. Extensive experimental results are also provided to validate the effectiveness of the new scheme.
Description:
Video coding techniques utilising background frames, provide better rate distortion performance by exploiting coding efficiency in uncovered background areas compared to the latest video coding standard. Parametric approaches such as the mixture of Gaussian (MoG) based background modeling has been widely used however they require prior knowledge about the test videos for parameter estimation. Recently introduced non-parametric (NP) based background modeling techniques successfully improved video coding performance through a HEVC integrated coding scheme. The inherent nature of the NP technique naturally exhibits superior performance in dynamic background scenarios compared to the MoG based technique without a priori knowledge of video data distribution. Although NP based coding schemes showed promising coding performances, they suffer from a number of key challenges - (a) determination of the optimal subset of training frames for generating a suitable background that can be used as a reference frame during coding, (b) incorporating dynamic changes in the background effectively after the initial background frame is generated, (c) managing frequent scene change leading to performance degradation, and (d) optimizing coding quality ratio between an I-frame and other frames under bit rate constraints. In this study we develop a new scene adaptive coding scheme using the NP based technique, capable of solving the current challenges by incorporating a new continuously updating background generation process. Extensive experimental results are also provided to validate the effectiveness of the new scheme.
Description:
Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
Description:
Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
Description:
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
Description:
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
Description:
In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.
Description:
With the pragmatic realization of computing as a utility, Cloud Computing has recently emerged as a highly successful alternative IT paradigm. Cloud providers are deploying large-scale data centers across the globe to meet the Cloud customers’ compute, storage, and network resource demands. Efficiency and scalability of these data centers, as well as the performance of the hosted applications’ highly depend on the allocations of the data center resources. Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the relevant background information and a detailed taxonomy that characterizes and classifies the various components of VM placement and migration techniques, as well as an elaborate survey and comparative analysis of the state of the art techniques. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms proposed by the research community, the survey further identifies the benefits and limitations of the existing techniques and discusses on the future research directions.
Description:
With immense success and rapid growth within the past few years, cloud computing has been established as the dominant paradigm of IT industry. To meet the increasing demand of computing and storage resources, infrastructure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Intelligent workload placement and relocation is one of the primary means to address these issues. This chapter presents an overview of the infrastructure resource management systems and technologies and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastages. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described.
Description:
In this paper, we discuss the problems associated with the present contention resolution mechanism of IEEE 802.11 DCF and present a new, dynamic and robust approach to improve it. Our new MAC, using OFDMA in the physical layer, can incorporate multiple concurrent transmissions or receptions in a dynamic manner and can adjust the collision probability based on the traffic load when nodes are endowed with a single half-duplex radio only. Simulation results show that our system improves throughput by up to 40 percent, reduces collision in control messages by up to 80 percent and reduces the average delay for data transmission by up to 20 percent.
Description:
In this paper, we discuss the challenges associated with integrating multiuser OFDMA in a single cell IEEE 802.11 based wireless ad hoc network and propose a new, dynamic and robust approach to improve it. Our new MAC, using OFDMA in the physical layer, can incorporate multiple concurrent transmissions or receptions in a dynamic manner and can adjust the collision probability based on the traffic load when nodes are endowed with a single half-duplex radio only. Simulation results show that for moderate number of users, our system improves throughput by up to 20%, decreases collision in control messages by up to 45% and reduces the average delay by up to 18%.
Description:
Multiuser dynamic OFDMA based IEEE 802.11 distributed coordination function (DCF) has received significant interest from the researchers in recent time. Though several proposals have been made, to the best of our knowledge, none of these have presented an analytical model for this kind of medium access control protocols for IEEE 802.11. This paper provides a simple, nevertheless, very accurate analytical model to estimate the performance characteristics of IEEE 802.11 DCF with OFDMA under the assumptions of ideal channel conditions and saturation load. Our model accounts for important system parameters like throughput, collision rate, transmission delay, average contention window size, average retry count and average time wasted in backoff. Analytical results are verified through extensive simulations.
Description:
This paper addresses the problem of stabilizing underwater videos with non-uniform geometric deformations or warping due to a wavy water surface. It presents an improved method to correct these geometric deformations of the frames, providing a high-quality stabilized video output. For this purpose, a non-rigid image registration technique is employed to accurately align the warped frames with respect to a prototype frame and to estimate the deformation parameters, which in turn, are applied in an image dewarping technique. The prototype frame is chosen from the video sequence based on a sharpness assessment. The effectiveness of the proposed method is validated by applying it on both synthetic and real- world sequences using various quality metrics. A performance comparison with an existing method confirms the higher efficacy of the proposed method.