Automatic spectral video matting

This paper presents a modified spectral matting to obtain automatic and accurate image matting. Spectral matting is the state-of-the-art image matting and also a milestone in theoretic matting research. However, using spectral matting without user guides, the accuracy is usually low. The proposed modified spectral matting effectively raises the accuracy. In the proposed modified spectral matting, the palette-based component classification is proposed to obtain the reliable foreground and background components. In contrast to the spectral matting, based on these reliable foreground and background components, the accuracy of obtained alpha matte is greatly increased. Experimental results show that the proposed method has better performance than the spectral matting. Therefore, the proposed image matting is very suitable for image and video editing.

In this paper, we are concerned with unsupervised natural image matting. Due to the under-constrained nature of the problem, image matting algorithms are usually provided with user interactions, such as scribbles or trimaps. This is a very tedious task and may even become impractical for some applications. For unsupervised matte calculation, we can either adopt a technique that supports an unsupervised mode for alpha map calculation, or we may automate the process of acquiring user interactions provided for a matting algorithm. Our proposed technique contributes to both approaches and is based on spectral matting. The latter is the only technique in the literature that supports automatic matting but it suffers from critical limitations among which is the unreliable unsupervised operation. Stressing on that drawback, spectral matting may produce erroneous mattes in the absence of guiding scribbles or trimaps. Using the Gestalt laws of grouping, we propose a method that automatically produces more truthful mattes than spectral matting. In addition, it can be used to generate trimaps, eliminating the required user interactions and making it possible to harness the powers of matting techniques that are better than spectral matting but don't support unsupervised operation. The main contribution of this research is the introduction of the Gestalt laws of grouping to the matting problem.

Video matting, or layer extraction, is a classic inverse problem in computer vision that involves the extraction of foreground objects, and the alpha mattes that describe their opacity, from a set of images. Modem approaches that work with natural backgrounds often require user-labelled "trimaps" that segment each image into foreground, background and unknown regions. For long sequences, the production of accurate trimaps can be time consuming. In contrast, another class of approach depends on automatic background extraction to automate the process, but existing techniques do not make use of spatiotemporal consistency, and cannot take account of operator hints such as trimaps. This paper presents a method inspired by natural image statistics that cleanly unifies these approaches. A prior is learnt that models the relationship between the spatiotemporal gradients in the image sequence and those in the alpha mattes. This is used in combination with a learnt foreground colour model and a prior on the alpha distribution to help regularize the solution and greatly improve the automatic performance of such systems. The system is applied to several real image sequences that demonstrate the advantage that the unified approach can afford.

We present a new approach to simulate depth-of-field effect with non-local means filtering in an unsupervised fashion which requires no user input at all. Unlike traditional rendering methods which handle the depth-of-field effect in 3D scenes, our novel approach handles the depth-of-field effect in 2D scenes. Our proposed approach mainly consists of two steps: first we extract the depth of field (DoF) region from the background in the form of alpha matte, which based on saliency detection technique and spectral matting. Then we blur the background with non-local means depth filtering, which is heavily used in image de-noising. We demonstrate our approach can precisely and efficiently simulate depth-of-field effect of images without prior knowledge of their content.

Machine learning algorithms have become increasingly prevalent in multiple\ndomains, such as autonomous driving, healthcare, and finance. In such domains,\ndata preparation remains a significant challenge in developing accurate models,\nrequiring significant expertise and time investment to search the huge search\nspace of well-suited data curation and transformation tools. To address this\nchallenge, we present AutoCure, a novel and configuration-free data curation\npipeline that improves the quality of tabular data. Unlike traditional data\ncuration methods, AutoCure synthetically enhances the density of the clean data\nfraction through an adaptive ensemble-based error detection method and a data\naugmentation module. In practice, AutoCure can be integrated with open source\ntools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of\nmachine learning. As a proof of concept, we provide a comparative evaluation of\nAutoCure against 28 combinations of traditional data curation tools,\ndemonstrating superior performance and predictive accuracy without user\nintervention. Our evaluation shows that AutoCure is an effective approach to\nautomating data preparation and improving the accuracy of machine learning\nmodels.\n

Suspension operation reliability is one of the most significant performance indexes that concerns maneuvering stability and drive safety. In this paper, in order to guarantee good suspension reliability, an adaptive fault detection and isolation scheme is proposed for quarter-car active suspension systems with parametric and nonlinear uncertainties. To realize fault diagnosis for active suspensions, an adaptive fault detection estimator and several fault isolation estimators are designed to generate state residuals. Corresponding adaptive thresholds are developed to help judge the occurrence and type of possible faults. In the process of constructing state estimators, the uncertain parameter is updated online so that good sensitivity of fault detection can be achieved. Illustrative simulation is carried out to validate the effectiveness of the fault diagnosis scheme, and results indicate that the proposed method is sensible to sudden faults and maintain robustness to model uncertainties appearing in suspension systems.

The multiuser detection is a signal processing tech- nique which enhances the performance and capacity in DS- CDMA technology. Since its complexity issue was too expensive to handle, there was focus on the suboptimal detectors. In this paper we propose a hybrid method called adaptive differential interference cancellation method (ADIC) which is a hybrid of the differential interference cancellation method and which is then combined in a adaptive way to determine a weight matrix which will map the outcome of the differential interference cancellation method to come out with a better performance. This paper also looks into the computational cost of different methods using the floating point operation (flops) which is a good estimation of the comparative computational cost if not the exact one. I. INTRODUCTION The conventional DS-CDMA match filter detector fails to combat these issues due three prominent drawbacks: unrealis- tic ucorrelated code, near-far problem (even with strict power control (1)), and tough multiple access interference (MAI). Many advanced signal processing approaches have been made to improve the performances of these kinds of detectors. In this paper we will study the different approaches that were made in this field and implement these methods to reach a conclusion about the detection methods. As the name of the topic suggests the focus of our work will be study and propose cost effective better performing multiuser detection method. We will explore Multiuser Detection Techniques in Synchronous CDMA environment. Multi-user detection deals with the development and ap- plication of joint demodulation and interference cancellation cancellation techniques for improved detection of a desired set of digital signals in the presence of multiple access interfer- ence, intersymbol interference and noise. Since veterbi forward dynamic algorithm (2) is used in case of Optimum multiuser detection (3) the complexity grows in with the increase in the number of users. Hence the complexity is immense which increases exponentially with the increase in the number of users. Looking ahead for lower complexity hence lesser cost of implementation, the paper proposes the use of sub-optimal linear detectors (4). The sub-optimal detector the paper next focuses on is Decorrelator type of Detector (5) which is optimal in least squares. This detector out performs the optimal detector in many respects. Then the Minimum Mean Square Error (MMSE) linear detector is proposed which tries to minimize the squared error from the required transformation. It is one of the most popular detectors in use due to its easiness to implement and its lower complexity. But then in the above detection methods the decisions made were based on certain linear transformation criterion and there are missing efforts to check the performance in between the detection process to improvise the transformation method better detection. So then the paper proposes Adaptive multiuser detection. Then the paper focuses on Interference cancellation methods which utilize the feedback to reduce Multiple Access Interference (MAI) in the detected bits. Under the Interference Cancellation umbrella the Successive Interference Cancellation (SIC) (6), (7) method is explored and simulated to observe the results. Then the next method which performs better than SIC and more commonly used in real life, called Parallel Interference Cancellation (PIC) (6), (8) is also implemented. Before going ahead to implement the final proposed detection method, this paper implements Differential Interference Cancellation (DIC) (9) method. And then this paper finally proposes an Inter- ference Cancellation method called the Adaptive Differential Interference Cancellation Multiuser Detection method which is cost effective in comparison to other Interference Cancellation methods as we will require lesser hardware (multipliers as in the SIC or Parallel Interference Cancellation (PIC)) for that matter. More ever since we are using adaptive method the desired BER can be further reduced. This proposed method will be discussed in detail in the following section.

Strain-based functional indices have been shown to provide superior performance in assessing global cardiac function when compared with classical volume-based metrics, such as ejection fraction. As in clinical practice global strain is typically used as an index of overall cardiac performance, local tracking algorithms, which are typically computationally intensive, could be substituted by more global approaches. We therefore propose to take advantage of a fast tracking method which uses an optical-flow algorithm on an anatomical ROI to estimate the global cardiac (affine) motion between consecutive frames. The proposed approach was tested in 19 RT3DE exams by assessing the global area strain (GAS), which combines both longitudinal and circumferential deformation. The agreement between the automatic tracking results and the reference show moderate correlation (r=0.698), while Bland-Altman analysis ([μ±1.96σ]=6.0±9.29%) revealed a significant bias, although having competitive limits of agreement when compared with the inter-observer variability ([μ±1.96σ]=0.85±9.52%). The proposed approach takes less than 1 second to perform the tracking between two subsequent frames, in a MATLAB implementation. These preliminary results point towards the feasibility of online estimation of global deformation parameters without any user intervention and near real-time.

For improving the processing speed and accuracy of edge detection, an adaptive edge detection method based on improved NMS (nonmaximum suppression) was proposed in this paper. In the method, the gradient image was computed by four directional Sobel operators. Then, the gradient image was processed by using NMS method. By defining a power map function, the elements values of gradient image histogram were mapped into a wider value range. By calculating the maximal between-class variance according to the mapped histogram, the corresponding threshold was obtained as adaptive threshold value in edge detection. Finally, to be convenient for engineering application, the proposed method was realized in FPGA (Field Programmable Gate Array). The experiment results demonstrated that the proposed method was effective in edge detection and suitable for real-time application.

To accelerate T1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE. To reconstruct T1ρ images from accelerated acquisition at different time of spin-lock (TSLs), we propose an approach to combine an advanced compressed sensing (CS) based reconstruction technique, LAISD (locally adaptive iterative support detection), and an advanced parallel imaging technique, JSENSE. Specifically, the reconstruction process alternates iteratively among local support detection in the domain of principal component analysis, compressed sensing reconstruction of the image sequence, and sensitivity estimation with JSENSE. T1ρ quantification results from accelerated scans using the proposed method are evaluated using in vivo knee cartilage data from bilateral scans of three healthy volunteers. T1ρ maps obtained from accelerated scans (acceleration factors of 3 and 3.5) using the proposed method showed results comparable to conventional full scans. The T1ρ errors in all compartments are below 1%, which is well below the in vivo reproducibility of cartilage T1ρ reported from previous studies. The proposed method can significantly accelerate the acquisition process of T1ρ quantification on human cartilage imaging without sacrificing accuracy, which will greatly facilitate the clinical translation of quantitative cartilage MRI.

Stripe noise is usually introduced into infrared images during acquisition, which seriously deteriorates the image quality and affects the subsequent analysis of the image. In this paper, the spatial mathematic model and the comb-like impulse spectrum characteristics in Fourier domain of stripe noise are given. On this basis, a local adaptive threshold noise detection linear interpolation filter (LALIF) for stripe noise removal in infrared images is designed. In the filter method, the first, we crop the original image into two sub-images with optimal size to make the comb-like impulse spectrum characteristics of the stripe noise most prominent. The second, we use a local adaptive threshold to distinguish noise frequency points from the useful frequency points in the peak regions of the amplitude spectrum, and reassign the value of noise frequency points by linear interpolation. At last, we amalgamate the sub-images to obtain the stripe noise removal image. Experimental results indicate that the proposed method has a good performance in adaptability and stability for complex stripe noise removal in infrared images.

In this paper, an adaptive change detection method for polarization synthetic aperture radar (Pol-SAR) images change detection in low SNR circumstances is presented. It is difficult to detect the changed targets in strong noise or interference circumstance. Original coherent change detection method is invalid, because of many of false-alarm targets appear. Firstly, we analyze the distribution characteristic of coherence parameters. And then, we find that a kind of coherence parameters of Pol-SAR images can be proved following Gaussian distribution. Finally, an adaptive judgment criterion was used to detect the change targets. The experiment shows that the proposed method has a better detection performance.

Driver's characteristics have an essential influence on the performance improvement of advanced driver assistance systems (ADAS), which is also a key issue that needs to be solved in the research and development of on-board systems. This paper proposed an adaptive driver fatigue identification method based on the Hidden Markov Model (HMM) to reduce the influence of different drivers on fatigue detection. The model herein was designed based on the fatigue dynamic generation characteristics and individual characteristics. Based on driver classification, the fatigue identification model database was developed, which provided a basis for the design of the adaptive fatigue detection model. Finally, an adaptive fatigue detection method based on maximum likelihood estimation is proposed. Test results show that the proposed method can effectively improve the adaptability of the detection system, and the fatigue recognition accuracy has more tremendous advantages.

Multiuser Detection (MUD) and Channel Estimation techniques in Space-Division Multiple Access aided Orthogonal Frequency Division Multiplexing (SDMA-OFDM) systems recently received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibit poor performance even though it achieves lower computational complexity. In this paper, Hybrid ML-MMSE adaptive multiuser detection based on joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method has good performance close to optimal ML performance at low SNR values and a low computational complexity at high SNR values.

This paper proposes a scalable and credible watermarking algorithm towards Scalable Video Coding (SVC) which aims to build Copyright Protection System (CPS). Firstly, we investigate where to embed watermark to ensure it can be detected in base layer as well as enhancement layer and what does wavelet Just Noticeable Distortion (JND) for video consist of, then a model that combines frequency masking, contrast masking, luminance adaption and temporal masking has been proposed. Finally whether watermark exists or not can be judged by adaptive detection which guarantees the proposed method has a good legal credibility, since its False Alarm Rate (FAR) closes to zero. The scalability and credibility of the proposed method is examined on JSVM (the H.264/MPEG-4 AVC scalable video extension) platform.