nils-herrmann/clean_scirepeval huggingface dataset:
The Influence of the analysis technique on estimating liver iron overload using magnetic resonance imaging T2∗ quantification

Iron toxicity is the major cause of tissue damage in patients with iron overload. Iron deposits mainly in the liver, where its concentration closely correlates with whole body iron overload. Different techniques have been proposed for estimating iron content, with liver biopsy being the gold standard despite its invasiveness and influence by sampling error. Recently, magnetic resonance imaging (MRI) has been established as an effective technique for evaluating iron overload by measuring T2* in the liver. However, various factors associated with the adopted analysis technique, mainly the exponential fitting model and signal averaging method, affect the resulting measurements. In this study, we evaluate the influences of these factors on T2* measurement in numerical phantom, calibrated phantoms, and nine patients with different degrees of iron overload. The results show different performances among the fitting models and signal averaging methods, which are affected by SNR, image quality and signal homogeneity inside the selected ROI for analysis.
Accurate analysis of cardiac tagged MRI using combined HARP and optical flow tracking

In this work, we present a new method for analyzing cardiac tagged Magnetic Resonance Imaging (tMRI). The method combines two major tracking techniques: Harmonic Phase (HARP) and Optical Flow (OF). The results of the two techniques are fused together to accurately estimate the displacement of each myocardium point. The developed methods were tested using numerical MRI phantom at different SNR levels and deformation rates. The results show that the proposed method is more accurate and reliable than the HARP and the OF methods.
Secure transcoding for compressive multimedia sensing

Compressive sensing (CS) has recently attracted much attention due to its unique feature of directly and simultaneously acquiring compressed and encrypted data based on their sparse or compressible properties. To securely transmit compressively sensed multimedia data over networks, it is required to support transcoder to securely convert compressed multimedia into several different types for diverse receivers. In this paper, a secure transcoding scheme for compressive multimedia sensing is proposed. We focus on securely converting compressively sensed multimedia data (not data compressed via standard codec) with a certain number of measurements into other different numbers of measurements without resorting to reconstruct the original data. We show that the security can be achieved via transforming multimedia re-sensing process into another secure domain at the transcoder. We also show that the computational security can be achieved while transmitting compressively sensed data between the sender (or each receiver) and the transcoder over networks.
Sensor localization via robust principle component analysis

In the received signal strength (RSS)-based localization problem, in spite of their different formulations, most of the existing methods minimize the measurement errors between sensor nodes, leading to the error diffusion problem. In this paper, aiming at solving this problem, we adopt a fundamentally different optimization strategy to propose a novel localization method exploiting the invariant distance characteristic in the network. To our knowledge, the proposed method is the first one formally dealing with the error diffusion.
Fast vision-based surface inspection of defects for steel billets

Steel has played a leading role in the development of human civilization and technology. To achieve the goal of industry 4.0 for steel industry, intelligent automated manufacturing becomes increasingly important. One of the key steps on the automatic production line of steel products is automatic inspection of surface detects. In this paper, we propose a fast vision-based surface inspection framework for defects on continuous casting steel billets. The proposed method relies only on simple pixel-domain vision-based operations to achieve fast and accurate inspection of surface defects. Experimental results have demonstrated that our method efficiently achieves good inspection accuracy on corner and sponge cracks on steel billets.
Self-learning-based signal decomposition for multimedia applications: A review and comparative study

Decomposition of a signal (e.g., image or video) into multiple semantic components has been an effective research topic for various image/video processing applications, such as image/video denoising, enhancement, and inpainting. In this paper, we present a survey of signal decomposition frameworks based on the uses of sparsity and morphological diversity in signal mixtures and its applications in multimedia. First, we analyze existing MCA (morphological component analysis) based image decomposition frameworks with their applications and explore the potential limitations of these approaches for image denoising. Then, we discuss our recently proposed self-learning based image decomposition framework with its applications to several image/video denoising tasks, including single image rain streak removal, denoising, deblocking, joint super-resolution and deblocking for a highly compressed image/video. By advancing sparse representation and morphological diversity of image signals, the proposed framework first learns an over-complete dictionary from the high frequency part of an input image for reconstruction purposes. An unsupervised or supervised clustering technique is applied to the dictionary atoms for identifying the morphological component corresponding to the noise pattern of interest (e.g., rain streaks, blocking artifacts, or Gaussian noises). Different from prior learning-based approaches, our method does not need to collect training data in advance and no image priors are required. Our experimental results have confirmed the effectiveness and robustness of the proposed framework, which has been shown to outperform state-of-the-art approaches.
Low-overhead content-adaptive spatial scalability for scalable video coding

To support spatial scalability, the scalable extension of H.264/AVC (SVC) uses video cropping or uniform scaling to downscale the original higher-resolution (HR) sequence to a lower resolution (LR) one. Both operations, however, will cause critical visual information loss in the retargeted frames. The content-adaptive spatial scalability SVC coders (CASS-SVC) use non-homogeneous scaling to avoid critical information loss, which, however, requires to send additional side information to signal the decoder, thereby degrading coding efficiency significantly. To address the problem, we propose a low-overhead CASS-SVC coder consisting of three main modules: a mosaic-guided video retargeter, a side-information coder, and a non-homogeneous inter-layer predictive coder. Our experimental results demonstrate that, compared to existing CASS-SVC coders, our method cannot only well preserve subjective quality of important content in the LR sequence, but also significantly improves the coding efficiency of HR sequence.
Near-duplicate subsequence matching for video streams

In this paper, we study the efficiency problem of near-duplicate subsequence matching for video streams. A simple but effective algorithm called incremental similarity update is proposed to address the problem. A similarity upper bound between two videos can be calculated incrementally by taking a lightweight computation to filter out the unnecessary time-consuming computation for the actual similarity between two videos. We integrate the algorithm with inverted frame indexing to scan video sequences for matching near-duplicate subsequences. Four state-of-the-art methods are implemented for comparison in terms of the accuracy, execution time, and memory consumption. Experimental results demonstrate the proposed algorithm yields comparable accuracy, compact memory size, and more efficient execution time.
3D modeling for steel billet images

To achieve the goal of Industry 4.0 for steel industry, intelligent automated manufacturing becomes increasingly important. One of the key steps on the automatic production line of steel products is automatic inspection of surface detects. Recent trends in vision-based surface inspections of steel products have shown that the use of 3D information would be more beneficial than only utilizing two dimensional surface data. In this paper, a 3D modeling framework is proposed to capture 3D information of steel billet images. Our major goal is to obtain the depth information for the billet images to be inspected for possibly further applications (e.g., inspection of surface defects). The presented results have shown the feasibility of the proposed framework.
Multi-View Distributed Video Coding with Low-Complexity Inter-Sensor Communication Over Wireless Video Sensor Networks

To meet the requirements of resource-limited video sensors, low-complexity video encoding technique is highly desired. In this paper, a low-complexity multi-view distributed video encoding scheme by using the correlations among video frames from adjacent video sensor nodes (VSNs) via robust media hashing at encoder and the global motion parameters estimated and fed back from the decoder is proposed. The frames from adjacent VSNs are warped into the same view-direction based on the global motion parameters. Then, the significant differences between the warped key frame and the non-key frame from adjacent VSNs are efficiently extracted based on robust media hashing for non-key frame compression. The key is that few data (hash information) exchanges among adjacent VSNs are allowed to efficiently exploit the correlations among VSNs. The coding performance and energy consumption of the proposed encoder have been verified through simulations and comparisons with existing low-complexity video encoders.
Cross-view action recognition via low-rank based domain adaptation

Cross-view action recognition is a challenging problem, since one typically does not have sufficient training data at the target view of interest. With recent developments of domain adaptation, we propose a novel low-rank based domain adaptation model for mapping labeled data from the original source view to the target view, so that training and testing can be performed at that domain. Our model not only provides an effective way for associating image data across different domains, we further advocate the structural incoherence between transformed data of different categories. As a result, additional data discriminating ability is introduced to our domain adaptation model, and thus improved recognition can be expected. Experimental results on the IXMAS dataset verify the effectiveness of our proposed method, which is shown to outperform state-of-the-art domain adaptation approaches.
Fast image stitching for continuous casting steel billet images

To achieve the goal of industry 4.0 for steel industry, intelligent automated manufacturing becomes increasingly important. One of the key steps on the automatic production line of steel products is automatic inspection of surface detects. In this paper, we consider the problem of stitching continuous casting steel billet images captured from billet production line. It has been shown that integrated individually captured continuous billet images would be beneficial to vision-based surface inspection. This paper presents a fast image stitching framework for continuous billet images relying on homography estimation. Experimental results have demonstrated that our method achieves fast image stitching with accurately stitched results.
A new error resilient coding scheme for JPEG image transmission based on data embedding and vector quantization

For an entropy-coded Joint Photographic Experts Group (JPEG) image, a transmission error in a codeword will not only affect the underlying codeword but also may affect subsequent codewords, resulting in a great degradation of the received image. In this study, a new error resilient coding scheme for JPEG image transmission based on data embedding and side-match vector quantization (VQ) is proposed. To cope with the synchronization problem, the restart capability of JPEG images is enabled. The objective of the proposed scheme is to recover high-quality JPEG images from the corresponding corrupted images. At the encoder, the important data (the codebook index) for each Y (U or V) block in a JPEG image are extracted and embedded into another "masking" Y (U or V) block in the image by the odd-even data embedding scheme. At the decoder, after all the corrupted blocks within a JPEG image are detected and located, if the important data for a corrupted block can be extracted correctly from the corresponding "masking" block, the extracted important data will be used to conceal, the corrupted block; otherwise, the side-match VQ technique is employed to conceal the corrupted block. Based on the simulation results, the performance of the proposed scheme is better than that of five existing approaches for comparison. The proposed scheme can recover high-quality JPEG images from the corresponding corrupted images up to a block loss rate of 30%.
Visual Depth Guided Color Image Rain Streaks Removal Using Sparse Coding

Rain removal from a single color image is a challenging problem as no temporal information among successive images can be obtained. In this paper, we propose a single-color-image-based rain removal framework by properly formulating rain removal as an image decomposition problem based on sparse representation. In our framework, an input color image is first decomposed into a low-frequency part and a high-frequency part by using the guided image filter so that the rain streaks would be in the high-frequency part with nonrain textures/edges, and the high-frequency part is then decomposed into a rain component and a nonrain component by performing dictionary learning and sparse coding. To separate rain streaks from the high-frequency part, a hybrid feature set, including histogram of oriented gradients, depth of field, and Eigen color, is employed to further decompose the high-frequency part. With the hybrid feature set applied, most rain streaks can be removed; simultaneously nonrain component can be enhanced. To the best of our knowledge, compared with the state-of-the-art approaches, the proposed method is among the first to focus on the problem of single color image rain removal and achieves promising results with not only the rain component being removed more completely, but also the visual quality of restored images being improved.
Vision-Based Fall Detection through Shape Features

A major cause of deaths among the elderly relates to accidental falls. Such falls are of particular medical concern to this population because they often result in severe injuries, since senior citizens usually live alone and cannot ask for help when accidents happen. In this paper, we propose a fall detection system with the help of a Gaussian mixture background model to build the background before motion history image (MHI) is applied to analyze the fall behavior. Finally, two extra features, acceleration and angular acceleration, are proposed to more accurately determine whether a fall event has happened.
Power-rate-distortion model for low-complexity video coding

Wireless visual sensor networks are potentially applicable for several emerging applications. Since the resource-limited restriction for a visual sensor node (VSN), efficient resource allocation for video compression is challenging. In this paper, a power-rate-distortion (PRD) model-based low-complexity multiview video codec is proposed. Our PRD model is used to characterize the relationship between the available resources and the RD performance of our video codec. More specifically, an RD function in terms of the percentages for different coding modes of blocks and the target bit rate under the available resource constraints is derived for optimal coding mode decision. Analytic and simulation results are provided to verify the resource scalability and accuracy of our PRD model in facilitating our low-complexity multiview video codec to achieve good performance.
Feature-Based Sparse Representation for Image Similarity Assessment

Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. To evaluate the applicability of the proposed feature-based sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems. Promising results have been obtained through simulations conducted on several public datasets, including the Stirmark benchmark, Corel-1000, COIL-20, COIL-100, and Caltech-101 datasets.
Joint Fingerprinting and Decryption for VQ Images through Bipartite Matching

The legality of redistribution of digital content over the Internet has become critical in commerce. The goal of the traitor tracing problem is to find out the traitor who illegally distributes the digital content. In this paper, a joint fingerprinting and decryption (JFD) scheme based on vector quantization and bipartite matching for traitor tracing is proposed. Before transmission, the proposed method encrypts the VQ compressed image through bipartite graphs. After the receiver performs decryption and fingerprinting, the recover image containing user's fingerprint is only slightly different from the original one and can be used for traitor tracing. The experimental results show that the encrypted image possesses unintelligibility and the recovered image has desirable image quality greatly related to the codebook size.
Two error resilient coding schemes for wavelet-based image transmission based on data embedding and genetic algorithms

For an entropy-coded wavelet-based image, such as a JPEG-2000 image, a transmission error in a codeword will not only affects the underlying codeword but also may affect subsequent codewords, resulting in a great degradation of the received image. In this study, two error resilient coding schemes for wavelet-based image transmission based on data embedding and genetic algorithms (GAs) are proposed. In this study, as JPEG-2000, an image is decomposed into 6 wavelet levels (levels 0-5). For level 0, an error resilient coding scheme based on data embedding and side-match vector quantization is employed. For level 1, the proposed basic scheme uses the embedded important data for a corrupted code block to conceal the corrupted code block, whereas the proposed enhanced scheme uses a GA-based error concealment scheme to conceal the corrupted code block. For level 2, the embedded important data for a corrupted code block are used to conceal the corrupted code block. For levels 3-5, the wavelet coefficients of each corrupted code block are simply replaced by zeros. Based on the simulation results obtained in this study, the performances of the two proposed schemes are better than that of the three existing approaches for comparison. The proposed schemes can recover high-quality wavelet-based JPEG-2000 images from the corresponding corrupted images up to a bit error rate of 0.5%.
Fast deconvolution-based image super-resolution using gradient prior

Single-image super-resolution (SR) is to reconstruct a high-resolution image from a low-resolution input image. Nevertheless, most SR algorithms are performed in an iterative manner and are therefore time-consuming. In this paper, we propose an iteration-free single-image SR algorithm based on fast deconvolution with gradient prior. Based on the prior calculated from the initially upsampled image via current approach (e.g., bicubic interpolation or example/learning-based approaches), we make the deconvolution process well-posed, which can be efficiently solved in FFT domain. Moreover, the proposed algorithm can be directly applied to video SR, where the temporal coherence can be automatically maintained. Experimental results demonstrate that the proposed method can simultaneously obtain significant acceleration and quality improvement over several existing SR methods.
Low-Complexity Wyner-Ziv Video Coding Based on Robust Media Hashing

To meet the requirement of distributed video coding in resource-limited sensor networks, Wyner-Ziv theorem-based source coding with side information available at the decoder states that an intraframe encoder with interframe decoder system can achieve comparable coding efficiency of a conventional video codec. Most existing Wyner-Ziv video coding systems are with light encoder and heavy decoder. In this paper, a new content-aware media hash-based Wyner-Ziv video codec with light encoder and light decoder is proposed. The key is that the significant differences between a video frame and its reference frame are efficiently extracted and used for frame recovery based on robust image hashing without needing to perform motion estimation. The particular contribution of our method is its low complexity in both the encoder and decoder sides. Simulation results demonstrate the achievable coding efficiency of our method in particular for videos with small and middle motions
Vision-Based Automatic Identification Tracking of Steel Products for Intelligent Steel Manufacturing

To achieve intelligent manufacturing in Industry 4.0 for steel industry, this paper considers a smart steel manufacturing framework, where an automatic identification tracking method for steel products is proposed. Automatically online tracking and identifying steel products on a production line is essential for intelligent manufacturing management since those products would be frequently moved and processed at different locations on the product flow. Current approaches usually rely on embedding or marking a series of characters (or identification codes) on the steel surface. However, steel-making process is usually conducted under a high temperature environment, making it difficult to well embed the identification codes with high quality and further automatically well recognize them online. To tackle the problem, this paper presents a novel vision-based automatic identification tracking framework without needing to embed any identification codes into the steel product. The key is to employ the essential identity of a steel product without any extrinsic information embedded, achieved by extracting visual features from the product. The presented preliminary results have verified the efficiency of the proposed method, which will be further improved in the future.
Abandoned object detection in complicated environments

In video surveillance, tracking-based approaches are very popular especially for detecting abandoned objects in public areas. Once the object has been tracked, the object status can be further classified as removed or abandoned. However, some shortcomings were found on tracking-based approaches, e.g. illumination changes and occlusion. Therefore, in this paper, an alternative approach to detect abandoned objects is proposed by incorporating background modeling and Markov model. In addition the shadow removal is employed to rectify detected objects and obtain more accurate results. The experimental results show that the proposed scheme is better than other methods in terms of accuracy and correctness.