nils-herrmann/clean_scirepeval huggingface dataset:
Fully Automatic Three-Dimensional Ultrasound Imaging Based on Conventional B-Scan

Robotic ultrasound systems have turned into clinical use over the past few decades, increasing precision and quality of medical operations. In this paper, we propose a fully automatic scanning system for three-dimensional (3-D) ultrasound imaging. A depth camera was first used to obtain the depth data and color data of the tissue surface. Based on the depth image, the 3-D contour of the tissue was rendered and the scan path of ultrasound probe was automatically planned. Following the scan path, a 3-D translating device drove the probe to move on the tissue surface. Simultaneously, the B-scans and their positional information were recorded for subsequent volume reconstruction. In order to stop the scanning process when the pressure on the skin exceeded a preset threshold, two force sensors were attached to the front side of the probe for force measurement. In vitro and in vivo experiments were conducted for assessing the performance of the proposed system. Quantitative results show that the error of volume measurement was less than 1%, indicating that the system is capable of automatic ultrasound scanning and 3-D imaging. It is expected that the proposed system can be well used in clinical practices.
Geometric Distortion Insensitive Image Watermarking in Affine Covariant Regions

Feature-based image watermarking schemes, which aim to survive various geometric distortions, have attracted great attention in recent years. Existing schemes have shown robustness against rotation, scaling, and translation, but few are resistant to cropping, nonisotropic scaling, random bending attacks (RBAs), and affine transformations. Seo and Yoo present a geometrically invariant image watermarking based on affine covariant regions (ACRs) that provide a certain degree of robustness. To further enhance the robustness, we propose a new image watermarking scheme on the basis of Seo's work, which is insensitive to geometric distortions as well as common image processing operations. Our scheme is mainly composed of three components: 1) feature selection procedure based on graph theoretical clustering algorithm is applied to obtain a set of stable and nonoverlapped ACRs; 2) for each chosen ACR, local normalization, and orientation alignment are performed to generate a geometrically invariant region, which can obviously improve the robustness of the proposed watermarking scheme; and 3) in order to prevent the degradation in image quality caused by the normalization and inverse normalization, indirect inverse normalization is adopted to achieve a good compromise between the imperceptibility and robustness. Experiments are carried out on an image set of 100 images collected from Internet, and the preliminary results demonstrate that the developed method improves the performance over some representative image watermarking approaches in terms of robustness.
Perceptually Aware Image Retargeting for Mobile Devices

Retargeting aims at adapting an original high-resolution photograph/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Typically, only low-level visual features are exploited, and human visual perception is not well encoded. In this paper, we propose a novel retargeting framework that rapidly shrinks a photograph/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserving graph ranking algorithm, which efficiently selects a few salient object patches to mimic the human gaze shifting path (GSP) when viewing a scene. Afterward, an aggregation-based CNN is developed to hierarchically learn the deep representation for each GSP. Based on this, a probabilistic model is developed to learn the priors of the training photographs that are marked as aesthetically pleasing by professional photographers. We utilize the learned priors to efficiently shrink the corresponding GSP of a retargeted photograph/video to maximize its similarity to those from the training photographs. Extensive experiments have demonstrated that: 1) our method requires less than 35 ms to retarget a  $1024\times 768$  photograph (or a  $1280\times 720$  video frame) on popular iOS/Android devices, which is orders of magnitude faster than the conventional retargeting algorithms; 2) the retargeted photographs/videos produced by our method significantly outperform those of its competitors based on a paired-comparison-based user study; and 3) the learned GSPs are highly indicative of human visual attention according to the human eye tracking experiments.
Multi-atlas Based Image Selection with Label Image Constraint

Atlas selection plays an important role in multiatlas based image segmentation. In atlas selection methods, manifold learning based techniques have recently emerged as very promisingly. However, due to the complexity of anatomical structures in raw images, it is difficult to get accurate atlas selection results by measuring only the distance between raw images on the manifolds. In this paper, we tackle this problem by proposing a label image constrained atlas selection (LICAS) method to exploit the shape and size information of the regions to be segmented from the label images. Constrained by the label images, a new manifold projection method is developed to help uncover the intrinsic similarity between the regions of interest across images. Compared with other existing methods, the experimental results of segmentation on 60 Magnetic Resonance (MR) images showed that the selected atlases are closer to the target structure and more accurate segmentation can be obtained by using the proposed method.
Robust Hashing With Local Models for Approximate Similarity Search

Similarity search plays an important role in many applications involving high-dimensional data. Due to the known dimensionality curse, the performance of most existing indexing structures degrades quickly as the feature dimensionality increases. Hashing methods, such as locality sensitive hashing (LSH) and its variants, have been widely used to achieve fast approximate similarity search by trading search quality for efficiency. However, most existing hashing methods make use of randomized algorithms to generate hash codes without considering the specific structural information in the data. In this paper, we propose a novel hashing method, namely, robust hashing with local models (RHLM), which learns a set of robust hash functions to map the high-dimensional data points into binary hash codes by effectively utilizing local structural information. In RHLM, for each individual data point in the training dataset, a local hashing model is learned and used to predict the hash codes of its neighboring data points. The local models from all the data points are globally aligned so that an optimal hash code can be assigned to each data point. After obtaining the hash codes of all the training data points, we design a robust method by employing ℓ2,1-norm minimization on the loss function to learn effective hash functions, which are then used to map each database point into its hash code. Given a query data point, the search process first maps it into the query hash code by the hash functions and then explores the buckets, which have similar hash codes to the query hash code. Extensive experimental results conducted on real-life datasets show that the proposed RHLM outperforms the state-of-the-art methods in terms of search quality and efficiency.
Weakly Supervised Multi-Graph Learning for Robust Image Reranking

Visual reranking has been widely deployed to refine the traditional text-based image retrieval. Its current trend is to combine the retrieval results from various visual features to boost reranking precision and scalability. And its prominent challenge is how to effectively exploit the complementary property of different features. Another significant issue raises from the noisy instances, from manual or automatic labels, which makes the exploration of such complementary property difficult. This paper proposes a novel image reranking by introducing a new Co-Regularized Multi- Graph Learning (Co-RMGL) framework, in which intra-graph and inter-graph constraints are integrated to simultaneously encode the similarity in a single graph and the consistency across multiple graphs. To deal with the noisy instances, weakly supervised learning via co-occurred visual attribute is utilized to select a set of graph anchors to guide multiple graphs alignment and fusion, and to filter out those pseudo labeling instances to highlight the strength of individual features. After that, a learned edge weighting matrix from a fused graph is used to reorder the retrieval results. We evaluate our approach on four popular image retrieval datasets and demonstrate a significant improvement over state-of-the-art methods.
Extracting key moving objects in video

This Paper presents a novel scheme for extracting the key moving objects in picture in picture news video. In this kind of video, some moving objects undergo the static camera while the others do not. Actually it is very difficult for computer to distinguish whether the motion is caused by the moving objects themselves or the moving camera by the frame difference only. So in the paper, the fast, automatic and robust anchorperson extraction is achieved by the simple anchorperson model. The experimental results demonstrate good performances.
Prostate Segmentation in MR Images Using Discriminant Boundary Features

Segmentation of the prostate in magnetic resonance image has become more in need for its assistance to diagnosis and surgical planning of prostate carcinoma. Due to the natural variability of anatomical structures, statistical shape model has been widely applied in medical image segmentation. Robust and distinctive local features are critical for statistical shape model to achieve accurate segmentation results. The scale invariant feature transformation (SIFT) has been employed to capture the information of the local patch surrounding the boundary. However, when SIFT feature being used for segmentation, the scale and variance are not specified with the location of the point of interest. To deal with it, the discriminant analysis in machine learning is introduced to measure the distinctiveness of the learned SIFT features for each landmark directly and to make the scale and variance adaptive to the locations. As the gray values and gradients vary significantly over the boundary of the prostate, separate appearance descriptors are built for each landmark and then optimized. After that, a two stage coarse-to-fine segmentation approach is carried out by incorporating the local shape variations. Finally, the experiments on prostate segmentation from MR image are conducted to verify the efficiency of the proposed algorithms.
Beauty and beast: image retrieval for image remodeling

This work presents a novel image retrieval for image remodeling (IR4IR) technique. A traditional content-based image retrieval (CBIR) system is regarded as a basis and colour features are selected to present the images' contents. After retrieving the related results, top positive examples are chosen to remodel the submitted query image by splitting an image into some minor sub-blocks and replacing them, meanwhile, the shifting information of each sub-block can be embedded into the least-significant-bit (LSB) of the corresponding pixels' colour channels, so that the remodeled image can come back to the original one. The new technique is helpful for arts related research and secures communication. In the experiments, the pre-process of image retrieval is on nearly 60,000 images from the Corel database, and the experimental results of IR4IR clearly show the advantage of the new methods for its application.
Compact Structure Hashing via Sparse and Similarity Preserving Embedding

Over the past few years, fast approximate nearest neighbor (ANN) search is desirable or essential, e.g., in huge databases, and therefore many hashing-based ANN techniques have been presented to return the nearest neighbors of a given query from huge databases. Hashing-based ANN techniques have become popular due to its low memory cost and good computational complexity. Recently, most of hashing methods have realized the importance of the relationship of the data and exploited the different structure of data to improve retrieval performance. However, a limitation of the aforementioned methods is that the sparse reconstructive relationship of the data is neglected. In this case, few methods can find the discriminating power and own the local properties of the data for learning compact and effective hash codes. To take this crucial issue into account, this paper proposes a method named special structure-based hashing (SSBH). SSBH can preserve the underlying geometric information among the data, and exploit the prior information that there exists sparse reconstructive relationship of the data, for learning compact and effective hash codes. Upon extensive experimental results, SSBH is demonstrated to be more robust and more effective than state-of-the-art hashing methods.
On decision-feedback detection of nondiagonal differential space-time modulation in temporally correlated fading channels

Existing work on decision feedback detection (DFD) of differential space-time modulation (DTSM) were largely confined to diagonal constellations. In other works considering nondiagonal constellations, the temporal variation of fading in the DTSM supersymbol duration was ignored. In this paper, we take a close look at the DFD receiver structure for nondiagonal DSTM in temporally correlated fading. We identify the constellations of which the error performance is not influenced by ignorance of fading variation. In addition, by analyzing the error performance, we demonstrate that existing DFD, is however, fundamentally mismatched to other constellations in fast fading. A major conclusion is that diagonal design still plays an important role for the efficacy of DFD for nondiagonal constellation in fast fading.
Conference spotlight - ICIP'04 in the lion city-Singapore

he 2004  the eleventh in the series, will be held in Singapore 24–27 October at the Suntec Singapore International Convention and Exhibition Centre. Sponsored by the IEEE Signal Processing Society, ICIP is the world's largest and most comprehensive premium forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. The conference has been held annually since 1994. Research frontiers in fields ranging from traditional image processing applications to evolving multimedia, such as image and video technologies, are regularly advanced by results first reported in ICIP technical sessions. Members of the ICIP'04 organizing committee are from local as well as overseas universities, with the majority coming from Nanyang Technological University (NTU), one of the three universities in Singapore. NTU, which focuses on technology, science, and business education, receives full support from the local government and industries. The engineering schools at NTU offer enriched curricula tailored to achieving a very close integration of fundamental theory with current engineering practices. The School of Electrical and Electronic Engineering at NTU has many staff members actively researching in the area of digital signal processing and its applications in image and video processing. The other two universities are the National University of Singapore, the most comprehensive university in Singapore, and Singapore Management University, with its main focus-es in business and management education. We hope that hosting ICIP in Singapore will promote further advancement in the image and signal processing activities at both national and international levels. Located at the southern tip of Malaysia, the strategic position of Singapore provides the link between East and West, and this strategic importance has helped it to grow into a major hub for trade, communications , and tourism. This strategic location on major sea routes has given Singapore an economic impact in Southeast Asia. Singapore has the fifth highest per capita GDP in the world, thanks to the highly developed and successful free-market economy and the open and corruption-free business environment. Exports in electronics, chemicals, and service industries are the major drives of the economy. With heavy spending in education and technology, the government is establishing the country as the region's financial and high-tech hub. Singapore is a vibrant, multicultur-al, and sophisticated city-state, where tradition and modernity and East and West meet and interact comfortably and harmoniously. Visitors are provided with …
Gallager Bounds for Noncoherent Decoders in Fading Channels

Recently, Gallager's bounding techniques have been used to derive tight performance bounds for coded systems in fading channels. Most works in this field have thus far dealt with coherent decoding. This paper develops Gallager bounds for noncoherent systems in fading channels. Unlike coherent decoding, the exact error probability of a noncoherent decoder/detector conditioned on the fading coefficients does not admit a closed-form expression. This difficulty is overcome in this paper by employing the Chernoff technique. Although it weakens the bounds to some extent, the Chernoff technique enables the derivations of the limit-before-average (LBA) bound and Gallager bounds in closed form for noncoherent fading channels. Numerical examples show that the proposed bounds are convergent and are tighter than the conventional union bound.
A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation

This letter proposes a simple and fast approach for no-reference image sharpness quality assessment. In this proposal, we define the maximum local variation (MLV) of each pixel as the maximum intensity variation of the pixel with respect to its 8-neighbors. The MLV distribution of the pixels is an indicative of sharpness. We use standard deviation of the MLV distribution as a feature to measure sharpness. Since high variations in the pixel intensities is a better indicator of the sharpness than low variations, the MLV of the pixels are subjected to a weighting scheme in such a way that heavier weights are assigned to greater MLVs to make the tail end of MLV distribution thicker. The weighting leads to an improvement of the MLV distribution to be more discriminative for different blur degrees. Finally, the standard deviation of the weighted MLV distribution is used as a metric to measure sharpness. The proposed approach has a very low computational complexity and the performance analysis shows that our approach outperforms the state-of-the-art techniques in terms of correlation with human vision system on several commonly used databases.
An objective distortion measure for binary document images based on human visual perception

As we are moving to a digital world, digital document image processing is receiving more and more attention. Digital document images are essentially binary images. In applications related to binary document images, such as data hiding and watermarking in binary images, distortion may be present and it is necessary to measure the distortion for performance comparison. However traditional objective distortion measures cannot describe the distortion in binary images well to have a good match with human visual perception. In this paper we present a novel objective distortion measure for binary document images that well correlates to the subjective distortion perception. This measure is based on the reciprocal of distance that is straight forward to calculate. Our results show that the proposed distortion measure matches well with subjective evaluation found on human visual perception.
Deep regional feature pooling for video matching

In this work, we study the problem of deep global descriptors for video matching with regional feature pooling. We aim to analyze the joint effect of ROI (Region of Interest) size and pooling moment on video matching performance. To this end, we propose to mathematically model the distribution of video matching function with a pooling function nested in. Matching performance can be estimated by the separability of these class-conditional distributions between matching and non-matching pairs. Empirical studies on the challenging MPEG CDVA dataset demonstrate that performance trends are consistent with the estimation and experimental results, though the theoretical model is largely simplified compared to video matching and retrieval in practice.
Binary image watermarking through biased binarization

This paper presents a watermarking algorithm for binary images. The original binary image is blurred to a gray-level image and we embed the watermark by biasing the threshold in binarization. A loop is used to control the quality of watermarked images and robustness, and a key is generated for extraction. We employ error correction codes to reduce extraction error. This algorithm can be applied to general binary images except dithered images. Experiments show that the distortion in the watermarked image is not obtrusive and the algorithm provides some degree of robustness.
Splicing detection in out-of-focus blurred images

This paper proposes a novel framework for image splicing detection based on the inconsistency in the blur degree and depth information of an image. Firstly, the blur kernels of image blocks (local blur kernels) are estimated. Next, a multi-step reblurring technique is used to measure the relative blur degrees of the local blur kernels. The relative blur degrees are used as a feature to classify the image blocks based on the blur degrees. Furthermore, block-based and pixel-based techniques are incorporated for a fine segmentation of the regions with different blur degrees. Finally, the consistency in the blur degree and depth information of the regions are analyzed. Any inconsistency in the blur degree and depth information can be used as an evidence of image splicing. The experimental results show that the proposed approach can discriminate a wide range of blur degrees in natural and artificial blurred images which outperforms the state-of-the-art approaches. Furthermore, our proposed approach has been evaluated for image splicing detection. The result shows that it can be used successfully for detection of image splicing.
Unsharp Masking Sharpening Detection via Overshoot Artifacts Analysis

In this letter, we propose a new method in detecting unsharp masking (USM) sharpening operation in digital images. Overshoot artifacts are found to occur around side-planar edges in the sharpened images. Such artifacts, measured by a sharpening detector, can serve as a rather unique feature for identifying the previous performance of sharpening operation. Test results on photograph images with regard to various sharpening operators show the effectiveness of our proposed method.