nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting Reflectional and Rotational Invariance in Single Image Superresolution

Stationarity of reconstruction problems is the crux to enabling convolutional neural networks for many image processing tasks: the output estimate for a pixel is generally not dependent on its location within the image but only on its immediate neighbourhood. We expect other invariances, too. For most pixel-processing tasks, rigid transformations should commute with the processing: a rigid transformation of the input should result in that same transformation of the output. In existing literature this is taken into account indirectly by augmenting the training set: reflected and rotated versions of the inputs are also fed to the network when optimizing the network weights. In contrast, we enforce this invariance through the network design. Because of the encompassing nature of the proposed architecture, it can directly enhance existing CNN-based algorithms. We show how it can be applied to SRCNN and FSRCNN both, speeding up convergence in the initial training phase, and improving performance both for pretrained weights and after finetuning.
Hyperspectral image deblurring with PCA and total variation

In this paper, we propose a novel algorithm for hyper-spectral (HS) image deblurring with principal component analysis (PCA) and total variation (TV). We first decorrelate the HS images and separate the information content from the noise by means of PCA. Then, we employ the TV method to jointly denoise and deblur the first principal components (PCs). Subsequently, noise in the last principal components is suppressed using a simple soft-thresholding scheme, for computational efficiency. Experimental results on simulated and real HS images are very encouraging.
Robust video feature extraction invariant to natural lighting by using LBP techniques with adaptive thresholding

Real time applications in video processing require low computational cost algorithms that allow processing a considerable number, commonly 25, of frames per second. Particularly, in outdoor visual scenes, a challenge is to develop robust algorithms with environmental conditions such as natural lighting. We propose to compute an adaptive threshold based on de probability distribution of the differences in intensity between the pixels and the points on their neighborhoods when applying the LBP technique. We assume, and prove it experimentally, that such distribution is a generalized Gaussian distribution under normal conditions. We consider normal conditions a visual scene in an outdoor field composed of different objects, colors and textures. To compute the adaptive threshold, we first estimate the parameters of the generalized Gaussian distribution using the set of all differences in the image between the intensity values of pixels and points in the neighbourhood. We test the methods on four videos captures during day and night in different places in the city of Ibague. The results of this approach are of interest to determine patterns, identify objects or detect background in a further step. However, an extra step for blur correction must be still included, considering that the images of the frames at night are commonly blurred.
Detection of a hand-raising gesture by locating the arm

This paper proposes a novel method for detecting hand-raising gestures in meeting room and classroom environments. The proposed method first detects faces in each frame of the video sequence in order to define the region of interest (ROI). Then the system locates arms in the region of interest by analyzing the geometric structure of edges on the arm instead of directly detecting the hand. The location and the orientation of a detected arm respect to the location of the face is used to make a decision on whether or not a person is raising hand. Finally, the frequency of a raised hand detected in previous frames is used to eliminate false positive detections and robustly detects persons who are raising a hand. Unlike major visual gesture recognition systems, our method does not rely on skin color or complex tracking algorithms, while achieving 92% sensitivity and 92% selectivity.
Weakly Convex Discontinuity Adaptive Regularization for Microwave Imaging

Reconstruction of inhomogeneous dielectric objects from microwave scattering is a nonlinear and ill-posed inverse problem. In this communication, we develop a new class of weakly convex discontinuity adaptive (WCDA) models as a regularization for quantitative microwave tomography. We show that this class includes the Huber regularizer and we show how to combine these methods with electromagnetic solvers operating on the complex permittivity profile. 2D reconstructions of objects from the Institute Fresnel database and experimental data at a single frequency demonstrate the effectiveness of the proposed regularization even when employing far less transmitters and receivers than available in the database.
Segmented image coding with contour simplification for video sequences

A segmented image coding algorithm for video sequences is presented. The first frame in the data is always encoded in an intraframe mode, while the rest of the frames in the data are encoded in an interframe mode. The interframe encoding is based on (1) block motion vector estimation and coding, (2) segmentation of the prediction error image and classification of the regions in the foreground/background, (3) contour simplification and coding, and (4) texture approximation by a linear combination of weakly separable base functions and coefficient coding. The contour simplification leads to an average reduction of 30% in the number of bits needed for the contour coding, the system can be adjusted at different bit-rates by parameter tuning, the simulation results are of high quality in terms of PSNR and they show that our coding approach is particularly promising for very low bit-rate applications.
A Recursive Scheme for Computing Autocorrelation Functions of Decimated Complex Wavelet Subbands

This correspondence deals with the problem of the exact computation of the autocorrelation function of a real or complex discrete wavelet subband of a signal, when the autocorrelation function or alternatively the power spectral density (PSD) of the signal in the time domain (or spatial domain) is either known or estimated using a separate technique. The solution to this problem allows us to couple time domain noise estimation techniques to wavelet domain denoising algorithms, which is crucial for the development of “blind” wavelet-based denoising techniques. Specifically, we investigate the Dual-Tree complex wavelet transform (DT-CWT), which has a good directional selectivity in 2-D and 3-D, is approximately shift-invariant and yields better denoising results than a discrete wavelet transform (DWT). The proposed scheme gives an analytical relationship between the PSD of the input signal/image and the PSD of each individual real/complex wavelet subband which is very useful for future developments. We also show that a more general technique, that relies on Monte Carlo simulations, requires a large number of input samples for a reliable estimate, while the proposed technique does not suffer from this problem.
Indoor assistance for visually impaired people using a RGB-D camera

In this paper a navigational aid for visually impaired people is presented. The system uses a RGB-D camera to perceive the environment and implements self-localization, obstacle detection and obstacle classification. The novelty of this work is threefold. First, self-localization is performed by means of a novel camera tracking approach that uses both depth and color information. Second, to provide the user with semantic information, obstacles are classified as walls, doors, steps and a residual class that covers isolated objects and bumpy parts on the floor. Third, in order to guarantee real time performance, the system is accelerated by offloading parallel operations to the GPU. Experiments demonstrate that the whole system is running at 9 Hz.
The lossless DCT for combined lossy/lossless image coding

This paper introduces a modification of the discrete cosine transform (DCT) that produces integer coefficients from which the original image data can be reconstructed losslessly. It describes an embedded coding scheme which incorporates this lossless DCT and presents some experimental rate-distortion curves for this scheme. The results show that the lossless compression ratio of the proposed scheme exceeds that of the lossless JPEG predictive coding scheme. On the other hand, in lossy operation the rate-distortion curve of the proposed scheme is very close to that of lossy JPEG. Also, the transform coefficients of the proposed scheme can be decoded with the ordinary DCT at the expense of a small error, which is only significant in lossless operation.
A versatile wavelet domain noise filtration technique for medical imaging

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.
ECG data compression with time-warped polynomials

Presents a new adaptive compression method for ECGs. The method represents each R-R interval by an optimally time-warped polynomial. It achieves a high-quality approximation at less than 250 bits/s. The author shows that the corresponding rates for other transform based schemes (the DCT and the DLT) are always higher. Also, the new method is less sensitive to errors in QRS detection and it removes more (white) noise from the signal. The reconstruction errors are distributed more uniformly in the new scheme and the peak error is usually lower. The reconstruction method is also useful for adaptive filtering of noisy ECG signals.<<ETX>>
A novel approach for detecting intersections from GPS traces

Intersection detection is a critical aspect for both route planning and path optimization. In literature, intersections are detected indirectly using the road users' turning behaviors. This paper proposes a novel approach to detect intersections directly using their definition of connecting road segments. We first detect the Longest Common Sub-Sequences (LCSS) between each pair of GPS traces using dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive substrings. The starting and ending points of each common substring are connecting points where two GPS traces split to different directions after they share a series of common locations. At last, we estimate Kernel Density (KD) of the connecting points and find the local maximas on the density map as intersections. Experimental results show our proposed method outperforms the state-of-the-art work with a high accuracy for intersection detection.
Markov Random Field based image inpainting with context-aware label selection

In this paper, we propose a novel global Markov Random Field based image inpainting method with context-aware label selection. Context is determined based on the texture and color features in fixed image regions and is used to distinguish areas of similar content to which the search for candidate patches is limited. Furthermore, we introduce a novel optimization approach, as an alternative to priority belief propagation framework, which further reduces the number of candidates and performs efficient inference to obtain final inpainting result. Experimental results show improvement over related state-of-the-art methods. Moreover, global optimization is significantly accelerated with the proposed inference approach.
Demo: Real-time 3D visualization of multi-camera room occupancy monitoring for immersive communication systems

This demo paper introduces a flexible 3D visualization framework that can visualize an abstract representation of real-world events such as human movement and human interaction in an immersive way by rendering animated avatars. In the presented demo, events are detected and sent to the visualization by a multi-camera room occupancy monitoring system that uses video analysis to track people in a room. Extracting high-level information about a scene and visualizing the relevant events in a 3D virtual environment can enable future immersive communication systems.
A Bayesian formulation of edge-stopping functions in nonlinear diffusion

We propose a novel, Bayesian formulation of the edge-stopping (diffusivity) function in a nonlinear diffusion scheme in terms of edge probability under a marginal prior on noise-free gradient. This formulation differs from the existing probabilistic diffusion approaches that give stochastic formulations for the conductivity but not for the diffusivity function of the gradient. In particular, we impose a Laplacian prior for the ideal gradient, but the proposed formulation is general and can be used with other marginal distributions. We also make links to related works that treat correspondences between nonlinear diffusion and wavelet shrinkage
Size Reduction of Look-Up Table Based Print Models

While printing is basically a binary process (put ink or not), the impression of grayvalues and colors can be obtained with patterns of small ink dots. The characteristics of and interactions between these dots have an important impact on the resulting print. Traditional neighbourhood look-up table based print models are practically limited to a 3x3 neighbourhood due to the exponential growth of the look-up table size. In this paper we propose novel techniques to reduce the size of the look-up table, which is valuable on it own, but also makes it achievable to employ neighbourhoods covering more than 3x3 pixels. Furthermore, we observed experimentally a favourable trade-off between modelling error and dimensionality reduction.
Tracking multiple objects using moving snakes

Active contours or snakes are widely used for segmentation and tracking. The ability of a snake to track an object depends on the movement of the object. If the object moves too far from one frame to another, the snake risks losing the true contour location. The subsequent evolution steps are negatively affected, reporting a false contour that can propagate to other frames. To overcome this problem a new snake algorithm has been developed. This new technique, moving snakes, works in two steps. During the fist step, the snake is translated as a rigid body towards the contour. This translation is calculated using the external force field of the image, therefore it does not require prior knowledge about the object movement. In the second step the actual shape evolution of the snake takes place.
Multimodal pLSA on visual features and tags

This work studies a new approach for image retrieval on largescale community databases. Our proposed system explores two different modalities: visual features and communitygenerated metadata, such as tags. We use topic models to derive a high-level representation appropriate for retrieval for each of our images in the database. We evaluate the proposed approach experimentally in a query-by-example retrieval task and compare our results to systems relying solely on visual features or tag features. It is shown that the proposed multimodal system outperforms the unimodal systems by approximately 36%.
An extended set of Haar-like features for rapid object detection

Recently Viola et al. [2001] have introduced a rapid object detection. scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated Haar-like features. These novel features significantly enrich the simple features of Viola et al. and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%.
A system for reliable dissolve detection in videos

Automatic shot boundary detection has been an active research area for nearly a decade and has led to high performance detection algorithms for hard cuts, fades and wipes. Reliable dissolve detection, however, is still an unsolved problem. We present the first robust and reliable dissolve detection system. A detection rate of 75% was achieved while reducing the false alarm rate to an acceptable level of 16% on a test video set for which so far the best reported detection and false alarm rate had been 66% and 59%, respectively. In addition, a dissolve's temporal extent is estimated, too. The core ideas of our novel approach are firstly the creation of a dissolve synthesizer capable of creating in principle an infinite number of dissolve examples of any duration from a video database of raw video footage allowing us to use an advanced machine learning algorithm such as neural networks and support vector machines which require large training sets, secondly, two simple features capturing the characteristics of dissolves, thirdly, a fully temporal multi-resolution search based on a fixed position and fixed-scale transition/special effect detector enabling us to determine also the true duration of detected dissolves, and finally, a post processing step which uses global motion estimation to further reduce the number of falsely detected dissolves.
Active Learning for Efficient Sampling of Control Models of Collectives

Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be modified autonomously by, e.g., Coalition formation and reconfiguration, adequate decisions on higher levels require a faithful abstracted model of a collective of agents. An illustrative example is found in calculating schedules for a set of power plants organized in a hierarchy of Autonomous Virtual Power Plants. Functional dependencies over the combinatorial domain, such as the joint costs or rates of change of power production, are approximated by repeatedly sampling input-output pairs and substituting the actual functions by piecewise linear functions. However, if the sampled data points are weakly informative, the resulting abstracted high-level optimization introduces severe errors. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Building on prior work, we propose to apply techniques from active learning to maximize the information gained by each additional point. Our results show that significantly better allocations in terms of cost-efficiency (up to 33.7 % reduction in costs in our case study) can be found with fewer but carefully selected sampling points using Decision Forests.
On the segmentation of text in videos

A new and robust multi-resolution approach of localizing and segmenting text in videos is proposed. The approach has been tested extensively on a large variety of video frame sizes such 352/spl times/240 up to 1920/spl times/1280 and a large representative set of video sequences such as home videos, newscasts, title sequences and commercials. 95% of the text bounding boxes in videos were localized correctly. 80% of all characters were segmented correctly, while 7.8% characters were damaged. 90% of the correctly segmented characters were recognized correctly by standard OCR software.
Key-Pose Prediction in Cyclic Human Motion

In this paper we study the problem of estimating inner cyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of pose lets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.
The MoCA Workbench: support for creativity in movie content analysis

Semantic access to the content of a video is highly desirable for multimedia content retrieval. Automatic extraction of semantics requires content analysis algorithms. Our MoCA (Movie Content Analysis) project provides an interactive workbench supporting the researcher in the development of new movie content analysis algorithms. The workbench offers data management facilities for large amounts of video/audio data and derived parameters. It also provides an easy-to-use interface for a free combination of basic operators into more sophisticated operators. We can combine results from video track and audio track analysis. The MoCA Workbench shields the researcher from technical details and provides advanced visualization capabilities, allowing attention to focus on the development of new algorithms. The paper presents the design and implementation of the MoCA Workbench and reports practical experience.
Fusing Local Image Descriptors for Large-Scale Image Retrieval

Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. Here we will employ the image content as a source of information to retrieve images and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Three different fusion approaches are explored. The image representations for each fusion approach are learned in an unsupervised fashion, and each image is modeled as a mixture of topics/object parts depicted in the image. However, not all object classes will benefit from all visual descriptors. Therefore, we also investigate which visual descriptor (set) is most appropriate for each of the twelve classes under consideration. We evaluate the presented models on a real world image database consisting of more than 246,000 images.