nils-herrmann/clean_scirepeval huggingface dataset:
Power-efficient CMOS image acquisition system based on compressive sampling

A novel compressive sampling scheme suitable for highly scalable hardware implementations is presented. The prototype design is implemented in a 0.18μm standard CMOS technology and utilizes compressed acquisition to boost the overall power efficiency. Specialized pixels, convenient for Comparator-Based Switched Capacitor readout are developed for this purpose. A custom measurement matrix generation algorithm is implemented which reduces in-pixel hardware complexity and performs measurement matrix generation in a single clock cycle. Column-Parallel Differential Cyclic-ADCs based on the Zero-Crossing Detection (ZCD) technique are used to convert the analog image measurements. Physical IC design issues such as the device noise, mismatch and non-linearity, are analyzed and their effects on compressed image acquisition are discussed. The final simulation results show that the proposed 256×256 pixels architecture consumes 1.45mW at 250fps and 26.2mW at 8000fps. The architecture can easily be scaled towards newer technology nodes and higher image resolutions.
Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust, and scalable framework for retrieving and analyzing recurring patterns of activity on graphs. Our method relies on a novel type of multilayer graph that encodes the spreading or propagation of events between successive time steps. We demonstrate the versatility of our method by applying it on three different real-world examples. First, we study how rumor spreads on a social network. Second, we reveal congestion patterns of pedestrians in a train station. Finally, we show how patterns of audio playlists can be used in a recommender system. In each example, relevant information previously hidden in the data is extracted in a very efficient manner, emphasizing the scalability of our method. With a parallel implementation scaling linearly with the size of the dataset, our framework easily handles millions of nodes on a single commodity server.
Multichannel Thresholding with Sensing Dictionaries

This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this algorithm and show that the latter results in much weaker conditions on the dictionary, sensing dictionary pair. We then do numerical simulations to confirm our theoretical findings, showing that p-thresholding is an interesting low complexity alternative to simultaneous greedy or convex relaxation algorithms for processing sparse multichannel signals with balanced coefficients, and finally point a connection to compressed sensing exploiting the additional freedom in designing the sensing dictionary.
Multiscale variational approach to simultaneous image regularization and segmentation

The problem of finding a piecewise smooth approximation of an original image while preserving edges is closely related to the image segmentation problem. Indeed, if we know the cartoon version of the original image, it is easier to perform an image partition into homogeneous regions. From this point of view, we propose to simultaneously perform regularization and segmentation. By working in the space of functions of bounded total variation, we allow discontinuities in the result of the minimisation. Therefore we perform regularization by using a total variation (TV) minimisation based on the gradient descent method. Each gradient step involves solving a discrete approximation of the corresponding partial differential equation that results in a smooth estimate of the original image without blurring across edges. This nonuniform smoothing allows an explicit region growing segmentation scheme that minimises one form of the Mumford-Shah functional. Therefore, the major novelity of our approach consists in competing two processes, one for boundary detection and the other for intraregion smoothing.
PCA using graph total variation

Mining useful clusters from high dimensional data has received significant attention of the signal processing and machine learning community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with problems such as high computational complexity (usually associated with the nuclear norm minimization), non-convexity (for matrix factorization methods) or susceptibility to gross corruptions in the data. In this paper we propose a convex, robust, scalable and efficient Principal Component Analysis (PCA) based method to approximate the low-rank representation of high dimensional datasets via a two-way graph regularization scheme. Compared to the exact recovery methods, our method is approximate, in that it enforces a piecewise constant assumption on the samples using a graph total variation and a piecewise smoothness assumption on the features using a graph Tikhonov regularization. Futhermore, it retrieves the low-rank representation in a time that is linear in the number of data samples. Clustering experiments on 3 benchmark datasets with different types of corruptions show that our proposed model outperforms 7 state-of-the-art dimensionality reduction models.
Scalable Feature Extraction for Coarse-to-Fine JPEG 2000 Image Classification

In this paper, we address the issues of analyzing and classifying JPEG 2000 code-streams. An original representation, called integral volume, is first proposed to compute local image features progressively from the compressed code-stream, on any spatial image area, regardless of the code-blocks borders. Then, a JPEG 2000 classifier is presented that uses integral volumes to learn an ensemble of randomized trees. Several classification tasks are performed on various JPEG 2000 image databases and results are in the same range as the ones obtained in the literature with noncompressed versions of these databases. Finally, a cascade of such classifiers is considered, in order to specifically address the image retrieval issue, i.e., bi-class problems characterized by a highly skewed distribution. An efficient way to learn and optimize such cascade is proposed. We show that staying in a JPEG 2000 framework, initially seen as a constraint to avoid heavy decoding operations, is actually an advantage as it can benefit from the multiresolution and multilayer paradigms inherently present in this compression standard. In particular, unlike other existing cascaded retrieval systems, the features used along our cascade are increasingly discriminant and lead therefore to a better tradeoff of complexity versus performance.
A low complexity Orthogonal Matching Pursuit for sparse signal approximation with shift-invariant dictionaries

We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to OMP at a computational cost similar to Matching Pursuit. Numerical experiments with a large audio signal show that, compared to OMP and Gradient Pursuit, the proposed algorithm runs in over 500 less time while leaving the approximation error almost unchanged.
Compressive multichannel cortical signal recording

This paper presents a novel approach to acquire multichannel wireless intracranial neural data based on a compressive sensing scheme. The designed circuits are extremely compact and low-power which confirms the relevance of the proposed approach for multichannel high-density neural interfaces. The proposed compression model enables the acquisition system to record from a large number of channels by reducing the transmission power per channel. Our main contributions are the twofold. First, a CMOS compressive sensing system to realize multichannel intracranial neural recording is described. Second, we explain a joint sparse decoding algorithm to recover the multichannel neural data. The idea has been implemented at system as well as circuit levels. The simulation results reveal that the multichannel intracranial neural data can be acquired by compression ratios as high as four.
Learning structured dictionaries for image representation

The dictionary approach to signal and image processing has been massively investigated in the last two decades, proving very attractive for a wide range of applications. The effectiveness of dictionary-based methods, however, is strongly influenced by the choice of the set of basis functions. Moreover, the structure of the dictionary is of paramount importance regarding efficient implementation and practical applications such as image coding. In this work, an over-complete code for sparse representation of natural images has been learnt from a set a real-world scenes. The functions found have been organized into a hierarchical structure. We take advantage of this representation of the dictionary, adopting a structured greedy algorithm to build sparse approximations of images. Using this procedure, no a-priori constraint is imposed on the structure of the dictionary, allowing great flexibility in its design and lower computational complexity.
Analysis of Parasitic Couplings in EMI Filters and Coupling Reduction Methods

In this paper, the effects of parasitic couplings in electromagnetic interference (EMI) filters are investigated using an equivalent circuit analysis and experiments. The investigated filter has the C-L-C filter structure which is composed of foil capacitors, feedthrough capacitors, and a toroidal inductor. The parasitic couplings between input capacitors and output capacitors increase the transfer function of the filter. We found that the increase of the transfer function is caused by two kinds of parasitic couplings: one is the parasitic inductive coupling between the two resonance circuits which are composed of feedthrough capacitors and foil capacitors, and another is the parasitic capacitive coupling between the input and the output foil capacitors. Balancing the two parasitic couplings, it is possible to reduce the transfer function increase without increasing the filter size. The experimental results of transfer functions agree with the equivalent circuit analysis results up to 30 MHz. The balancing condition obtained by the circuit analysis has been confirmed by the experiments.
Data format and view synthesis for free-viewpoint video streaming of super multiview video

Our goal is to propose and evaluate a new streaming data format that can be adapted to the limited bandwidth and capable of free-viewpoint video streaming using super multi-view video plus depth (MVD). Additionally, we proposed a view synthesis method for our data format. Given a requested free-viewpoint, we use the two closest views and corresponding depth maps to perform free-viewpoint video synthesis. The new data format consists of all views and corresponding depth maps in a lowered resolution, and the two closest views to the requested viewpoint in the high resolution. When the requested viewpoint changes, the two closest viewpoints will change, but one or both views are transmitted only in the low resolution during periods of large round-trip delay time. Therefore, the resolution compensation is required before view synthesis. Experimental results show that our proposed framework achieves view synthesis quality close to view synthesis using high resolution multi-view video plus depth.
View synthesis using superpixel based inpainting capable of occlusion handling and hole filling

The existing virtual view synthesis methods generate the images with many artifacts that are annoying, especially for forward virtual viewpoint, and virtual viewpoint generated by reference views with large baseline, due to occlusions and the limited sampling density. In this paper, we propose a new view synthesis method, robust to the above-mentioned problem, consist of three steps, using stereo contents. Firstly, view plus depth data of each viewpoint is 3D warped to the virtual viewpoint. We determine which neighboring pixels should be connected or kept isolated. Polygons enclosed by the connected pixels, i.e. superpixel, are interpolated. Secondly, we blend those warped images by comparing each pixel's depth value to obtain the virtual view, in which non-occlusion holes have already been interpolated by the process in the first step. Thirdly, the remaining holes are filled by inpainting. Our experimental results and comparisons show that the proposed view synthesis method allows smoother view reconstruction, while holes due to occlusion and 3D warping are filled with less artifacts.
Super-resolved free-viewpoint image synthesis combined with sparse-representation-based super-resolution

We consider super-resolved free-viewpoint image synthesis (SR-FVS), where a high-resolution (HR) image that would be observed from a virtual viewpoint is synthesized from a set of low-resolution multi-view images. In previous studies, methods for SR-FVS were proposed on the basis of reconstruction-based super-resolution (RB-SR). RB-SR uses multiple images to synthesize an HR image and thereby can naturally be applied to SR-FVS, where multi-view images are given as the input. However, the quality of the synthesized image depends on observation conditions such as the depth of the target scene, so sometimes the quality of SR-FVS can degrade severely. To mitigate such degradation, we propose integrating learning-based super-resolution (LB-SR), which uses knowledge learned from massive natural images, into the SR-FVS process. In this paper, we adopt sparse coding super-resolution (ScSR) as a LB-SR method and combine ScSR with an existing SR-FVS method.
How does Subsampling of Multi-View Images Affect the Rate-Distortion Performance?

This paper introduces a new theoretical model on the rate-distortion performance in transmitting multi-view image data. To reduce the data amount, a practical solution is just decreasing the number of images by subsampling. The questions we focus on are (i) how much bit-rate can be reduced, and (ii) how much additional distortion would be caused, by the subsampling of images. In our theoretical model, the rate-distortion theory and the plenoptic sampling theory are combined to consider the relation between the sampling condition (the cameras' interval) and the compression efficiency. Numerical simulations which show the theoretical lower bounds for (i) and (ii) are presented with discussions.
Rank analysis of a light field for dual-layer 3D displays

In this paper, a new type of 3D display, called a layered light-field display, was investigated. By using only a few light-attenuating layers located in front of a backlight, this display can present many views in different directions simultaneously without sacrificing the resolutions of each view. The essential factor for efficient layer-based representation, which has not been deeply analyzed in previous works, is redundancy- namely, a low rank structure-of the light-field data. Accordingly, to reveal the origin of the redundancy, a generative model, in which a textured surface located at a certain depth generates a light field, was formulated and evaluated in this paper. Our theoretical analysis shows that the redundancy depends on not only the texture complexity but also the depth of the surface from the light attenuating layers. The theoretical model was validated through experimental simulation of the display.
Nitride Trapping Memory Technology to Realize Flash-Embedded SoC of 65nm and Beyond

Nitride trapping memory technology to realize flash-embedded SoC of 65nm and beyond has been demonstrated for the first time. The device of 0.024 mum2/bit has been fabricated with advanced SoC process, comprising NiSi-salicidation, ultra shallow junction, fine planarization, WL gap-filling and highly reliable Cu/FSG interconnect with very narrow pitch. In-process charging damage has been eliminated by newly developed FEOL-level protection diode.
Bounding-Box Based Segmentation with Single Min-cut Using Distant Pixel Similarity

This paper addresses the problem of interactive image segmentation with a user-supplied object bounding box. The underlying problem is the classification of pixels into foreground and background, where only background information is provided with sample pixels. Many approaches treat appearance models as an unknown variable and optimize the segmentation and appearance alternatively, in an expectation maximization manner. In this paper, we describe a novel approach to this problem: the objective function is expressed purely in terms of the unknown segmentation and can be optimized using only one minimum cut calculation. We aim to optimize the trade-off of making the foreground layer as large as possible while keeping the similarity between the foreground and background layers as small as possible. This similarity is formulated using the similarities of distant pixel pairs. We evaluated our algorithm on the GrabCut dataset and demonstrated that high-quality segmentations were attained at a fast calculation speed.
A focus measure for light field rendering

Light field rendering is a fundamental method for synthesizing free-viewpoint images from a set of multiviewpoint images. In the simplest case, the scene structure is approximated by a simple plane: a focal plane. This approximation leads to focus-like effects on synthetic images where the focused depth is determined by the focal plane. A serious problem is that the range of the focused depth is too small in most practical cases. In this paper, we propose a focus measure that is specialized for synthetic images by light field rendering. When a set of differently-focused images is generated at a given viewpoint, the proposed focus measure enables us to obtain a depth map and an all in-focus image. Our approach has some remarkable differences from other related techniques, such as depth-from-stereo and depth-from-focus methods. Experimental results show that the proposed method effectively enhances PSNR of the final synthetic images.
Simulating a stacked-layer light-field display using orthographic/perspective view models

A stacked-layer light-field display consists of a few light attenuating layers stacked in front of a backlight. The appearance of the three-dimensional images produced with this display differs depending on how the input multi-view images are assigned to the light rays that are to be emitted from the display. In this research, we develop a simulator of this display where input images can be made correspond to both the orthographic and perspective view models. Our simulator allows us to check the appearance of the display in real time and helps us to determine the display design such as the output view model and layer intervals that are suitable for the scene.
From focal stacks to tensor display: A method for light field visualization without multi-view images

A new type of light field display called a tensor display was investigated. Although this display consists of only a few light attenuating layers located in front of a backlight, many views can be emitted in different directions simultaneously without sacrificing the resolution of each view. The transmittance pattern of each layer is calculated from a light field, namely, a set of dense multi-view images (typically dozens) that are to be observed from different directions. However, preparing such images is often cumbersome for real objects. We propose a method that does not require multi-view images as the input; instead, a focal stack composed of only a few differently focused images is directly transformed into the layer patterns. Our method greatly reduces the data acquisition cost while also maintaining the quality of the output light field. We validated the method with experiments using synthetic light field datasets and a focal stack acquired by an ordinary camera.
From Focal Stack to Tensor Light-Field Display

We propose a method of using a focal stack, i.e., a set of differently focused images, as the input for a novel light field display called a “tensor display.” Although this display consists of only a few light attenuating layers located in front of a backlight, it can be viewed from many directions (angles) simultaneously without the resolution of each viewing direction being sacrificed. Conventionally, a transmittance pattern is calculated for each layer from a light field, namely, a set of dense multi-view images (typically dozens) that are to be observed from different directions. However, preparing such a massive amount of images is often cumbersome for real objects. We developed a method that does not require a complete light field as the input; instead, a focal stack composed of only a few differently focused images is directly transformed into layer patterns. Our method greatly reduces the cost of acquiring data while also maintaining the quality of the output light field. We validated the method with experiments using synthetic light field data sets and a focal stack acquired by an ordinary camera.
A Theory of Aliasing Separation for Light Field Data

A light field means a 4-D function which characterizes the flow of light rays from a target scene, and used for image-based rendering. This paper presents a novel theoretical framework which considers the aliasing problem in dealing with discrete light field data. We introduce a new scheme called the aliasing separation to isolate the additional aliasing component caused by subsampling, and give a new perspective for how to optimize the reconstruction filter to interpolate light field data without aliasing artifacts. This optimization is closely related with depth estimation. Both the focus measure for light field rendering and the multiple baseline stereo can be derived from our theoretical framework.
Foreground-background segmentation using iterated distribution matching

This paper addresses the problem of image segmentation with a reference distribution. Recent studies have shown that segmentation with global consistency measures outperforms conventional techniques based on pixel-wise measures. However, such global approaches require a precise distribution to obtain the correct extraction. To overcome this strict assumption, we propose a new approach in which the given reference distribution plays a guiding role in inferring the latent distribution and its consistent region. The inference is based on an assumption that the latent distribution resembles the distribution of the consistent region but is distinct from the distribution of the complement region. We state the problem as the minimization of an energy function consisting of global similarities based on the Bhattacharyya distance and then implement a novel iterated distribution matching process for jointly optimizing distribution and segmentation. We evaluate the proposed algorithm on the GrabCut dataset, and demonstrate the advantages of using our approach with various segmentation problems, including interactive segmentation, background subtraction, and co-segmentation.