nils-herrmann/clean_scirepeval huggingface dataset:
Inter-View Direct Mode for Multiview Video Coding

Global disparity between views is usually caused by the displacement between cameras, which can be accurately represented by a global geometric transformation. In this paper, we first propose an inter-view motion model in terms of the global geometric transformation to represent the motion correlation between two adjacent views. Specifically, the motion vector of a pixel in one view may be directly derived from that in another view according to the inter-view motion model. Further, we propose an inter-view direct mode to signal the decoder that the motion of a macroblock (MB) can be achieved from the coded view without any coding bits. The proposed inter-view direct mode is further incorporated in the existing multiview video coding (MVC) schemes (i.e., AVC-based MVC and 4-D wavelet-based MVC), working together with the other classical coding modes. The mode selection at each MB is accomplished with the rate-distortion optimization technique. The proposed inter-view direct mode can significantly reduce bits to code motion vectors especially at low bit rates, thus improving the coding efficiency
Low complexity RDO mode decision based on a fast coding-bits estimation model for H.264/AVC

Rate distortion optimization (RDO) is a very efficient tool for deciding a macroblock's coding mode, which has been adopted by the H.264/AVC codec and brings higher coding efficiency. However, due to the precise calculation of the current macroblock's distortion and coding-bits for each coding mode, RDO-based mode decision involves high computational complexity. In this paper, a simple model for quickly estimating the coding-bits of the quantized transform coefficients, which account for the major part of the computation when coding a macroblock, is presented. The model only uses nonzero coefficients' magnitude and coordinates information by add operations to fulfill estimation so that computational complexity can be significantly reduced. Experimental results show that with this model the computational complexity of RDO-based mode decision in the H.264/AVC codec can be reduced by 40-60% while coding efficiency has little drop.
Symmetric segment-based stereo matching of motion blurred images with illumination variations

Most existing methods of stereo matching focus on dealing with clear image pairs. Consequently, there is a lack of approaches capable of handling degraded images captured under challenging real situations, e.g. motion blur is present and an image pair is in different illumination conditions. In this paper we propose a novel approach to handling these challenging situations by formulating the problem into a Maximum a Posteriori (MAP) estimation framework, and adopt a segment-based symmetric stereo matching method to infer a mask of disparity map which indicates whether a disparity is affected by motion blur and estimate the disparity value. The experimental results show that our stereo matching method is able to compute more accurate disparity maps of this type of degraded images.
Review on control method of PVC production process

The up-to-the-minute research results of optimized operation and control technology on Polyvinyl Chloride (PVC) production process is introduced in details. It mainly includes PVC production technique, intelligent control strategy and modelling method. Some representative results of theoretical studies and practical projects from 1990 to 2009 are also discussed. In the end, the existent problems and future research directions of control technology in the PVC production process is concluded.
A Bit-Plane Decomposition Matrix-Based VLSI Integer Transform Architecture for HEVC

In this brief, a new very-large-scale integrated (VLSI) integer transform architecture is proposed for the High Efficiency Video Coding (HEVC) encoder. The architecture is designed based on the signed bit-plane transform (SBT) matrices, which are derived from the bit-plane decompositions of the integer transform matrices in HEVC. Mathematically, an integer transform matrix can be equally expressed by the binary weighted sum of several SBT matrices that are only composed of binary 0 or ±1. The SBT matrices are very simple and have lower bit width than the original integer transform in the form. The SBT matrices are also sparse and there are many zero elements. The sparse characteristic of SBT matrices is very helpful for saving the addition operators of SBT. In the proposed architecture, instead of the original integer transform in high bit width, the video data can be respectively transformed with the SBT matrices in lower bit width. As a result, the delay of the transform unit circuit can be significantly reduced with the proposed SBT. Moreover, exploiting the redundant element characteristic of SBT matrices, in which the elements are 0 or ±1, the adder reuse strategy is proposed for our transform architecture, which can save the circuit area efficiently. The simulation results show that by employing the proposed strategies the VLSI transform architecture can be synthesized in a proper area with a high working frequency and low latency. The architecture can support all HEVC encoders coding ultra high-definition video sequences in real time.
Signature Based Spectrum Sensing Algorithms for IEEE 802.22 WRAN

Spectral sensing for the presence of ATSC DTV signals in VHF/UHF TV bands under a very low SNR environment is one of the core technologies in IEEE 802.22 WRAN. There are multiple signatures embedded in the ATSC DTV signals and these signatures can be utilized to perform spectrum sensing. Signature-based spectrum sensing algorithms have an advantage in that when the signals other than noise are detected, we are almost sure that they are signals we want to detect. Thus, in this paper, several signature based spectrum sensing algorithms are presented. The false alarm rate for detectors employing different algorithms is analytically derived. The operating characteristic curves for different detectors are determined from computer simulations using ATSC A/74 DTV signal captures as a test database.
A System for Automatic Generation of Music Sports-Video

In this paper, we present a new representation of sports video abstract-music sports-video (MSV), which provides exciting sports content accompanied with high quality background music for audiences and is available for high-quality audio-visual entertainment. We also propose a system generating MSV from user-provided sports video and music automatically. Firstly, the given sports video is segmented into a series of story units. Then all the story units are ordered by the predefined exciting degree (ED) and some high ED or user preferred story units are selected for MSV generation. Secondly, the ED of the given music is estimated by energy analysis on music beat. Thirdly, the selected story units are matched with music by their ED corresponding. Finally, the output MSV is rendered by connecting the selected exciting story units with appropriate transition effects, accompanied with the music. Experiments show encouraging results
Maintenance oriented aircraft cockpit electrical power system function simulation of the A320

The urgency of maintenance training is become more and more outstanding in the civil aviation industry, and the specialized training equipments are of the shortage. In view of this, the power simulation system is designed for the maintenance personnel. According to the aircraft electrical power system is multi input multi output (MIMO) and control logic is complex. Taking into account the association with other electrical systems, put forward the functionality of the input, ontology and the output, and the simplified of the function model is establishment. Then the mathematical model is determined using matrix identification. Which solves the problems of elements multiple and logical relation complicated, speeds up the system speed and improves subsequent expansibility. Applying OpenGL as key technical means, VC++ and XML are used as resource allocation tools to establish simulation platform. It not only realizes simulation the normal functions of ELEC page, EMER ELEC PWR panel, ELEC panel, but also achieve a variety of electrical power system fault simulation.
A highly efficient mode decision algorithm and architecture for AVS HD Video Encoder

In Advanced Audio Video coding Standard (AVS), the utilization of variable block size ranging from 16×16 to 8×8 in inter frame encoding improves the coding efficiency significantly compared with a fixed MB partition. Rate distortion optimization (RDO) is the best known mode decision method, but the corresponding extremely high computational complexity limits its application. This paper proposes an algorithm based on the visual perception model and Sobel operator edge detection model to quickly select the best inter mode from 16×16, 16×8, 8×16 and 8×8 just by using the original pixels. We further analyze and redesign the MB level pipeline structure, and give the optimized hardware structure of the encoder. We tested different sequences including cif, 720p and 1080p, and the experimental results show that the coding efficiency is comparable with the traditional RDO method. The proposed hardware structure saves fractional motion estimation (FME) by 60% in areas and reduces the processing time by 200 cycles. Our proposed mode decision architecture can support the real time processing of 1080P@30fps.
A low power high speed ROIC design for 1024×1024 IRFPA with novel readout stage

A low power high speed Read-Out Integrated Circuit (ROIC) for a short-wave Infra-Red Focal Plane Array (IRFPA) is designed as a prototype for 1024 times 1024 image system. Ripple integration and readout scheme as well as highly efficient power management are introduced to this design in order to decrease total power. To further increase the readout speed while decrease the power dissipation, a novel readout stage is proposed and adopted in this circuit. By using the new structure, the ROIC achieves a data rate of 10 M/s per channel, with the total power dissipation of 56 mW.
Animating arbitrary topology 3D facial model using the MPEG-4 FaceDefTables

In this paper we put forward a method to animate an arbitrary topology facial model (ATFM) based on the MPEG-4 standard. This paper deals mainly with the problem of building the FaceDefTables, which play a very important role in the MPEG-4 based facial animation system. The FaceDefTables for our predefined standard facial model (SFM) are built using the interpolation method. Since the FaceDefTables depend on facial models, the FaceDefTables for the SFM can be applied only to those facial models having the same topology as the SFM. For those facial models that have different topology, we have to build the FaceDefTables accordingly. To acquire the FaceDefTables for ATFM, we first select feature points on ATFM, then transform the SFM according to those feature points. Finally, we project each vertex on the ATFM to the transformed SFM and build the FaceDefTables for the ATFM according to the projection position. With the FaceDefTables we built, realistic animation results have been acquired.
Shot classification for action movies based on motion characteristics

In this paper, we propose a shot classification method for action movies. Considering that motion characteristic is very important for semantic movie analysis, and it contains abundant information in action movies, the structure tensor analysis is used for feature extraction due to its capability of representing both spatial and temporal characteristics of a shot. Firstly, the movie shots with known labels are decomposed into a set of overlapped fixed-length segments and their structure tensor histogram are computed. The labels of segments are identical to the shots they belong to. Then Adaboost is used to train the semantic classifier with these structure tensor histogram sets. In testing procedure, the unknown shot are decomposed in the same way, and feature vector of each segment is extracted and classified by the classifier. Finally, the label of the shot is generalized by the segment label voting scheme. Experimental results show that this scheme could effectively deal with multiple motion patterns within shots and promising results are achieved.
Recognizing components of handwritten characters by attributed relational graphs with stable features

We present a method for Chinese character component recognition. We use an attributed relational graph model to describe a component. This model allows us to express the knowledge of the component shape and can include stable features of the component in various writing styles and various characters. We then describe a method to extract graph representation of an input character. The graph models are used to recognize components from a whole character by subgraph isomorphism. Our method need not segment component from character and can tolerate links and overlaps between a component and the other part. Experiments for handwritten Chinese characters show its efficiency.
B-rep from unregistered multiple range images

The authors present a method to produce an integrated description of an object given multiple range views without registration. The multiple view representation is in the form of a B-rep (boundary representation). They describe each view of the object as an attributed graph whose nodes are surface patches and links are the relations between surfaces. Any two attributed graphs, each corresponding to a given view, are matched, and the rigid motion transformation between them is computed. The basic strategy for multiple view integration is composed of two aspects: first, a composite graph which contains the bounding surfaces and their corresponding attributes is built and then these surfaces are intersected so that the edges and vertices corresponding to the B-rep description are identified. Results on objects with polyhedral as well as quadratic curved surfaces are presented.<<ETX>>
Tunable tensor voting improves grouping of membrane-bound macromolecules

Membrane-bound macromolecules are responsible for structural support and mediation of cell-cell adhesion in tissues. Quantitative analysis of these macromolecules provides morphological indices for damage or loss of tissue, for example as a result of exogenous stimuli. From an optical point of view, a membrane signal may have nonuniform intensity around the cell boundary, be punctate or diffused, and may even be perceptual at certain locations along the boundary. In this paper, a method for the detection and grouping of punctate, diffuse curvilinear signals is proposed. Our work builds upon the tensor voting and the iterative voting frameworks to propose an efficient method to detect and refine perceptually interesting curvilinear structures in images. The novelty of our method lies on the idea of iteratively tuning the tensor voting fields, which allows the concentration of the votes only over areas of interest. We validate the utility of our system with synthetic and annotated real data. The effectiveness of the tunable tensor voting is demonstrated on complex phenotypic signals that are representative of membrane-bound macromolecular structures.
Classification of tumor histopathology via sparse feature learning

Our goal is to decompose whole slide images (WSI) of histology sections into distinct patches (e.g., viable tumor, necrosis) so that statistics of distinct histopathology can be linked with the outcome. Such an analysis requires a large cohort of histology sections that may originate from different laboratories, which may not use the same protocol in sample preparation. We have evaluated a method based on a variation of the restricted Boltzmann machine (RBM) that learns intrinsic features of the image signature in an unsupervised fashion. Computed code, from the learned representation, is then utilized to classify patches from a curated library of images. The system has been evaluated against a dataset of small image blocks of 1k-by-1k that have been extracted from glioblastoma multiforme (GBM) and clear cell kidney carcinoma (KIRC) from the cancer genome atlas (TCGA) archive. The learned model is then projected on each whole slide image (e.g., of size 20k-by-20k pixels or larger) for characterizing and visualizing tumor architecture. In the case of GBM, each WSI is decomposed into necrotic, transition into necrosis, and viable. In the case of the KIRC, each WSI is decomposed into tumor types, stroma, normal, and others. Evaluation of 1400 and 2500 samples of GBM and KIRC indicates a performance of 84% and 81%, respectively.
Detection of nuclei in H&E stained sections using convolutional neural networks

Detection of nuclei is an important step in phenotypic profiling of histology sections that are usually imaged in bright field. However, nuclei can have multiple phenotypes, which are difficult to model. It is shown that convolutional neural networks (CNN)s can learn different phenotypic signatures for nuclear detection, and that the performance is improved with the feature-based representation of the original image. The feature-based representation utilizes Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Several combinations of input data representations are evaluated to show that by LoG representation, detection of nuclei is advanced. In addition, the efficacy of CNN for vesicular and hyperchromatic nuclei is evaluated. In particular, the frequency of detection of nuclei with the vesicular and apoptotic phenotypes is increased. The overall system has been evaluated against manually annotated nuclei and the F-Scores for alternative representations have been reported.
Tracking of convex objects

In this paper, we present a technique for grouping line segments into convex sets, where the line segments are obtained by linking edges obtained from the Canny edge detector. The novelty of the approach is twofold: first we define an efficient approach for testing the global convexity criterion, and second, we develop an optimal search based on dynamic programming or grouping the line segments into convex sets. Furthermore, we use the convexity results as the initial conditions for a deformable contour for object tracking. We show results on real images, and present a specific domain where this type of grouping can be directly applied.
Shape from interference patterns

A unique imaging modality based on equal thickness contours (ETC) has introduced a new opportunity for 3D shape reconstruction from multiple views. These ETCs are generated as a result of interference between transmitted and diffracted beams. We present a computational framework for representing each view of an object in terms of its object thickness, and then integrating these representations into a 3D surface by algebraic reconstruction. In this framework, the object thickness is first derived from ideal contours and then extended to real data. For real data, the object thickness is inferred by grouping curve segments that correspond to points of second derivative maxima. At each step of the process, we use some form of regularization to ensure closeness to the original features, as well as neighborhood continuity. We apply our approach to images of a sub-micron crystal structure obtained through a graphic process.
Shape Recovery from Equal Thickness Contours

A unique imaging modality based on equal thickness contours (ETC) has introduced a new opportunity for 3D shape reconstruction from multiple views. These ETC can be generated through an interference between transmitted and diffracted beams. We present a computational framework for representing each view of an object in terms of its object thickness and then integrating these representations into a 3D surface by algebraic reconstruction. In this framework, the object thickness is first derived from ideal contours and then extended to real data. For real data, the object thickness is inferred by grouping curve segments that correspond to points of second derivative maxima. At each step of the process, we use some form of regularization to ensure closeness to the original features as well as neighborhood continuity. We apply our approach to images of a submicron crystal structure obtained through a holographic process.
Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events

Saliency is an important perceptual cue that occurs at different levels of resolution. Important attributes of saliency are symmetry, continuity, and closure. Detection of these attributes is often hindered by noise, variation in scale, and incomplete information. This paper introduces the iterative voting method, which uses oriented kernels for inferring saliency as it relates to symmetry. A unique aspect of the technique is the kernel topography, which is refined and reoriented iteratively. The technique can cluster and group nonconvex perceptual circular symmetries along the radial line of an object's shape. It has an excellent noise immunity and is shown to be tolerant to perturbation in scale. The application of this technique to images obtained through various modes of microscopy is demonstrated. Furthermore, as a case example, the method has been applied to quantify kinetics of nuclear foci formation that are formed by phosphorylation of histone gammaH2AX following ionizing radiation. Iterative voting has been implemented in both 2-D and 3-D for multi image analysis
Shape metamorphism using p-Laplacian equation

We present a new approach for shape metamorphism, which is a process of gradually changing a source shape (known) through intermediate shapes (unknown) into a target shape (known). The problem, when represented with implicit scalar function, is under-constrained, and regularization is needed. Using the p-Laplacian equation (PLE), we generalize a series of regularization terms based on the gradient of the implicit function, and we show that the present methods lack additional constraints for a more stable solution. The novelty of our approach is in the deployment of a new regularization term when p /spl rarr/ /spl infin/ which leads to the infinite Laplacian equation (ILE). We show that ILE minimizes the supremum of the gradient and prove that it is optimal for metamorphism since intermediate solutions are equally distributed along their normal direction. Applications of the proposed algorithm for 2D and 3D objects are demonstrated.
Telepresence for in-situ microscopy

We present an approach for remote operation of instruments in the Internet environment. We have applied this approach to in-situ electron microscopy experiments that require dynamic interaction with the specimen under observation, as it is excited with external stimuli, i.e., temperature variation, EM field variation etc. The dynamic operations include control of the sample's position and orientation under the electron beam, the illumination conditions and focus, etc. Remote control via wide area networks like the Internet that do not offer real time data and command delivery guarantees is not practical for the finely tuned adjustments that dynamic studies require. We suggest that an effective approach to remote operation for this class of dynamic control applications must involve automated control operations performed near the instrument in order to eliminate the wide area network real-time delivery requirement. Our approach to this problem is based on advanced computer vision algorithms that permit instrumentation adjustments to be made in response to information extracted from the video signal generated by the microscope imaging system. We have determined the type of servo loops needed to enable remote operation and collaboration, and have introduced a novel partitioning of the control architecture for implementing this approach.