nils-herrmann/clean_scirepeval huggingface dataset:
Rate-Distortion Optimized Progressive 3D Reconstruction from Multi-view Images

Most of the previous multi-view reconstruction algorithms focus on minimizing reconstruction distortion, i.e. reconstructing a 3D model as close to the real object as possible, and rely on subsequent simplification process to control the reconstruction rate for practical usage. In this paper, in addition to reconstruction distortion, we directly consider the reconstruction rate in wide-baseline multi-view reconstruction. In particular, we develop a novel rate-distortion efficient multi-view reconstruction system, which consists of two major components: the PDE-based mesh optimization and the local subdivision based mesh refinement. Although both components are prior arts, to achieve rate-distortion optimization, new ingredients are proposed including non-uniform sample distribution for efficient and accurate surface distortion approximation in mesh optimization and non-uniform vertex distribution in mesh refinement. From a set of calibrated and segmented wide baseline images, our developed system is able to reconstruct the optimal 3D mesh that achieves minimum reconstruction distortion at any rate within a certain range. Experimental results show that our proposed system significantly outperforms the original PDE-based reconstruction method in both rate-distortion performance and visual quality.
Reference Plane Assisted Sketching Interface for 3D Freeform Shape Design

This paper presents a sketch-based modeling system with auxiliary planes as references for 3D freeform shape design. The user first creates a rough 3D model of arbitrary topology by sketching some contours of the model. Then the user can use sketching to perform deformation, extrusion, etc, to edit the model. To regularize and interpret the user's inputs properly, we introduce some rules for the strokes into the system, which are based on both the semantic meaning of the sketched strokes and human psychology. Unlike other sketching systems, all the creation and editing operations in the presented system are performed with reference to some auxiliary planes that are automatically constructed based on the user’s sketches or default settings. The use of reference planes provides a heuristic solution to the problem of ambiguity of 2D interface for modeling in 3D space. Examples demonstrate that the presented system can allow the user to intuitively and intelligently create and edit 3D models even with complex topology, which is usually difficult in other similar sketch-based modeling systems.
User-Friendly Interactive Image Segmentation Through Unified Combinatorial User Inputs

One weakness in the existing interactive image segmentation algorithms is the lack of more intelligent ways to understand the intention of user inputs. In this paper, we advocate the use of multiple intuitive user inputs to better reflect a user's intention. In particular, we propose a constrained random walks algorithm that facilitates the use of three types of user inputs: 1) foreground and background seed input, 2) soft constraint input, and 3) hard constraint input, as well as their combinations. The foreground and background seed input allows a user to draw strokes to specify foreground and background seeds. The soft constraint input allows a user to draw strokes to indicate the region that the boundary should pass through. The hard constraint input allows a user to specify the pixels that the boundary must align with. Our proposed method supports all three types of user inputs in one coherent computational framework consisting of a constrained random walks and a local editing algorithm, which allows more precise contour refinement. Experimental results on two benchmark data sets show that the proposed framework is highly effective and can quickly and accurately segment a wide variety of natural images with ease.
Fast environment matting extraction using compressive sensing

The existing high-accuracy environment matting extraction methods usually require the capturing of thousands of sample images and spend several hours in data acquisition. In this paper, a fast environment matting algorithm is proposed to extract the environment matte data effectively and efficiently. In particular, we incorporate the recently developed compressive sensing theory to simplify the data acquisition process. Moreover, taking into account special properties of light refraction and reflection effects of transparent object, we further propose to use hierarchical sampling and group clustering based recovery to accelerate the matte extraction process. Compared with the state-of-the-art approaches, our proposed algorithm significantly accelerates the environment matting extraction process while still achieving high-accuracy results.
Natural and Seamless Image Composition With Color Control

While the state-of-the-art image composition algorithms subtly handle the object boundary to achieve seamless image copy-and-paste, it is observed that they are unable to preserve the color fidelity of the source object, often require quite an amount of user interactions, and often fail to achieve realism when there exists salient discrepancy between the background textures in the source and destination images. These observations motivate our research towards color controlled natural and seamless image composition with least user interactions. In particular, based on the Poisson image editing framework, we first propose a variational model that considers both the gradient constraint and the color fidelity. The proposed model allows users to control the coloring effect caused by gradient domain fusion. Second, to have less user interactions, we propose a distance-enhanced random walks algorithm, through which we avoid the necessity of accurate image segmentation while still able to highlight the foreground object. Third, we propose a multiresolution framework to perform image compositions at different subbands so as to separate the texture and color components to simultaneously achieve smooth texture transition and desired color control. The experimental results demonstrate that our proposed framework achieves better and more realistic results for images with salient background color or texture differences, while providing comparable results as the state-of-the-art algorithms for images without the need of preserving the object color fidelity and without significant background texture discrepancy.
Design and Development of a Virtual Dolphinarium for Children With Autism

The recent proliferation of virtual reality (VR) technology applications in the autism therapy to promote learning and positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-assisted therapy has also become a topic of public and research interest for autism intervention and treatment. This paper will present an innovative design and development of a Virtual Dolphinarium for potential autism intervention. Instead of emulating the swimming with dolphins, our virtual dolphin interaction program will allow children with autism to act as dolphin trainers at the poolside and to learn (nonverbal) communication through hand gestures with the virtual dolphins. Immersive visualization and gesture-based interaction are implemented to engage children with autism within an immersive room equipped with a curved screen spanning a 320 and a high-end five-panel projection system. This paper will also report a pilot study to establish trial protocol of autism screening to explore the participants' readiness for the virtual dolphin interaction. This research will have two potential benefits in the sense of helping children with autism and protecting the endangered species.
Interpolation over arbitrary topology meshes using a two-phase subdivision scheme

The construction of a smooth surface interpolating a mesh of arbitrary topological type is an important problem in many graphics applications. This paper presents a two-phase process, based on a topological modification of the control mesh and a subsequent Catmull-Clark subdivision, to construct a smooth surface that interpolates some or all of the vertices of a mesh with arbitrary topology. It is also possible to constrain the surface to have specified tangent planes at an arbitrary subset of the vertices to be interpolated. The method has the following features: 1) it is guaranteed to always work and the computation is numerically stable, 2) there is no need to solve a system of linear equations and the whole computation complexity is O(K) where K is the number of the vertices, and 3) each vertex can be associated with a scalar shape handle for local shape control. These features make interpolation using Catmull-Clark surfaces simple and, thus, make the new method itself suitable for interactive free-form shape design.
Review of fault diagnosis of PMSM drive system in electric vehicles

Fault diagnosis is essential to guarantee the continuity and safety of the operation for Electric Vehicles (EVs). Permanent Magnet Synchronous Motor (PMSM) drive system as the key component of the EVs requires a robust and reliable system to detect and diagnosis various faults that may occur during operation. An undetected fault in the PMSM system may lead to high repair cost, or even catastrophic failure. Therefore, effective and timely fault diagnosis methods are greatly needed, which may realize the preventive maintenance for the PMSM drive system. In this paper, the types and fault diagnosis methods are summarized and compared. The PMSM fault diagnosis methods are divided into traditional and modern groups. The advantages, disadvantages of each method are reported. And the new directions are discussed based on previous work in the end.
Progressive Coding and Illumination and View Dependent Transmission of 3-D Meshes Using R-D Optimization

For transmitting complex 3-D models over bandwidth-limited networks, efficient mesh coding and transmission are indispensable. The state-of-the-art 3-D mesh transmission system employs a wavelet-based progressive mesh coder, which converts an irregular mesh into a semi-regular mesh and directly applies the zerotree-like image coders to compress the wavelet vectors, and view-dependent transmission, which saves the transmission bandwidth through only delivering the visible portions of a mesh model. We propose methods to improve both progressive mesh coding and transmission based on thorough rate-distortion analysis. In particular, by noticing that the dependency among the wavelet coefficients generated in remeshing is not being considered in the existing approaches, we propose to introduce a preprocessing step to scale up the wavelets so that the inherent dependency of wavelets can be truly understood by the zerotree-like image compression algorithms. The weights used in the scaling process are carefully designed through thoroughly analyzing the distortions of wavelets at different refinement levels. For the transmission part, we propose to incorporate the illumination effects into the existing view-depend progressive mesh transmission system to further improve the performance. We develop a novel distortion model that considers both illumination distortion and geometry distortion. Based on our proposed distortion model, given the viewing and lighting parameters, we are able to optimally allocate bits among different segments in real time. Simulation results show significant improvements in both progressive compression and transmission.
Shading-Based Surface Detail Recovery Under General Unknown Illumination

Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision. High quality reconstruction is usually challenging especially when fine detail is needed and the albedo of the object is non-uniform. This paper introduces vertex overall illumination vectors to model the illumination effect and presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are the two important observations: (1) the illumination over the surface of an object often appears to be piecewise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing the surface, which was often overlooked previously. Thus we propose to use TV to regularize the overall illumination vectors and use visual hull to constrain partial vertices. The reconstruction is formulated as a constrained TV-minimization problem that simultaneously treats the shape and illumination vectors as unknowns. An augmented Lagrangian method is proposed to quickly solve the TV-minimization problem. As a result, our approach is robust, stable and is able to efficiently recover high-quality surface details even when starting with a coarse model obtained using MVS. These advantages are demonstrated by extensive experiments on the state-of-the-art MVS database, which includes challenging objects with varying albedo.
View-Based 3D Model Transmission via Mesh Segmentation

For network-based graphics applications, a key challenge is how to efficiently transmit complex three-dimensional (3D) models over bandwidth-limited communication channels such as wireless links. Most existing 3D mesh coding algorithms do not consider the view-dependent rendering issue, and therefore result in transmitting unnecessary portions of 3D mesh models which leads to the waste in precious network bandwidth. In this paper, we propose a novel view-dependent 3D model transmission scheme, where a 3D model is partitioned into a number of segments, each segment is then independently coded using the MPEG-4 3DMC coding algorithm, and finally only the visible segments are selected and delivered to the client. Such a view-based 3D model transmission is able to substantially save the transmission bandwidth and therefore has a significant impact on wireless network based graphics applications.
Vector field fitting for real-time environment matting of transparent objects

The major drawback of real-time environment matting method is that the extracted environment matte data often contains significant amount of noise and errors. Although some filtering methods have been employed to remove the noise and obtain acceptable composition results, they are incapable of removing potential errors. In this paper, we first establish a light motion field to better describe the environmental matting effect of transparent objects and propose a new vector field fitting algorithm to simultaneously remove both noise and errors in the extracted matte data by using energy minimization approach. Experimental results show that our method is less sensitive to noise and error and can generate perceptually better composition results than the existing real-time environment matting approaches.
Performance evaluation for face recognition using wavelet-based image de-noising

In this research we scrutinize the face recognition system performance when the test images are imposed to different levels of noise. We tried to imitate the real world scenarios when the face images are captured from video cameras or scanners and suffer some noise. To investigate the performance of proposed system, we simulate this scenario by adding A WGN (additive white Gaussian noise) to the test images in the face database. For image de-noising, we used two different algorithms namely; Discrete Wavelets Transform (DWT) and Dual-Tree Complex Wavelets Transform (DTCWT). The denoised images are then fed to a PCA-based face recognition system for better recognition performance.
Sift-based iris recognition using sub-segments

In this paper, we investigate the use of Scale Invariant Feature Transform (SIFT) for iris recognition problem with sub-segments. Instead of using the whole iris, we extracted sub-segments from the iris image for classification. These subsegments were used separately for classification. Also, feature based fusion is applied using different sub-segments from the same iris. A preprocessing step for cropping the iris area from the images was address in this paper as well to increase performance of the system. The simulation results show high performance on the used database.
A new variable step-size block LMS algorithm for a non-stationary sparse systems

The conventional LMS algorithm has been successfully used in adaptive filtering for system identification (SI) problem. In telecommunications, acoustic echo SI problems usually have relatively large filter lengths that take a long time to be estimated. To overcome this problem, the block least-mean-square algorithm (BLMS) has been proposed. In BLMS, the filter coefficients are updated for blocks of input instead of each sample of input data. Using this advantage, we propose a new block-LMS algorithm with a function controlled variable step-size LMS (FC-VSSLMS) for non-stationary sparse systems identification. The performance of proposed algorithm is compared to those of the original BLMS and reweighted zero-attracting block LMS (RZA-BLMS), in terms of convergence rate and mean-square-deviation (MSD) in additive white Gaussian noise (AWGN) and additive uniformly distributed noise (AUDN). Simulations show that the proposed algorithm has a better performance than those of the other algorithms.
A block LMS-type algorithm with a function controlled variable step-size for sparse system identification

Block least-mean-square algorithm has a very fast processing time compared to the conventional LMS algorithm. This is due to the updating mechanism of the filter coefficients. Filter coefficients are updated for each sample input for the LMS algorithm. This process is faster with BLMS algorithm as the filter coefficients are updated for blocks of the input sequence instead. The BLMS algorithm can also be improved in the same manner as LMS by using different approaches such as variable step-size and/or sparsity. This paper proposes a new BLMS-type algorithm with a function controlled variable step-size LMS for sparse system identification. The performance of the proposed algorithm is compared to that of the BLMS algorithm in terms of convergence rate and mean-square-deviation. The effects of the filter length, sparsity degree and signal-to-noise ratio (SNR) on MSD were also investigated. Simulations prove that the proposed algorithm always outperforms the BLMS algorithm.
Multiresolution eigenspace and fisherspace face recognition

The paper introduces a multiresolution face recognition system using features extracted from eigen and fisher spaces. Discrete wavelet transform has been used to generate images at varying resolutions. Two methods are proposed to combine the features extracted from a set of face images with varying resolution. The first method is called the multiresolution feature concatenation (MFC), where we use principal component analysis (PCA) and linear discriminant analysis (LDA) as a dimensionality reduction process on each subband. Then the resulting projection coefficients of each subband are concatenated to perform classification. The second method is called the multiresolution majority voting (MMV), where the classification are done separately on each subband and then the majority voting is applied for making decision. The results obtained from both of the methods show promising results and MMV approach outperforms the MFC approach. Moreover, the two methods outperform the conventional PCA and LDA approaches respectively approach.
Face Recognition using Multiresolution PCA

In this paper we develop two techniques for face recognition using the idea of multiresolution face recognition. The multiresolution subbands are generated by using wavelet transform. The first technique is called the Multiresolution Feature Concatenation (MFC), where we use principal component analysis (PCA) as a dimensional reduction approach on each subband then concatenate the resulting projection coefficients of each subband together and perform classification. The second technique is called the Multiresolution Majority Voting (MMV), where the PCA approach and the classification are done separately on each subband and then the majority voting is applied for making decision. Both techniques show promising results and MMV approach outperforms the MFC approach. Moreover, the two techniques outperform the conventional PCA approach.
Adaptive and fixed eigenspace methods with a novel fitness measure for video based face recognition

In this paper a new system for identifying faces from video sequences using adaptive and fixed eigenspace approaches with a novel fitness measure is proposed. During the recognition process, each image in the gallery set is assigned a fitness value. The fitness value is updated for each frame and at the end of the probe video; the person corresponding to the gallery image with the highest fitness value is declared to be the identified person in the probe video. Eigenspace is used, for generating the feature vectors from the gallery set images. Two approaches have been introduced, where in the first approach; the eigenspace is updated after each frame is processed. The eigenspace update is performed by updating the fitness values and discarding the gallery images with the lowest respective fitness values. In the second method, a fixed eigenspace is generated from the initial gallery set and the fitness value for each gallery image is updated through the processing of the frames in the probe video. Again, in the end the gallery face image with the highest fitness value is declared to be the identified person. The BANCA video face database was adapted for performance testing. Both of the methods showed very competitive recognition rates in different scenarios.
DWT subbands fusion using ant colony optimization for edge detection

In this paper, a new approach for image edge detection using wavelet based ant colony optimization (ACO) is proposed. The proposed approach applies discrete wavelet transform (DWT) on the image. ACO is applied to the generated four subbands (Approximation, horizontal, vertical, and diagonal) separately for edge detection. After obtaining edges from the 4 subbands, inverse DWT is applied to fuse the results into one image with same size as the original one. The proposed approach outperforms the conventional ACO approach.
Phase retrieval of sparse signals from Fourier Transform magnitude using non-negative matrix factorization

Signal and image reconstruction from Fourier Transform magnitude is a difficult inverse problem. Fourier transform magnitude can be measured in many practical applications, but the phase may not be measured. Since the autocorrelation of an image or a signal can be expressed as convolution of x(n) with x(-n), it is possible to formulate the inverse problem as a non-negative matrix factorization problem. In this paper, we propose a new algorithm based on the sparse non-negative matrix factorization (NNMF) to estimate the phase of a signal or an image in an iterative manner. Experimental reconstruction results are presented.
Breast cancer classification using moments

This paper presents a system for detecting breast cancer based on moments. Instead of trying to improve the applied classifier we focused on improving the input attributes. We extracted new features from database samples using the first four moments namely, mean, variance, skewness and kurtosis. Through simulations, 10-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the classification performances. Various classifiers were used for evaluating the proposed approach. Results indicate advantage of such features in improving classification performance for all of the applied classifiers.