nils-herrmann/clean_scirepeval huggingface dataset:
A novel fingerprint image compression technique using wavelets packets and pyramid lattice vector quantization

A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization , a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor , for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and no training and multi-quantizing are required. We also show that the wedge region problem encountered with sharply distributed random sources is resolved in the proposed algorithm. The proposed algorithms adapt to variability in input images and to specified bit rates. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.
On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier

In this paper, we investigate the effects of different wavelet families as well as the effects of number of neurons on a the performance of a neural network based face recognition system. The face images are transformed using multi-level wavelets from which features are extracted. The resulting feature vectors are project over an orthogonal space using a simple PCA (Principal Component Analysis) projection. The uncorrelated transformed feature vectors are then used with an Multilayer Perceptron (MLP) based classifier. Different scenarios in terms of wavelet families and network structures are investigated. Extensive experimental results were performed using the ORL database. We show that certain families together with certain MLP structures give the best results in terms of recognition accuracy.
Salt-Dome Detection Using a Codebook-Based Learning Model

In this letter, we present a novel supervised codebook-based learning model for salt-dome detection in seismic imaging using texture-based attributes. The proposed algorithm is data driven and overcomes the limitations of existing texture-attributes-based salt-dome detection techniques which are heavily dependent upon the relevance of attributes to the geological nature of salt domes and the number of attributes used for classification. The algorithm works by combining the attributes from the gray-level cooccurrence matrix (GLCM) and those from the Gabor filter, with a codebook-based learning approach to delineate salt boundaries in seismic data. The combination of GLCM- and Gabor-filter-based attributes ensures that the algorithm works well even in the absence of strong reflectors along the salt boundary. Contrary to existing salt-dome detection techniques, our algorithm works with a codebook of small size and is shown to be robust and computationally efficient. The learning properties of the codebook-based model make the algorithm flexible and adaptable to the nature of time-scale varying data acquired in seismic surveys. We used the Netherlands F3 block to evaluate the performance of the proposed algorithm. Our experimental results show that the proposed codebook-based workflow can detect salt domes with good accuracy, superior to existing salt-dome detection techniques.
A new approach to pitch and voicing detection through spectrum periodicity measurement

A new method for detecting pitch and voicing information of speech with a high accuracy is addressed. The method is based on a novel approach to using the concept of instantaneous frequency (IF). In this method an IF estimation technique in the frequency domain is employed to expose the harmonic structure of the signal using a periodicity measure. This measure is based on the flatness of the IF, which describes the spectrum periodicity within a certain frequency band where the pitch harmonics are most likely found. The flatness measurement also yields voicing information extracted using an auto-thresholding technique. The proposed method was evaluated through comparison with cepstral pitch and voicing detection considering accuracy and reconstructed speech quality.
A new hybrid LPC-DWT algorithm for high quality audio coding

This paper examines a new method for coding high quality digital audio signals based on a combination of linear predictive coding (LPC) and the discrete wavelet transform (DWT). In this method, a linear predictor is first used to model each audio frame. Then, the prediction error is analyzed using the DWT. The LPC coefficients and DWT coefficients are quantized using a novel bit allocation scheme which minimizes the overall quantization error with respect to the masking threshold. The proposed coder is capable of delivering near-transparent audio signal quality at encoding bit rates of around 90-96 kb/s. Objective and subjective results suggest that the proposed coder operating at 90-96 kb/s has a performance comparable to that of the MPEG layer II codec operating at 128 kb/s.
Implementation and evaluate the no-reference image quality assessment based on spatial and spectral entropies on the different image quality databases

The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality. In this work, it suggest that a single strategy used for single database may not be sufficient; rather, The no-reference/blind image quality assessment (NR-IQA) is the most difficult due to the reference images are not available. Spatial-Spectral Entropy-based Quality (SSEQ) index has been proven successful in image modeling and feature extraction. However, it have been improve the general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features used and their relevance to perception and thoroughly evaluate the algorithm on another databases than LIVE IQA database (applied to another three databases of subjective image quality: 1. The TID database, 2. the Toyama database, and 3. the Categorical Subjective Image Quality _CSIQ_ database.). Need find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top.
On the validity of Gaussian approximations to exact test statistics of energy detector based spectrum sensing for cognitive radios

Cognitive radio is an emerging technology which offers dynamic and opportunistic access to under utilized spectrum resources. Energy detection is the dominantly used spectrum sensing approach owing to its low computational complexity and ability to identify spectrum holes without requiring a priori knowledge of primary transmission characteristics. In this paper, we discuss the challenges in using the Gaussian approximation to the exact test statistics of energy detector. More importantly, we present an in depth analysis on the validity of the Gaussian approximation under practical sensing scenarios. Specifically, in addition to the number of observed samples, the roles of signal to noise ratio and performance constraints in terms of required probability of detection or allowed false-alarm rate are highlighted when the Gaussian approximation is used instead of the exact test statistics.
Robust image segmentation based on convex active contours and the Chan Vese model

In this paper, we present a robust image segmentation technique based on the Geodesic Convex Active Contour (GCAC) and the Chan-Vese (CV) model. The proposed algorithm overcomes the drawbacks of existing interactive image segmentation techniques which are heavily dependent upon the initial user input. Here, we propose to start with a Geodesic based contour before using the Chan-Vese model. Contrary to the basic Geodesic model and the Random Walk technique, our algorithm works with minimal input and is shown to be independent of the location of the input pixels provided by the user. The algorithm works by initiating a contour based on the Geodesic distance which is then used with the Chan-Vese model to further refine the segmentation results. The combination of region-based and boundary-based segmentation techniques ensures that the proposed algorithm works well with all types of images. We tested the proposed algorithm on several standard databases using both subjective and objective measures. Our experimental results show that the proposed algorithm outperforms existing approaches over indoor and outdoor images in terms of both processing time and segmentation accuracy.
A multilevel memory-assisted lossless compression algorithm for medical images

As medical imaging facilities move towards film-less imaging technology, robust image compression systems are starting to play a key role. Conventional storage and transmission of large-scale raw medical image datasets can be very expensive and time-consuming. Recently, we proposed a memory-assisted lossless image compression algorithm based on Principal Component Analysis(PCA). In this paper, we further improve the performance of the algorithm in two different directions: Firstly, we replace PC A with NMF (Non Negative Matrix Factorization). NMF has several advantages in representing images with an image-like basis, results in sparse factors, and provides better user control over iterations. Secondly, we expand the single-level model with a new multilevel decomposition/projection framework to further reduce entropy of residual images. Our experimental results on X-ray images confirm that both modifications provide significant improvements over the single level PCA based algorithm as well as existing non-memory based techniques.
New results in low bitrate audio coding using a combined harmonic-wavelet representation

In this paper, we propose a new combined harmonic-wavelet representation for audio where a harmonic analysis-synthesis scheme is used, first, to approximate each audio frame as a sum of several sinusoids. Then, the difference between the original signal and the reconstructed harmonic signal is analyzed using a wavelet filtering scheme. After each step (harmonic analysis and wavelet filtering), parameters are quantized and encoded. Compared to previously proposed methods, our audio coder uses different harmonic analysis-synthesis and wavelet filtering schemes. We use the total least squares (TLS)-prony algorithm for the harmonic analysis-scheme, and an M-band wavelet transform for analyzing the residual. Altogether, our proposed coder is capable of delivering excellent audio signal quality at encoder bitrates of 60-70 kb/s.
Performance analysis of normalized least mean p-norm lattice algorithm for alpha-stable processes

The existence of impulsive noise with /spl alpha/-stable distributions has been addressed in many applications. We present two new adaptive algorithms using the lattice structure for /spl alpha/-stable processes. In these algorithms fractional lower order moments of residual errors are used to update the filter coefficients. Based on the empirical results, the proposed algorithms show superior convergence speed over presently available techniques in parameter estimation of /spl alpha/-stable processes. The problem of misalignment of the mean of the estimated parameters with respect to the true values is also addressed.
Variable Step-Size Transform Domain ILMS and DLMS algorithms with system identification over adaptive networks

This paper presents a powerful performance and convergence speed of Variable Step-Size Transform Domain Incremental/Diffusion Least Mean Square (VSS-TD-I/D-LMS). It modifies and extends several already existing algorithms of VSS-LMS and VSS-TD-LMS to wireless sensor adaptive networks. The effect of transform domain along with power normalization plays a rule in reduce eigenvalue spread of input autocorrelation and whitening the highly correlated process. In ILMS, each node sensor is allowed to share its estimate with a direct neighbor while in DLMS each node update its estimate a long with a group of neighbors. Simulation results are shown that the performance improvement of cooperative fashion has substantial and favorable convergence speed. Simulation results are shown the performance improvement of cooperative fashion in convergence speed.
Low bit-rate audio coder using an M-band multiresolution filter bank

A new audio coding system is proposed. Using an M-band multiresolution filter bank technique. This consists of a cascade of 4-band and 8-band filter banks. Experiments with a complete audio coding system were carried out with the proposed filter bank, masking model, bit allocation algorithm, scalar quantisation and Huffman coding. For the broadband signals tested, the proposed system resulted in near transparent quality at bit-rates of 78-91 kbit/s with low computational load. It also achieved similar performance to the MPEG layer 2 coder at 128 kbit/s.
Regression based bandwidth selection for segmentation using Parzen windows

We consider the problem of segmentation of images that can be modelled as piecewise continuous signals having unknown, nonstationary statistics. We propose a solution to this problem which first uses a regression framework to estimate the image PDF, and then mean-shift to find the modes of this PDF. The segmentation follows from mode identification wherein pixel clusters or image segments are identified with unique modes of the multimodal PDF. Each pixel is mapped to a mode using a convergent, iterative process. The effectiveness of the approach depends upon the accuracy of the (implicit) estimate of the underlying multimodal density function and thus on the bandwidth parameters used for its estimate using Parzen windows. Automatic selection of bandwidth parameters is a desired feature of the algorithm. We show that the proposed regression-based model admits a realistic framework to automatically choose bandwidth parameters which minimizes a global error criterion. We validate the theory presented with results on real images.
Path planning using a potential field representation

The findpath problem is the problem of moving an object to the desired position and orientation while avoiding obstacles. The authors present an approach to this problem using a potential-field representation of obstacles. A potential function similar to an electrostatic potential is assigned to each obstacle, and the topological structure of the free space is derived in the form of minimum potential valleys. A path specified by a subset of valley segments and associated object orientations, which minimizes a heuristic estimate of path length and the chance of collision, is selected as the initial guess of the solution. Then, the selected path as well as the orientation of the moving object along the path is modified to minimize a defined cost of the path. Findpath problems possessing three different levels of difficulty are identified. Path optimization is performed in up to three stages, according to the level of difficulty of the problem. These three stages are addressed by three separate algorithms which are automatically selected. The performance of the algorithms is illustrated.<<ETX>>
Non-frontal Camera Calibration Using Focal Stack Imagery

A non-frontal camera has its lens and sensor plane misaligned either due to manufacturing limitations or an intentional tilting as in tilt-shift cameras. Under ideal perspective imaging, a geometric calibration of tilt is impossible as tilt parameters are correlated with the principal point location parameter. In other words, there are infinite combinations of principal point and sensor tilt parameters such that the perspective imaging equations are satisfied equally well. Previously, the non-frontal calibration problem (including sensor tilt estimation) has been solved by introducing constraints to align the principal point with the center of radial distortion. In this paper, we propose an additional constraint which incorporates image blur/defocus present in non-frontal camera images into the calibration framework. Specifically, it has earlier been shown that a non-frontal camera rotating about its center of projection captures images with varying focus. This stack of images is referred to as a focal stack. Given a focal stack of a known checkerboard (CB) pattern captured from a non-frontal camera, we combine geometric re-projection error and image bur error computed from current estimate of sensor tilt as the calibration optimization criteria. We show that the combined technique outperforms geometry-only methods while also additionally yielding blur kernel estimates at CB corners.
Closed-form solution+maximum likelihood: a robust approach to motion and structure estimation

A robust approach is presented to estimation motion and structure from image sequences. The approach consists of two steps. The first step is estimating the motion parameters using a robust linear algorithm that gives a closed-form solution for motion parameters and scene structure. The second step is improving the results from the linear algorithm using maximum-likelihood estimation. An algorithm using point correspondences from monocular images is discussed in detail and experimented with. An algorithm using line correspondences is briefly discussed. The simulations show that maximum-likelihood estimation achieves remarkable improvement over the preliminary estimates given by the linear algorithm. The algorithm is also tested on images of real scenes from automatically computed displacement field. The proposed approach is independent of the exact tokens used to establish correspondences, e.g. displacement flow, optical flow, or discrete features. Two or more types of tokens may be used, for monocular or binocular images.<<ETX>>
An analytically tractable potential field model of free space and its application in obstacle avoidance

An analytically tractable potential field model of free space is presented. The model assumes that the border of every two dimensional (2D) region is uniformly charged. It is shown that the potential and the resulting repulsion (force and torque) between polygonal regions can he calculated in closed form. By using the Newtonian potential function, collision avoidance between object and obstacle thus modeled is guaranteed in a path planning problem. A local planner is developed for finding object paths going through narrow areas of free space where the obstacle avoidance is most important. Simulation results show that not only does individual object configuration of a path obtained with the proposed approach avoid obstacles effectively, the configurations also connect smoothly into a path.
A new collaborative infrastructure: SCAPE

This paper presents a multi-user collaborative infrastructure, SCAPE (an acronym for Stereoscopic Collaboration in Augmented and Projective Environments), which is based on recent advancement in head-mounted projective display (HMPD) technology. The SCAPE mainly consists of a 3'/spl times/5' interactive workbench and a 12'/spl times/12'/spl times/9' room-sized walk-through display environment, multiple head-tracked HMPDs, multi-modality interface devices, and a generic application-programming interface (API) designed to coordinate the components. The infrastructure provides a shared space in which multiple users can simultaneously interact with a 3D synthetic environment from their individual viewpoints. We detail the SCAPE implementation and include an application example that demonstrates major interface and cooperation features.
A hybrid design for robot path planning in the presence of obstacles

A method combining the tools of combinatorics and optimal control theory is presented to solve the path-planning problem. Initially, a combinatorial problem is solved to identify collision-free paths originally designed for a point object. Then the problem of moving the actual object through the safe paths for the point object is posed as an optimal control problem and is solved using a numerical technique involving finite-dimensional optimization. Some of the constraints to the optimization problem are in the form of inequalities, resulting in nondifferentiable expression when augmented with the performance index. Because of this, the R. Hooke and T.A. Jeeves method (1961), which is computationally quite prohibitive, has been adopted. To overcome this burden, at least partially, a method has been introduced whereby several variables appearing in the optimization are grouped into aggregate quantities, thus reducing the number of actual variables used in the iterations. 2-D and 3-D examples illustrate the applicability of the proposed hybrid design.<<ETX>>
A Refractive Camera for Acquiring Stereo and Super-resolution Images

We propose a novel depth sensing system composed of a single camera, and a transparent plate which is placed in front of the camera and rotates about the optical axis of the camera. The camera takes a sequence of images as the plate rotates, which provide the equivalent of a large number of stereo pairs. Compared with conventional multi-camera stereo systems, the use of a single camera for capturing stereo pairs helps improve the accuracy of detecting correspondences. The availability of the large number of stereo pairs also reduces matching ambiguities, even for objects with low texture. By using both the images and the estimated depth map, we show that the proposed system is also capable of generating super-resolution images. Experimental results on reconstructing 3D structures and recovering highresolution images are presented.
Connected Segmentation Tree — A joint representation of region layout and hierarchy

This paper proposes a new object representation, called connected segmentation tree (CST), which captures canonical characteristics of the object in terms of the photometric, geometric, and spatial adjacency and containment properties of its constituent image regions. CST is obtained by augmenting the objectpsilas segmentation tree (ST) with inter-region neighbor links, in addition to their recursive embedding structure already present in ST. This makes CST a hierarchy of region adjacency graphs. A regionpsilas neighbors are computed using an extension to regions of the Voronoi diagram for point patterns. Unsupervised learning of the CST model of a category is formulated as matching the CST graph representations of unlabeled training images, and fusing their maximally matching subgraphs. A new learning algorithm is proposed that optimizes the model structure by simultaneously searching for both the most salient nodes (regions) and the most salient edges (containment and neighbor relationships of regions) across the image graphs. Matching of the category model to the CST of a new image results in simultaneous detection, segmentation and recognition of all occurrences of the category, and a semantic explanation of these results.
Piecewise Approximation of Pictures Using Maximal Neighborhoods

Suppose that we are given a picture having approximately piecewise constant gray leveL Each point P has a largest neighborhood N(P) that is entirely contained in one of the constant regions, and the set of maximal N(P)'s (i.e., N(P)'s not contained in other N(P)'s) constitutes an economical description of the picture, generalizing the Blum "skeleton" or medial axis transformation. This description can be used to construct approximations to the picture (e.g., by discarding small N(P)'s). The picture can be smoothed, without excessive blurring, by averaging over each N(P). By taking differences between pairs of touching maximal N(P)'s, the edges between the regions can be detected; since this edge detection scheme is not based on symmetrical detection operators, it is not handicapped when two adjacent regions differ greatly in size.