nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised multidimensional hierarchical clustering

A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The number of clusters and the parameters for clustering are determined automatically and adaptively for a given data set which makes it unsupervised and non-parametric. The method is simple, noniterative and requires low computation. Results on various sample data sets are presented.
Using a head-mounted projective display in interactive augmented environments

Head-mounted projective displays (HMPD) have been recently proposed as an alternative to conventional eyepiece-type head-mounted displays (HMDs). HMPDs consist of a pair of miniature projection lenses, beamsplitters, and displays mounted on the helmet and retro-reflective sheeting materials placed strategically in the environment. The HMPD technology is first reviewed briefly, which includes its features and capabilities and a comparison with conventional visualization techniques, as well as our recent implementation of a compact HMPD prototype. Then we present some preliminary findings on retro-reflective materials and discuss a framework for collaborative AR environments, which supports at least three modes of collaboration: interactive local collaboration in an AR environment, passive distant collaboration, and interactive distant collaboration. Finally, two preliminary application examples of the HMPD technology for interactive collaboration in augmented environments are included, which demonstrate some of the HMPD characteristics and embody the framework for distant collaboration.
Matching Two Perspective Views

A computational approach to image matching is described. It uses multiple attributes associated with each image point to yield a generally overdetermined system of constraints, taking into account possible structural discontinuities and occlusions. In the algorithm implemented, intensity, edgeness, and cornerness attributes are used in conjunction with the constraints arising from intraregional smoothness, field continuity and discontinuity, and occlusions to compute dense displacement fields and occlusion maps along the pixel grids. The intensity, edgeness, and cornerness are invariant under rigid motion in the image plane. In order to cope with large disparities, a multiresolution multigrid structure is employed. Coarser level edgeness and cornerness measures are obtained by blurring the finer level measures. The algorithm has been tested on real-world scenes with depth discontinuities and occlusions. A special case of two-view matching is stereo matching, where the motion between two images is known. The algorithm can be easily specialized to perform stereo matching using the epipolar constraint.
Learning recognition and segmentation of 3-D objects from 2-D images

A framework called Cresceptron is introduced for automatic algorithm design through learning of concepts and rules, thus deviating from the traditional mode in which humans specify the rules constituting a vision algorithm. With the Cresceptron, humans as designers need only to provide a good structure for learning, but they are relieved of most design details. The Cresceptron has been tested on the task of visual recognition by recognizing 3-D general objects from 2-D photographic images of natural scenes and segmenting the recognized objects from the cluttered image background. The Cresceptron uses a hierarchical structure to grow networks automatically, adaptively, and incrementally through learning. The Cresceptron makes it possible to generalize training exemplars to other perceptually equivalent items. Experiments with a variety of real-world images are reported to demonstrate the feasibility of learning in the Cresceptron.<<ETX>>
Texel-based texture segmentation

Given an arbitrary image, our goal is to segment all distinct texture subimages. This is done by discovering distinct, cohesive groups of spatially repeating patterns, called texels, in the image, where each group defines the corresponding texture. Texels occupy image regions, whose photometric, geometric, structural, and spatial-layout properties are samples from an unknown pdf. If the image contains texture, by definition, the image will also contain a large number of statistically similar texels. This, in turn, will give rise to modes in the pdf of region properties. Texture segmentation can thus be formulated as identifying modes of this pdf. To this end, first, we use a low-level, multiscale segmentation to extract image regions at all scales present. Then, we use the meanshift with a new, variable-bandwidth, hierarchical kernel to identify modes of the pdf defined over the extracted hierarchy of image regions. The hierarchical kernel is aimed at capturing texel substructure. Experiments demonstrate that accounting for the structural properties of texels is critical for texture segmentation, leading to competitive performance vs. the state of the art.
Estimating motion and structure from line matches: performance obtained and beyond

The performance issues of estimating motion and structure from line correspondences are studied. An approach to optimal estimation of motion and structure using line correspondences is presented. To minimize the expected errors in the estimated parameters, it is necessary to minimize the matrix-weighted discrepancy between the computed lines and the observed lines. In order to reliably reach the global minimum solution, a closed-form solution is computed and then used as the initial starting condition for an iterative optimal estimation algorithm. Simulation results show that, in the presence of noise, the accuracy of the optimal solution is not only considerably better than that of the closed-form solutions, but it has also reached a level that it is comparable with that of point-based optimal algorithms. Simulations also show that the error of the optimal solution is close to a theoretical lower error bound, the Cramer-Rao bound, which implies that there exists little room for accuracy improvement beyond the performance obtained.<<ETX>>
Estimating motion/structure from line correspondences: a robust linear algorithm and uniqueness theorems

A closed-form solution to motion and structure from line correspondences in monocular perspective image sequences is presented. The algorithm requires a minimum of 13 lines over three perspective views. Redundancy in the data provides overdetermination to combat noise. The estimates can be used as an initial guess for further optimization. A unique solution to motion and structure is guaranteed if and only if the line configuration is not degenerate and the translation between any two views does not vanish. Necessary and sufficient conditions for degenerate spatial line configurations have been derived. Simulations are performed which show the performance of the algorithm in the presence of noise.<<ETX>>
Learning the Taxonomy and Models of Categories Present in Arbitrary Images

This paper proposes, and presents a solution to, the problem of simultaneous learning of multiple visual categories present in an arbitrary image set and their inter-category relationships. These relationships, also called their taxonomy, allow categories to be defined recursively, as spatial configurations of (simpler) subcategories each of which may be shared by many categories. Each image is represented by a segmentation tree, whose structure captures recursive embedding of image regions in a multiscale segmentation, and whose nodes contain the associated region properties. The presence of any occurring categories is reflected in the occurrence of associated, similar subtrees within the image trees. Similar subtrees across the entire image set are clustered. Each cluster corresponds to a discovered category, represented by the cluster properties. A (subcategory) cluster of small matching subtrees may occur within multiple clusters (categories) of larger matching subtrees, in different spatial relationships with subtrees from other small clusters. Such recursive embedding, grouping and intersection of clusters is captured in a directed acyclic graph (DAG) which represents the discovered taxonomy. Detection, recognition and segmentation of any of the learned categories present in a new image are simultaneously conducted by matching the segmentation tree of the new image with the learned DAG. This matching also yields a semantic explanation of the recognized category, in terms of the presence of its subcategories. Experiments with a newly compiled dataset of four-legged animals demonstrate good cross-category resolvability.
Extracting Subimages of an Unknown Category from a Set of Images

Suppose a set of images contains frequent occurrences of objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: (1) unsupervised identification of photometric, geometric, and topological (mutual containment) properties of multiscale regions defining objects in the category; (2) learning a region-based structural model of the category in terms of these properties from a set of training images; and (3) segmentation and recognition of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to find the maximally matching subtrees across the set, the existence of which is itself viewed as evidence that a category is indeed present. The matched subtrees are fused into a canonical tree, which represents the learned model of the category. Recognition of objects in a new image and image segmentation delineating all object parts are achieved simultaneously by finding matches of the model with subtrees of the new image. Experimental comparison with state-of-the-art methods shows that the proposed approach has similar recognition and superior localization performance while it uses fewer training examples.
Design Analysis of a High-Resolution Panoramic Camera Using Conventional Imagers and a Mirror Pyramid

Wide field of view (FOV) and high-resolution image acquisition is highly desirable in many vision-based applications. Several systems have reported the use of reflections off mirror pyramids to capture high-resolution, single-viewpoint, and wide-FOV images. Using a dual mirror pyramid (DMP) panoramic camera as an example, in this paper, we examine how the pyramid geometry, and the selection and placement of imager clusters can be optimized to maximize the overall panoramic FOV, sensor utilization efficiency, and image uniformity. The analysis can be generalized and applied to other pyramid-based designs
Mean-shift segmentation with wavelet-based bandwidth selection

Recently, various non-linear techniques for segmentation have been proposed based on non-parametric density estimation. These approaches model image data as clusters of pixels in the combined range-domain space, using kernel based techniques to represent the underlying, multi-modal Probability Density Function (PDF). In Mean-shift based segmentation, pixel clusters or image segments are identified with unique modes of the multi-modal PDF by mapping each pixel to a mode using a convergent, iterative process. The advantages of such approaches include flexible modeling of the image and noise processes and consequent robustness in segmentation. An important issue is the automatic selection of scale parameters a problem far from satisfactorily addressed. In this paper, we propose a regression-based model which admits a realistic framework to choose scale parameters. Results on real images are presented.
Improving head and body pose estimation through semi-supervised manifold alignment

In this paper, we explore the use of a semi-supervised manifold alignment method for domain adaptation in the context of human body and head pose estimation in videos. We build upon an existing state-of-the-art system that leverages on external labelled datasets for the body and head features, and on the unlabelled test data with weak velocity labels to do a coupled estimation of the body and head pose. While this previous approach showed promising results, the learning of the underlying manifold structure of the features in the train and target data and the need to align them were not explored despite the fact that the pose features between two datasets may vary according to the scene, e.g. due to different camera point of view or perspective. In this paper, we propose to use a semi-supervised manifold alignment method to bring the train and target samples closer within the resulting embedded space. To this end, we consider an adaptation set from the target data and rely on (weak) labels, given for example by the velocity direction whenever they are reliable. These labels, along with the training labels are used to bias the manifold distance within each manifold and to establish correspondences for alignment.
A new imaging model

This paper has been prompted by observations of some anomalies in the performance of the standard imaging models (pin-hole, thin-lens and Gaussian thicklens), in the context of composing omnifocus images and estimating depth maps from a sequence of images. A closer examination of the models revealed that they assume a position of the aperture that conflicts with the designs of many available lenses. We have shown in this paper that the imaging geometry and photometric properties of an image are significantly influenced by the position of the aperture. This is confirmed by the discrepancies between observed mappings and those predicted by the models. We have therefore concluded that the current imaging models do not adequately represent practical imaging systems. We have proposed a new imaging model which overcomes these deficiencies and have given the associated mappings. The impact of this model on some common imaging scenarios is described, along with experimental verification of the better performance of the model on three real lenses.
Wyner-Ziv coding of video: an error-resilient compression framework

This paper addresses the problem of video coding in a joint source-channel setting. In particular, we propose a video encoding algorithm that prevents the indefinite propagation of errors in predictively encoded video-a problem that has received considerable attention over the last decade. This is accomplished by periodically transmitting a small amount of additional information, termed coset information, to the decoder, as opposed to the popular approach of periodic insertion of intra-coded frames. Perhaps surprisingly, the coset information is capable of correcting for errors, without the encoder having a precise knowledge of the lost packets that resulted in the errors. In the context of real-time transmission, the proposed approach entails a minimal loss in performance over conventional encoding in the absence of channel losses, while simultaneously allowing error recovery in the event of channel losses. We demonstrate the efficacy of the proposed approach through experimental evaluation. In particular, the performance of the proposed framework is 3-4 dB superior to the conventional approach of periodic insertion of intra-coded frames, and 1.5-2 dB away from an ideal system, with infinite decoding delay, operating at Shannon capacity.
Digital image watermarking: issues in resolving rightful ownership

In the literature many strategies for attacking and subverting a watermark have been presented. Such attacks suggest that the ability to embed non-erasable watermarks does not necessarily imply that the watermarking scheme can be used to establish ownership. The main aim of this paper is to formulate necessary and sufficient requirements for a watermarking scheme to be able to resolve rightful ownership. It is shown that some of the popular schemes proposed in literature for watermarking images do not satisfy these requirements. Finally, we show that a modification of a watermarking scheme in literature (Piva et al., 1997) performs satisfactorily.
Dense stereo matching using kernel maximum likelihood estimation

There has been much interest, recently, in the use of Bayesian formulations for solving image correspondence problems. For the two-view stereo matching problem, typical Bayesian formulations model the disparity prior as a pairwise Markov random field (MRF). Approximate inference algorithms for MRFs, such as graph cuts or belief propagation, treat the stereo matching problem as a labelling problem yielding discrete valued disparity estimates. In this paper, we propose a novel robust Bayesian formulation based on the recently proposed kernel maximum likelihood (KML) estimation framework. The proposed formulation uses probability density kernels to infer the posterior probability distribution of the disparity values. We present an efficient iterative algorithm, which uses a variational approach to form a KML estimate from the inferred distribution. The proposed algorithm yields continuous-valued disparity estimates, and is provably convergent. The proposed approach is validated on standard stereo pairs, with known sub-pixel disparity ground-truth data.
Uniformity and homogeneity-based hierarchical clustering

This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (n/sub f/-dimensional) function in a n/sub s/-dimensional space (n/sub s/+n/sub f/=n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in n/sub f/-dimensional space is performed to detect the sets of dots in n-dimensional space having similar n/sub f/-variate function values (location based clustering using a homogeneity model). Clustering in n/sub s/ dimensional space is performed to detect the sets of dots in n-dimensional space having similar interneighbor distances (density based clustering with a uniformity model). Clusters in the n-dimensional space are obtained by combining the results in the two subspaces.
Omnifocused 3D display using the nonfrontal imaging camera

The Nonfrontal Imaging Camera (NICAM) simultaneously provides omnifocused intensity and range panoramics (i.e., panoramic intensity images and depth maps). In this paper, we describe the estimation of range which is based on focusing. An analysis of errors in the range estimates is presented along with examples of experimental results. The image and depth map are combined to approximate and reconstruct pseudostereo pairs from NICAM's viewpoint for use in 3D displaying of the scene. Estimates of such stereo views can be used to advantage in virtual reality environments.
A direct data approximation based motion estimation algorithm

A novel algorithm for estimating motion and structure from orthographic projections is presented. The estimate is based on the image locations of a set of points on a rigid object viewed in many frames. The object can have an arbitrary shape. Motion is assumed to consist of rotation about a fixed-direction axis and linear translation, each at a constant rate. The algorithm uses closed-form expressions to find estimates of the motion and the structure parameters and can accept any number of points over any number of frames as input. The performance of the algorithm on synthetic data generated to represent a range of motion and structure conditions and on data extracted from real images is evaluated. It is shown that the algorithm produces accurate results from synthetic data given the positions of 10 points over 10 frames, with a total rotation of 90 degrees and with uniformly distributed noise of 0.9%.<<ETX>>
A transform for detection of multiscale image structure

A new transform is introduced to facilitate integrated edge and region detection in an image at all geometric and photometric scales at which structure is present. The proposed transform allows the structure to emerge from interactions among the pixels. A pixel's interaction with all other pixels is considered instead of testing specific neighborhoods for a specific structure. Computations are performed on pairs of pixels followed by vector integration of the results, rather than scalar, weighted averaging over pixel neighborhoods. For pixels on either side of a region boundary, the transformation yields high affinities, but there is little affinity between pixels across regions.<<ETX>>
Predictive encoding using coset codes

Predictive encoding with respect to multiple possible predictors is a common scenario encountered in many digital set-top box applications, such as redundant storage of video/audio data, real-time robust communication with peripherals and Internet video/audio telephony. A key problem associated with this scenario is that of predictive mismatch or drift. In the present paper, we pose the problem of predictive encoding with multiple possible predictors as a variant of the well-known Wyner-Ziv side-information problem. We propose an approach based on the use of coset codes for predictive encoding, for mitigating the effect of drift without overly sacrificing compression efficiency. The proposed approach can be used to improve coding performance in a wide range of practical applications such as multiple description coding, scalable coding and redundant storage of video/audio streams. We illustrate the efficacy of the proposed approach through a simple example based on the application of low-delay Internet telephony. Our results indicate that the proposed approach significantly outperforms conventional predictive encoding for communication over lossy channels.
An Adaptive Threshold Logic Gate Using Capacitive Analog Weights

A physical realization of an adaptive threshold logic gate which can be used to realize linearly separable switching functions is presented. The system is completely electronic and is easily implemented in the sense that standard components may be used throughout. The memory circuit used for each weight of the device stabilizes the voltage across a capacitor by means of a sampling technique. Over the range that the capacitor voltage is stabilized, the net average capacitor current is zero at a discrete number of stable voltages which can be made numerous enough to approximate analog memory. The device exhibits both long-term stability and ease in weight adjustment. Training results are given for several linearly separable switching functions. This circuit shows that a practical adaptive threshold logic gate can be realized. The incorporation of integrated circuitry in the device presented would greatly enhance the feasibility of building more complex trainable machines.
GPLS VME module: a diagnostic and display tool for NSLS micro systems

The General Purpose Light Source VME module is an integral part of every front-end micro in the NSLS control system. The board incorporates features such as a video character generator, clock signals, time-of-day clock, a VME bus interrupter and general-purpose digital inputs and outputs. This module serves as a valuable diagnostic and real-time display tool for the micro development as well as for the final operational systems. This paper describes the functions provided by the board for the NSLS micro control monitor software.