nils-herrmann/clean_scirepeval huggingface dataset:
Fusion of Median and Bilateral Filtering for Range Image Upsampling

We present a new upsampling method to enhance the spatial resolution of depth images. Given a low-resolution depth image from an active depth sensor and a potentially high-resolution color image from a passive RGB camera, we formulate it as an adaptive cost aggregation problem and solve it using the bilateral filter. The formulation synergistically combines the median and bilateral filters thus it better preserves the depth edges and is more robust to noise. Numerical and visual evaluations on a total of 37 Middlebury data sets demonstrate the effectiveness of our method. A real-time high-resolution depth capturing system is also developed using commercial active depth sensor based on the proposed upsampling method.
SDG Cut: 3D Reconstruction of Non-lambertian Objects Using Graph Cuts on Surface Distance Grid

We show that the approaches to 3D reconstruction that use volumetric graph cuts to minimize a cost function over the object surface have two types of biases, the minimal surface bias and the discretization bias. These biases make it difficult to recover surface extrusions and other details, especially when a non-lambertian photo-consistency measure is used. To reduce these biases, we propose a new iterative graph cuts based algorithm that operates on the Surface Distance Grid (SDG), which is a special discretization of the 3Dspace, constructed using a signed distance transform of the current surface estimate. It can be shown that SDG significantly reduces the minimal surface bias, and transforms the discretization bias into a controllable degree of surface smoothness. Experiments on 3D reconstruction of non-lambertian objects confirm the effectiveness of our algorithm over previous methods.
Unsupervised Segmentation of Objects using Efficient Learning

We describe an unsupervised method to segment objects detected in images using a novel variant of an interest point template, which is very efficient to train and evaluate. Once an object has been detected, our method segments an image using a conditional random field (CRF) model. This model integrates image gradients, the location and scale of the object, the presence of object parts, and the tendency of these parts to have characteristic patterns of edges nearby. We enhance our method using multiple unsegmented images of objects to learn the parameters of the CRF, in an iterative conditional maximization framework. We show quantitative results on images of real scenes that demonstrate the accuracy of segmentation.
POCS based adaptive image magnification

We tackle the problem of magnifying an image without incurring blurring, ringing or other artifacts common to classical schemes. The proposed iterative scheme starts with an initial magnified image generated by a process of selective interpolation. By placing suitable constraints on the final magnified image, which are convex in nature, we show that magnification can be posed as a problem of finding a solution which lies at the intersection of convex sets. By avoiding explicit high frequency enhancing assumptions in the iterative process, we avoid edge enhancement artifacts in the magnified image.
Location- and Density-Based Hierarchical Clustering Using Similarity Analysis

This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical location- and density-based clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The approach is applied to a two-step texture analysis, where points represent centroid and average color of the regions in image segmentation.
Robust predictive coding and the Wyner-Ziv problem

The problem of robust communication of predictive encoded video in a joint source-channel setting is addressed. Specifically, the problem of predictive mismatch, where there is a drift between the state of the encoder and the decoder is addressed as a variant of the Wyner-Ziv problem. A video encoding algorithm based on the H.26L video codec, which presents the propagation of error in predictively encoded video in the event of predictive mismatch (or drift) between the encoder and the decoder, is proposed. One of the main advantages of the proposed approach is that there is minimal loss in performance over the standard H.26L encoder during error-free transmission, while simultaneously allowing error recovery in the event of errors. Using turbo codes as coset codes, the performance of the proposed codec is evaluated and the efficacy of the proposed framework is demonstrated. The performance of the proposed approach can only improve with the use of superior coset codes.
Online learning with kernels: Overcoming the growing sum problem

Online kernel algorithms have an important computational drawback. The computational complexity of these algorithms grow linearly over time. This makes these algorithms difficult to use for real time signal processing applications that need to continuously process data over prolonged periods of time. In this paper, we present a way of overcoming this problem. We do so by approximating kernel evaluations using finite dimensional inner products in a randomized feature space. We apply this idea to the Kernel Least Mean Square (KLMS) algorithm, that has recently been proposed as a non-linear extension to the famed LMS algorithm. Our simulations show that using the proposed method, constant computational complexity can be achieved, with no observable loss in performance.
A high-resolution panoramic camera

Wide field of view (FOV) and high resolution are two desirable properties in many vision-based applications such as tele-conferencing, surveillance, and robot navigation. In some applications, such as 3D reconstruction and rendering, it is also desired that all viewing directions share a single viewpoint, the entire FOV be imaged simultaneously, in real-time, and the depth of field be large. In this paper, we review such a panoramic camera proposed by Nalwa in 1996 that uses reflections off planar mirrors to achieve the first four of the aforementioned capabilities He uses a single mirror pyramid (SMP) and a number of cameras that point to the individual pyramid faces. Together the cameras yield a visual field having a width of 360 degrees and a height same as that of the individual cameras. We propose a double mirror-pyramid (DALP) design that still achieves a 360-degree FOV horizontally but doubles the vertical FOV. It retains the other three capabilities namely high resolution, a single apparent viewpoint across the entire FOV, and real-time panoramic capture. We specify the visual field mapping from the scene to the sensor realized by, the proposed camera. Finally, an implementation of the proposed DMP design is described and examples of preliminary panoramic images obtained are included.
An Omnidirectional Stereo Vision System Using a Single Camera

We describe a new omnidirectional stereo imaging system that uses a concave lens and a convex mirror to produce a stereo pair of images on the sensor of a conventional camera. The light incident from a scene point is split and directed to the camera in two parts. One part reaches camera directly after reflection from the convex mirror and forms a single-viewpoint omnidirectional image. The second part is formed by passing a subbeam of the reflected light from the mirror through a concave lens and forms a displaced single viewpoint image where the disparity depends on the depth of the scene point. A closed-form expression for depth is derived. Since the optical components used are simple and commercially available, the resulting system is compact and inexpensive. This, and the simplicity of the required image processing algorithms, make the proposed system attractive for real-time applications, such as autonomous navigation and object manipulation. The experimental prototype we have built is described
Robust Visual Tracking Using Oblique Random Forests

Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much needed for online tracking. In this paper, we propose a discriminative tracker based on a novel incremental oblique random forest. Unlike conventional orthogonal decision trees that use a single feature and heuristic measures to obtain a split at each node, we propose to use a more powerful proximal SVM to obtain oblique hyperplanes to capture the geometric structure of the data better. The resulting decision surface is not restricted to be axis aligned and hence has the ability to represent and classify the input data better. Furthermore, in order to generalize to online tracking scenarios, we derive incremental update steps that enable the hyperplanes in each node to be updated recursively, efficiently and in a closed-form fashion. We demonstrate the effectiveness of our method using two large scale benchmark datasets (OTB-51 and OTB-100) and show that our method gives competitive results on several challenging cases by relying on simple HOG features as well as in combination with more sophisticated deep neural network based models. The implementations of the proposed random forest are available at https://github.com/ZhangLeUestc/ Incremental-Oblique-Random-Forest.
Integrating multiresolution image acquisition and coarse-to-fine surface reconstruction from stereo

The authors are concerned with the problems of reconstructing surfaces from stereo images for large scenes having large depth ranges. They present an approach to acquiring coarse structural information about the scene in the vicinity of the next fixation point during the current fixation and utilizing this information for surface reconstruction in the vicinity of the next fixation point. The approach involves the processing of peripheral low-resolution parts of the current images away from the image center, in additive to accurate surface estimation from the central high-resolution parts containing the fixated object. The processing of the low-resolution parts yields coarse surface estimates to be refined after the cameras have refixated and the parts of the scene around the new fixation point (currently at low resolution) are imaged more sharply. The coarse estimates are obtained from both stereo and focus. The choice as to which estimate is actually used depends on which is determined to be more accurate in the given situation. Experimental results demonstrate that the approach may be well suited for active data acquisition and surface reconstruction by an autonomous stereo system.<<ETX>>
Multiprocessor Pyramid Architectures for Bottom-Up Image Analysis

This paper describes three hierarchical organizations of small processors for bottom-up image analysis:pyramids, interleaved pyramids, and pyramid trees. Progressively lower levels in the hierarchies process image windows of decreasing size. Bottom-up analysis is made feasible by transmitting up the levels quadrant borders and border-related information that captures quadrant interaction of interest for a given computation. The operation of the pyramid is illustrated by examples of standard algorithms for interior-based computations (e.g., area) and border-based computations of local properties (e.g., perimeter). A connected component counting algorithm is outlined that illustrates the role of border-related information in representing quadrant interaction. Interleaved pyramids are obtained by sharing processors among several pyramids. They increase processor utilization and throughput rate at the cost of increased hardware. Trees of shallow interleaved pyramids, calld pyramid trees, are introduced to reduce the hardware requirements of large interleaved pyramids at the expense of increased processing time, without sacrificing processor utilization. The three organizations are compared with respect to several performance measures.
Calibration of a head-mounted projective display for augmented reality systems

In augmented reality (AR) applications, registering a virtual object with its real counterpart accurately and comfortably is one of the basic and challenging issues in the sense that the size, depth, geometry, as well as physical attributes of the virtual objects have to be rendered precisely relative to a physical reference, which is well known as the calibration or registration problem. This paper presents a systematic calibration process to address static registration in a custom-designed augmented reality system, which is based upon the recent advancement of head-mounted projective display (HMPD) technology. Following a concise review of the HMPD concept and system configuration, we present in detail a computational model for system calibration, describe the calibration procedures to obtain estimations of the unknown transformations, and include the calibration results, evaluation experiments and results.
Dense shape and motion from region correspondences by factorization

In this paper, we propose an algorithm for estimating dense shape and motion of dynamic piecewise planar scenes from region correspondences using factorization. Region correspondences are used since they are easier to establish and more reliable than either line or point correspondences. The image measurements required are the centroid and area for each region. Singular value decomposition is employed to find the basis of range space of the motion, shape, and surface normal matrices. By imposing model constraints, motion, shape, and surface normal can be recovered only from region correspondences.
Optimal motion and structure estimation

The problem of estimating motion and structure of a rigid scene from two perspective monocular views is studied. The optimization approach presented is motivated by the following observations of linear algorithms: (1) for certain types of motion, even pixel-level perturbations (such as digitization noise) may override the information characterized by epipolar constraint; (2) existing linear algorithms do not use the constraints in the essential parameter matrix E in solving for this matrix. The authors present approaches to estimating errors in the optimal solutions, investigate the theoretical lower bounds on the errors in the solutions and compare them with actual errors, and analyze two types of algorithms of optimization: batch and sequential. The analysis and experiments show that, in general, a batch technique performs better than a sequential technique for any nonlinear problems. A recursive batch processing technique is proposed for nonlinear problems that require recursive estimation.<<ETX>>
Path planning using a potential field representation

An approach to two-dimensional as well as three-dimensional findpath problems that divides each problem into two steps is presented. Rough paths are found based only on topological information. This is accomplished by assigning to each obstacle an artificial potential similar to electrostatic potential to prevent the moving object from colliding with the obstacles, and then locating minimum-potential valleys. The paths defined by the minimum-potential valleys are modified to obtain an optimal collision-free path and orientations of the moving object along the path. Three algorithms are given to accomplish this second step. These three algorithms based on potential fields are nearly complete in scope, and solve a large variety of problems.<<ETX>>
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection

An approach is described that integrates the processes of feature matching, contour detection, and surface interpolation to determine the three-dimensional distance, or depth, of objects from a stereo pair of images. Integration is necessary to ensure that the detected surfaces are smooth. Surface interpolation takes into account detected occluding and ridge contours in the scene; interpolation is performed within regions enclosed by these contours. Planar and quadratic patches are used as local models of the surface. Occluded regions in the image are identified, and are not used for matching and interpolation. A coarse-to-fine algorithm is presented that generates a multiresolution hierarchy of surface maps, one at each level of resolution. Experimental results are given for a variety of stereo images.
Single image super-resolution using adaptive domain transformation

In this paper we propose a new image domain prior term for regularizing the super-resolution reconstruction algorithm. This term encourages preserving the local ramp structure around edges, in the reconstruction algorithm. Ramp at a pixel is defined as the steepest sequence of monotonically increasing (or decreasing) pixels among all feasible directions around the pixel. As described in previous work, ramp based modeling is a richer characterization of local image structure than conventional gradients. Our proposed ramp-preserving constraint image is obtained by first running an accurate segmentation algorithm (which is itself obtained by ramp based modeling) on the low resolution image. We then perform a domain transformation of the pixels belonging to the steepest ramps at the edge pixels, in order to preserve sharpness. The resulting non-uniformly spaced image is then upscaled to a uniform, high resolution grid, using an edge preserving non-uniform interpolation scheme. This image is then used both as the prior constraint as well as the initial guess for the iterative super-resolution reconstruction algorithm. Our results compare favorably to the classical back-projection algorithm as well as newer methods which use learning based gradient domain priors.
Cresceptron: a self-organizing neural network which grows adaptively

Cresceptron uses a hierarchical framework to grow neural networks automatically, adaptively, and incrementally through learning. At every level of the hierarchy, new concepts are detected automatically and the network grows by creating new neurons and synapses which memorize the new concepts and their context. The training samples are generalized to other perceptually equivalent items through hierarchical tolerance of deviation. The neural network recognizes the learned items and their variations by hierarchically associating the learned knowledge with the input. It segments the recognized items from the input through back training along the response paths.<<ETX>>
Learning subcategory relevances for category recognition

A real-world object category can be viewed as a characteristic configuration of its parts, that are themselves simpler, smaller (sub)categories. Recognition of a category can therefore be made easier by detecting its constituent subcategories and combing these detection results. Given a set of training images, each labeled by an object category contained in it, we present an approach to learning: (1) Taxonomy defined by recursive sharing of subcategories by multiple image categories; (2) Subcategory relevance as the degree of evidence a subcategory offers for the presence of its parent; (3) Likelihood that the image contains a subcategory; and (4) Prior that a subcategory occurs. The images are represented as points in a feature space spanned by confidences in the occurrences of the subcategories. The subcategory relevances are estimated as weights, necessary to rescale the corresponding axes of the feature space so that the images with the same label are closer to each other than to those with different labels. When a new image is encountered, the learned taxonomy, relevances, likelihoods, and priors are used by a linear classifier to categorize the image. On the challenging Caltech-256 dataset, the proposed approach significantly outperforms the best categorizations reported. This result is significant in that it not only demonstrates the advantages of exploiting subcategory taxonomy for recognition, but also suggests that a feature space spanned by part properties, instead of direct object properties, allows for linear separation of image classes.
Gradient Adaptive Image Restoration and Enhancement

Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.
Improving the efficiency and effectiveness of railcar safety appliance inspection using machine vision technology

Before a train departs a yard, many aspects of the freight cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the interface between humans and railcars during transportation. Federal safety rules govern the design and condition of safety appliances. The current car inspection process is primarily visual making it laborious, redundant, and generally lacking of memory. There exists a potential to increase both the effectiveness and efficiency of safety appliance inspections by utilizing machine vision technology to enhance the railcar inspection process. Machine vision consists of capturing digital video and using algorithms capable of detecting and analyzing the particular objects or patterns of interest. Computer algorithms can objectively inspect railcars without tiring or becoming distracted and can also focus on certain parts of the railcar not easily seen by an inspector on the ground. Thus far, algorithms have been developed that can detect deformed ladders, handholds, and brake wheels on open-top gondolas and hoppers. Next, visual learning will be employed to teach the algorithm the differences between Federal Railroad Administration (FRA) safety appliance defects and other types of deformation not requiring a car to be bad ordered. The final product will be a wayside inspection system capable of detecting safety appliance defects on passing railcars.