nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive integration of depth and color for objectness estimation

The goal of objectness estimation is to predict a moderate number of proposals of all possible objects in a given image with high efficiency. Most existing works solve this problem solely in conventional 2D color images. In this paper, we demonstrate that the depth information could benefit the estimation as a complementary cue to color information. After detailed analysis of depth characteristics, we present an adaptively integrated description for generic objects, which could take full advantages of both depth and color. With the proposed objectness description, the ambiguous area, especially the highly textured regions in original color maps, can be effectively discriminated. Meanwhile, the object boundary areas could be further emphasized, which leads to a more powerful objectness description. To evaluate the performance of the proposed approach, we conduct the experiments on two challenging datasets. The experimental results show that our proposed objectness description is more powerful and effective than state-of-the-art alternatives.
OBSIR: Object-based stereo image retrieval

Recent years, the stereo image has become an emerging media in the field of 3D technology, which leads to an urgent demand of stereo image retrieval. In this paper, we attempt to introduce a framework for object-based stereo image retrieval (OBSIR), which retrieves images containing the similar objects to the one captured in the query image by the user. The proposed approach consists of both online and offline procedures. In the offline procedure, we propose a salient object segmentation method making use of both color and depth to extract objects from each image. The extracted objects are then represented by multiple visual feature descriptors. In order to improve the image search efficiently, we construct an approximate nearest neighbor (ANN) index using cluster-based locality sensitive hashing (LSH). In the online stage, the user may supply the query object by selecting a region of interest (ROI) in the query image, or clicking one of the objects recommended by the salient object detector. For the image retrieval evaluation we build a new dataset containing over 10K stereo images. The experiments on this dataset show that the proposed method can effectively recommend the correct object and the final retrieval result is also better than other baseline methods.
A new filtering method for ultrasound images incorporating prior statistics concerning medical features

20 to 50 percent of neonates with very low birth weight (VLBW:<1500 g) suffer from white matter damage (leukomalacia). Nowadays the diagnosis of WMD is still solely dependent on the visual interpretation by an expert. A need for a (semi-) computerized way of segmenting the affected regions, in order to make quantitative measurements as an aid to the subjective diagnosis, is felt. Applying active contours for this purpose, is a classical approach. The performance of active contours for this purpose, however, is heavily deteriorated by the presence of speckle noise. In this paper a new filter, taking into account local statistics in the image, is proposed; it removes a significant amount of speckle noise in the healthy parts, while it makes the areas affected by WMD more uniform, thus severely improving the performance of the active contour. The results show that applying an active contour after the proposed technique yields a segmentation much closer to that of an expert.
Recursive temporal denoising and motion estimation of video

In this paper, we present a new technique for video denoising which is based on a novel motion estimation algorithm. First, a recursive temporal denoising is performed through the estimated motion trajectory. After that, appropriate spatial filtering is done. The proposed algorithm automatically adapts to the detected noise level, provided it is short-tail noise, such as Gaussian noise. It uses a one-level wavelet decomposition where both motion estimation and denoising is performed. The non-decimated transform is used because it is nearly shift invariant and thus yields better motion estimation and denoising results than the decimated transform. The results on different image sequences demonstrate that the proposed filter outperforms the other state-of-the-art filters both in terms of PSNR and visual aspect.
Complex wavelet joint denoising and demosaicing using Gaussian scale mixtures

Wavelet-based demosaicing techniques have the advantage of being computationally relatively fast, while having a reconstruction performance that is similar to state-of-the-art techniques. Because the demosaicing rules are linear, it is fairly simple to integrate denoising into the demosaicing. In this paper, we present a method that performs joint denoising and demosaicing, using a Gaussian Scale Mixture (GSM) prior model, thereby modeling the local edge direction as a hidden variable. The results indicate that this technique offers a better reconstruction performance (in PSNR sense and visually) than sequential demosaicing and denoising. On a recent GPU, our algorithm takes 3.5 s for reconstructing a 12 megapixel RAW digital camera image.
Relevance Criteria for Spatial Information Retrieval Using Error-Tolerant Graph Matching

In this paper, we present a graph-based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image features and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows determining the bounds of error tolerance and, in this way, characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy, the derived bounds of error tolerance are essential
Effects of static and dynamic image noise and background luminance on letter contrast threshold

We performed a pilot psychovisual experiment to determine the contrast threshold and slope of the psychometric function for a target embedded in two levels of static and dynamic external image noise. Sloan letters were presented in a local background surrounded by a global background, both varied over four luminance levels: 58.62, 155.97, 253.50, and 347.47 candela per square meter. Uncorrelated Gaussian noise with normalized standard deviation 0.019 and 0.087 was added to the stimuli. A noise-free stimulus was also tested. No systematic effect of global background luminance was found. The contrast threshold was approximately 1% in the noise-free stimulus and increased monotonically with rms noise contrast, following a power law relationship. Thresholds were higher in static noise. The model will be incorporated in a no-reference, task-based medical quality metric for x-ray sequences.
Quasar — A new heterogeneous programming framework for image and video processing algorithms on CPU and GPU

In image and video processing research, rapid prototyping and testing of different variations of an algorithm is quite essential (e.g., to find out if a given algorithm can solve a given problem or work in real-time). In the past decade, the computational performance of graphical processing units (GPUs) has improved significantly, where speed-up factors of 10×-50× compared to single-threaded CPU execution are not uncommon. However, GPU programming is challenging, requiring a significant programming expertise and moreover, most existing programming approaches are not well suited for rapid prototyping. In this Show & Tell session, we present a new programming framework, aimed at making the bridge between high-level program specification and low-level implementation and optimization on heterogeneous computation devices. The goal is that the programmer is relieved from (most) implementation issues and can focus on the specification and improvement of the algorithms. We present a new prototype domain-specific programming language (in the first place aimed at image and video processing) that provides a uniform programming approach toward different hardware devices, a run-time environment to manage and communicate with the heterogeneous devices and an integrated development environment (IDE). The IDE provides various useful image processing-related debugging and profiling features.
A fast iterative kernel PCA feature extraction for hyperspectral images

A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyperspectral images. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which solves the eigenvectors through iteration. Without performing eigen decomposition on Gram matrix, our method can reduce the space complexity and time complexity greatly. Experimental results were validated in comparison with the standard KPCA and linear version methods.
Optimal camera selection in vision networks for shape approximation

Within a camera network, the contribution of a camera to the observation of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time. An automatic selection of a subset of cameras that significantly contributes to the desired observation of a scene can be of great value for the reduction of the amount of transmitted or stored image data. In this work, we propose low data rate schemes to select from a vision network a subset of cameras that provides a good frontal observation of the persons in the scene and allows for the best approximation of their 3D shape. We also investigate to what degree low data rates trade off quality of reconstructed 3D shapes.
Rate Allocation Algorithm for Pixel-Domain Distributed Video Coding Without Feedback Channel

In some video coding applications, it is desirable to reduce the complexity of the video encoder at the expense of a more complex decoder. Distributed video (DV) coding is a new paradigm that aims to achieve this. To allocate a proper number of bits to each frame, most DV coding algorithms use a feedback channel (FBC). However, in some cases, a FBC does not exist. In this paper, we therefore propose a rate allocation (RA) algorithm for pixel-domain distributed video coders without FBC. Our algorithm estimates at the encoder the number of bits for every frame without significantly increasing the encoder complexity. Experimental results show that our RA algorithm delivers satisfactory estimates of the adequate encoding rate, especially for sequences with little motion.
Image interpolation using constrained adaptive contrast enhancement techniques

In this paper we present a method for interpolating images that also preserves sharp edge information. We concentrate on tackling blurred edges by mapping level curves of the image. Level curves or isophotes are spatial curves with constant intensity. The mapping of these intensities can be seen as a local contrast enhancement problem, therefore we can use contrast enhancement techniques coupled with additional constraints for the interpolation problem. A great advantage of this approach is that the shape of the level set contours is preserved and no explicit edge detection is needed here. Results show an improvement in visual quality: edges are sharper and ringing effects are removed.
Classification of hyperspectral images with very small training size using sparse unmixing

Hyperspectral images are high dimensional while the available number of training samples can be very low. For very small training sizes, classical supervised classification strategies may fail. In this work we propose an alternative, semi-supervised approach which is based on sparse unmixing. In this method, all training samples are gathered in a dictionary and serve as possible endmembers. Unmixing then reveals the relative contributions of the different training samples to an unlabeled sample. Since standard unmixing strategies as the Fully Constrained Linear Spectral Unmixing (FCLSU) typically assume only one endmember per class, we investigate the use of sparse unmixing. In this work, we apply the SunSAL algorithm. We show that this method outperforms SVM classification in the case of extremely small training sizes of only a few samples per class.
Analysing multispectral textures in very high resolution satellite images

With the advent of very high resolution satellite images, such as IKONOS, the question of how we can incorporate textural information in classifying and segmenting different regions has become of great interest. In this work we compare the power of classifying regions based on using color information alone to using texture and color texture information. We use a 2D and 3D extension of the co-occurrence matrix and the features derived from them. In the latter case the effect of color space reduction is also evaluated. We found that although color features perform best in the easy classification tasks, very high classification rates are obtained using color texture features and the fragmentation degree in the classified areas is smaller.
Comparing color and textural information in very high resolution satellite image classification

With the advent of very high resolution satellite images, such as IKONOS, the question of how we can incorporate textural information in classifying and segmenting different regions has become of great interest, in this paper we compare the power of classifying regions based on using color information alone to using texture and color texture information. We use a 2D and 3D extension of the co-occurrence matrix and the features derived from them. In the latter case the effect of color space reduction is also evaluated. We found that although color features perform best in the easy classification tasks, very high classification rates are obtained using color texture features and the fragmentation degree in the classified areas is smaller.
Road detection statistics for automated quality control of GIS data

We examine the use of road detection in VHR satellite images to automate the process of quality assessment of digital road network data. An important aspect is the emphasis on accuracy and reliability of the system. Although road detection has been studied for more than a decade, it is often difficult to assess what performance can be expected over datasets other than the images published. We propose a system to train a road detector based on image examples of typical roads. The system calculates the optimal detection parameters and estimates the performance over the dataset in terms of detection rate and degree of fragmentation. The methodology relies on error propagation and image statistics, and is generic in nature. By showing image examples, variations due to shadow, activity on the road, weather conditions etc. can be taken into account when estimating the expected performance
Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.
Registration of vector data and aerial thermal images using modified mutual information

The topic of registering multi- and hyperspectral imagery is well studied in literature. However when the registration must be done between multispectral images and vector data, the literature is more limited. In this paper we focus on registering aerial images in the thermal (IR) band, and vector data delineating houses and other man-made structures in the same region. This differs from classical registration because first of all, the vector data is not in the same format as the thermal image (i.e. vectors versus pixels), although it can be rasterized initially. Secondly, feature points fail to match between the two representations while intensity based methods also have problems due to the large changes between the rasterized vector and thermal images. We present a method based on a modified version of mutual information that outperforms existing methods for these specific inputs.
A novel tracing method for the segmentation of cell wall networks

Cell wall networks are a common subject of research in biology, which are important for plant growth analysis, organ studies, etc. In order to automate the detection of individual cells in such cell wall networks, we propose a new segmentation algorithm. The proposed method is a network tracing algorithm, exploiting the prior knowledge of the network structure. The method is applicable on multiple microscopy modalities such as fluorescence, but also for images captured using non invasive microscopes such as differential interference contrast (DIC) microscopes.
Real-time, long-term hand tracking with unsupervised initialization

This paper proposes a complete tracking system that is capable of long-term, real-time hand tracking with unsupervised initialization and error recovery. Initialization is steered by a three-stage hand detector, combining spatial and temporal information. Hand hypotheses are generated by a random forest detector in the first stage, whereas a simple linear classifier eliminates false positive detections. Resulting detections are tracked by particle filters that gather temporal statistics in order to make a final decision. The detector is scale and rotation invariant, and can detect hands in any pose in unconstrained environments. The resulting discriminative confidence map is combined with a generative particle filter based observation model to enable robust, long-term hand tracking in real-time. The proposed solution is evaluated using several challenging, publicly available datasets, and is shown to clearly outperform other state of the art object tracking methods.
Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization.

Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps.
Control for Power Gating of Wires

In the deep sub-micron domain wires consume more power than transistors. Power Gating for Wires is a form of bus segmentation that alleviates the power loss from on-chip interconnects, by switching off the supply voltage from inactive drivers, cycle by instruction-cycle. The success of Power Gating for Wires depends much on control: the gain from segmentation can conceivably be undone by control costs. Yet during design exploration, the data required for statistical analysis are not available. A theory of efficient control for Power Gating for Wires and a design framework, determining the balance of cost factors, at an early stage, are both needed. In this paper, we formulate a theory of Useful State Analysis to obtain minimal-redundancy encoding of control information. We establish two figures of merit, based on network topology: Intrinsic Sectioning Gain and Useful Encoding Efficiency. They quantify the power loss reduction achievable, and the success of Useful State Analysis in keeping control costs low. We propose a design pattern for the operation of a control plane, wherein the costs of control can be identified. From use cases, we find that architectures can have an Intrinsic Sectioning Gain of 50% and more. Useful Encoding Efficiency is found to be in a range of 44-80% for some common multipath architectures. Although ultimately, the limits of feasibility to control Power Gating for Wires must be decided by means of statistical analysis, we find Useful State Analysis is applicable to networks with tens of terminals, and that our method of control scales well with increasing network size and complexity.
PhD forum: Multi-view occupancy maps using a network of low resolution visual sensors

An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e.g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter using the occupancy map per time instance as measurement. We found that for a room of 8.8 by 9.2 m, 4 cameras with a resolution as low as 64 by 48 pixels was sufficient to estimate accurate occupancy maps and track up to 4 people. These findings indicate that it is possible to use low resolution visual sensors to build a cheap, power efficient and privacy-friendly system for occupancy monitoring.
Removal of Correlated Noise by Modeling Spatial Correlations and Interscale Dependencies in the Complex Wavelet Domain

We develop a new vector-based shrinkage rule, based on the concept of "signal of interest", for the removal of correlated noise. The multivariate Bessel K form density is used for modeling the spatial correlations between complex wavelet coefficients. The interscale dependencies between the coefficients are captured using a hidden Markov tree model. The combined spatial and interscale model gives improvements over recently proposed hidden Markov models for white noise. The results show that correlated noise is suppressed well while image details are being preserved.