nils-herrmann/clean_scirepeval huggingface dataset:
Adding Gaussian noise to “denoise” JPEG for detecting image resizing

A common problem affecting most image resizing detection algorithms is that they are susceptible to JPEG compression. This is because JPEG introduces periodic artifacts, as it works on 8×8 blocks. We propose a novel yet counter intuitive technique to “denoise” JPEG images by adding Gaussian noise. We add a suitable amount of Gaussian noise to a resized and JPEG compressed image so that the periodicity due to JPEG compression is suppressed while that due to resizing is retained. The controlled Gaussian noise addition works better than median filtering and weighted averaging based filtering for suppressing the JPEG induced periodicity.
Automated tracking and modeling of microtubule dynamics

The method of microtubule tracking and dynamics analysis, presented here, improves upon the current means of manual and automated quantification of microtubule behavior. Key contributions are increasing accuracy and data volume, eliminating user bias and providing advanced analysis tools for the discovery of temporal patterns in cellular processes. By tracking the entire length of each resolvable microtubule, as opposed to only the tip, it is possible to boost dynamics studies with positional information that is virtually impossible to collect manually. We demonstrate the method on the analysis of a microtubule dataset, which was manually tracked and analyzed in the study of betaIII-tubulin isoform. Our results show that automated recognition of temporal patterns in cellular processes offers a highly promising potential
Object browsing and searching in a camera network using graph models

This paper proposes a novel system to assist human image analysts to effectively browse and search for objects in a camera network. In contrast to the existing approaches that focus on finding global trajectories across cameras, the proposed approach directly models the relationship among raw camera observations. A graph model is proposed to represent detected/tracked objects, their appearance and spatial-temporal relationships. In order to minimize communication requirements, we assume that raw video is processed at camera nodes independently to compute object identities and trajectories at video rate. However, this would result in unreliable object locations and/or trajectories. The proposed graph structure captures the uncertainty in these camera observations by effectively modeling their global relationships, and enables a human analyst to query, browse and search the data collected from the camera network. A novel graph ranking framework is proposed for the search and retrieval task, and the absorbing random walk algorithm is adapted to retrieve a representative and diverse set of video frames from the cameras in response to a user query. Preliminary results on a wide area camera network are presented.
A computational approach to boundary detection

A unified approach to boundary perception is presented. The model consists of a hierarchical system which extracts and groups salient features in the image at different spatial scales. In the first stage a Gabor wavelet decomposition provides a representation of the image which is orientation selective, has optimal localization properties, and provides a good model for early feature detection. Following this, local competitive interactions are introduced which help in reducing the effects of noise and illumination variations. Scale interactions help in localizing line ends and corners, and play an important role in boundary perception. The final stage groups similar features aiding in boundary completion. Experimental results on detecting edges, texture boundaries, and illusory contours are provided.<<ETX>>
Improving the quality of depth image based rendering for 3D Video systems

In 3D Video (3DV) applications, a reduced number of views plus depth maps are transmitted or stored. When there is a need to render virtual views in between the actual views, the technique of depth image based rendering (DIBR) can be used to generate the intermediate views. To address the problem of noisy depth information in 3DV systems, we propose novel methods that can be easily incorporated into DIBR to improve synthesized image quality. These include: (1) a heuristic scheme with adaptive spatting that blends multiple warped reference pixels based on their depth, warped pixel positions and camera parameters; (2) an approximation of the first scheme with up-sampling for fast processing; (3) boundary only splatting; and (4) view weighting based on hole distribution. Experiment results show that the proposed methods can improve synthesis quality significantly.
Texture features and learning similarity

This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. With learning similarity the performance of similar pattern retrieval improves significantly. An important aspect of this work is its application to real image data. Texture feature extraction with similarity learning is used to search through large aerial photographs. Feature clustering enables efficient search of the database as our experimental results indicate.
EdgeFlow: a technique for boundary detection and image segmentation

A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. Segmentation results on a large and diverse collections of natural images are provided, demonstrating the usefulness of this method to content based image retrieval.
Data hiding in video

We propose a video data embedding scheme in which the embedded signature data is reconstructed without knowing the original host video. The proposed method enables a high rate of data embedding and is robust to motion compensated coding, such as MPEG-2. Embedding is based on texture masking and utilizes a multi-dimensional lattice structure for encoding signature information. Signature data is embedded in individual video frames using the block DCT. The embedded frames are then MPEG-2 coded. At the receiver both the host and signature images are recovered from the embedded bit stream. We present examples of embedding image and video in video.
Matrix Embedding With Pseudorandom Coefficient Selection and Error Correction for Robust and Secure Steganography

In matrix embedding (ME)-based steganography, the host coefficients are minimally perturbed such that the transmitted bits fall in a coset of a linear code, with the syndrome conveying the hidden bits. The corresponding embedding distortion and vulnerability to steganalysis are significantly less than that of conventional quantization index modulation (QIM)-based hiding. However, ME is less robust to attacks, with a single host bit error leading to multiple decoding errors for the hidden bits. In this paper, we employ the ME-RA scheme, a combination of ME-based hiding with powerful repeat accumulate (RA) codes for error correction, to address this problem. A key contribution of this paper is to compute log likelihood ratios for RA decoding, taking into account the many-to-one mapping between the host coefficients and an encoded bit, for ME. To reduce detectability, we hide in randomized blocks, as in the recently proposed Yet Another Steganographic Scheme (YASS), replacing the QIM-based embedding in YASS by the proposed ME-RA scheme. We also show that the embedding performance can be improved by employing punctured RA codes. Through experiments based on a couple of thousand images, we show that for the same embedded data rate and a moderate attack level, the proposed ME-based method results in a lower detection rate than that obtained for QIM-based YASS.
Robust dynamical model for simultaneous registration and segmentation in a variational framework: A Bayesian approach

We introduce a dynamical model for simultaneous registration and segmentation in a variational framework for image sequences, where the dynamics is incorporated using a Bayesian formulation. A linear stochastic equation relating the tracked object (or a region of interest) is first derived under the assumption that the successive images in the sequence are related by a dense and possibly non-linear displacement field. This derivation allows for the use of a computationally efficient and recursive implementation of the Bayesian formulation in this framework. The contour of the tracked object returned by the dynamical model is not only close to the previously detected shape but is also consistent with the temporal statistics of the tracked object. The performance of the proposed approach is evaluated on real image sequences. It is shown that, with respect to a variety of error metrics such as F-measure, mean absolute deviation and Hausdorff distance, the proposed approach outperforms the state-of-the art approach without the dynamical model.
A curvicylindrical coordinate system for the visualization and segmentation of the ascidian tail

State of the art biological imaging methods, such as confocal microscopy, create 3D volumes by sampling on a cartesian grid. This cartesian coordinate system is often not convenient for visualization and analysis of multi layered organs or tissues. The ascidian embryonic tail, for example, is organized along anterioposterior (AP), dorsoventral (DV) and left-right (LR) axes that are locally orthogonal but curved in the XYZ microscope space. Here, we propose a “curvicylindrical” coordinate system for analysis of such biological structures. By extracting representative paths that traverse different tissue layers, the embryo can be visualized in a small number of 2D images (3 images in the case of the ascidian tail). As we demonstrate, this reduction of the dimensionality from 3D to 2D facilitates the initialization process for high quality segmentation of different cell types, and identification of tissue boundaries.
Probabilistic occlusion boundary detection on spatio-temporal lattices

In this paper, we present an algorithm for occlusion boundary detection. The main contribution is a probabilistic detection framework defined on spatio-temporal lattices, which enables joint analysis of image frames. For this purpose, we introduce two complementary cost functions for creating the spatio-temporal lattice and for performing global inference of the occlusion boundaries, respectively. In addition, a novel combination of low-level occlusion features is discriminatively learnt in the detection framework. Simulations on the CMU Motion Dataset provide ample evidence that proposed algorithm outperforms the leading existing methods.
Graph-Based Topic-Focused Retrieval in Distributed Camera Network

Wide-area wireless camera networks are being increasingly deployed in many urban scenarios. The large amount of data generated from these cameras pose significant information processing challenges. In this work, we focus on representation, search and retrieval of moving objects in the scene, with emphasis on local camera node video analysis. We develop a graph model that captures the relationships among objects without the need to identify global trajectories. Specifically, two types of edges are defined in the graph: object edges linking the same object across the whole network and context edges linking different objects within a spatial-temporal proximity. We propose a manifold ranking method with a greedy diversification step to order the relevant items based on similarity as well as diversity within the database. Detailed experimental results using video data from a 10-camera network covering bike paths are presented.
Image processing in the Alexandria Digital Library project

The management of images, video, and in general, multimedia data, is an important issue in the design of digital libraries. In particular, the following problems stand out: efficient storage, fast retrieval, and protection of intellectual property. We outline below some of the recent advances in image processing in the context of the UCSB Alexandria Digital Library (ADL) project whose goal is to create a database of spatially indexed data. Maps and satellite images are among the main data sets in this project. The focus of this overview is on image retrieval using texture and on digital watermarking. A texture thesaurus for browsing aerial photographs and a wavelet based digital watermarking scheme are presented.
Peer group image enhancement

Peer group image processing identifies a "peer group" for each pixel and then replaces the pixel intensity with the average over the peer group. Two parameters provide direct control over which image features are selectively enhanced: area (number of pixels in the feature) and window diameter (window size needed to enclose the feature). A discussion is given of how these parameters determine which features in the image are smoothed or preserved. We show that the Fisher discriminant can be used to automatically adjust the peer group averaging (PGA) parameters at each point in the image. This local parameter selection allows smoothing over uniform regions while preserving features like corners and edges. This adaptive procedure extends to multilevel and color forms of PGA. Comparisons are made with a variety of standard filtering techniques and an analysis is given of computational complexity and convergence issues.
Exploiting Spatial Structure for Localizing Manipulated Image Regions

The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.
An eigenspace update algorithm for image analysis

During the past few years several interesting applications of eigenspace representation of images have been proposed. These include face recognition, video coding, pose estimation, etc. However, the vision research community has largely overlooked parallel developments in signal processing and numerical linear algebra concerning efficient eigenspace updating algorithms. These new developments are significant for two reasons: adopting them makes some of the current vision algorithms more robust and efficient. More important is the fact that incremental updating of eigenspace representations opens up new and interesting research applications in vision such as active recognition and learning. The main objective of the paper is to put these in perspective and discuss a recently introduced updating scheme that has been shown to be numerically stable and optimal. We provide an example of one particular application to 3D object representation projections and give an error analysis of the algorithm. Preliminary experimental results are shown.
Edge Preserving Filters using Geodesic Distances on Weighted Orthogonal Domains

We introduce a framework for image enhancement, which smooths images while preserving edge information. Domain (spatial) and range (feature) information are combined in one single measure in a principled way. This measure turns out to be the geodesic distance between pixels, calculated on weighted orthogonal domains. The weight function is computed to capture the underlying structure of the image manifold, but allowing at the same time to efficiently solve, using the Fast Marching algorithm on orthogonal domains, the eikonal equation to obtain the geodesic distances. We show promising results in edge-preserving denoising of gray scale, color and texture images.
Variational image segmentation using boundary functions

A general variational framework for image approximation and segmentation is introduced. By using a continuous "line-process" to represent edge boundaries, it is possible to formulate a variational theory of image segmentation and approximation in which the boundary function has a simple explicit form in terms of the approximation function. At the same time, this variational framework is general enough to include the most commonly used objective functions. Application is made to Mumford-Shah type functionals as well as those considered by Geman and others. Employing arbitrary Lp norms to measure smoothness and approximation allows the user to alternate between a least squares approach and one based on total variation, depending on the needs of a particular image. Since the optimal boundary function that minimizes the associated objective functional for a given approximation function can be found explicitly, the objective functional can be expressed in a reduced form that depends only on the approximating function. From this a partial differential equation (PDE) descent method, aimed at minimizing the objective functional, is derived. The method is fast and produces excellent results as illustrated by a number of real and synthetic image problems.
Features we trust!

We investigate the problem of image classification within a supervised learning framework that exploits implicit mutual information in different visual features and their associated classifiers. In our proposed two stage hierarchical processing, visual features are first clustered with the objective of maximizing diversity. Majority vote within each cluster is used to enforce diversity. Many partitioning variations are evaluated using K-nearest neighbor to obtain the highest inter-cluster entropy. In the second step, a richer measure of discrimination is obtained using a fully connected conditional random fields (CRF) over clusters. The unary and interaction potentials are defined over mutual information within each cluster and inter-dependencies across clusters respectively. Experimenting over five distinct datasets, we demonstrate an average performance gain of 30% compared with state of the art techniques.
A Model Based Approach To Detecting Spinal Disk Degenerations From MRI Data

We present in this paper an eficient and robwt computer vision algorithm for analyzing the sagittal m e w of the lower lumbar spine. The objectiue of the system is to detect and localize any abnormalities in the spinal disks. There are two main processing stages in the algorithm: in the first stage the entire study (which typically consists of 10-15 sagittal cross sections) is processed to determine a volume of interest. In the second stage a more detailed analysis of the work area yields accurate intervertebral disk contours, providing quantitative diagnostic information.
A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation

Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley segmentation data set by comparing its performance with other segmentation techniques.
Activity Analysis in Microtubule Videos by Mixture of Hidden Markov Models

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.
Statistical restoration for robust and secure steganography

We investigate data hiding techniques that attempt to defeat steganalysis by restoring the statistics of the composite image to resemble that of the cover. The approach is to reserve a number of host symbols for statistical restoration: host statistics perturbed by data embedding are restored by suitably modifying the symbols from the reserved set. While statistical restoration has broad applicability to a variety of hiding methods, we illustrate our ideas here for quantization index modulation (QIM) based hiding. We propose a method for significantly reducing the detectability of QIM, while preserving its robustness to attacks. We next use the framework of statistical restoration to develop a method to combat steganalysis techniques which detect block-DCT embedding by evaluating the increase in blockiness of the image due to hiding. Numerical results demonstrating the efficacy of these techniques are provided.