nils-herrmann/clean_scirepeval huggingface dataset:
Color and texture descriptors

This paper presents an overview of color and texture descriptors that have been approved for the Final Committee Draft of the MPEG-7 standard. The color and texture descriptors that are described in this paper have undergone extensive evaluation and development during the past two years. Evaluation criteria include effectiveness of the descriptors in similarity retrieval, as well as extraction, storage, and representation complexities. The color descriptors in the standard include a histogram descriptor that is coded using the Haar transform, a color structure histogram, a dominant color descriptor, and a color layout descriptor. The three texture descriptors include one that characterizes homogeneous texture regions and another that represents the local edge distribution. A compact descriptor that facilitates texture browsing is also defined. Each of the descriptors is explained in detail by their semantics, extraction and usage. The effectiveness is documented by experimental results.
Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts

We present a weakly-supervised approach to semantic segmentation. The goal is to assign pixel-level labels given only partial information, for example, image-level labels. This is an important problem in many application scenarios where it is difficult to get accurate segmentation or not feasible to obtain detailed annotations. The proposed approach starts with an initial coarse segmentation, followed by a spectral clustering approach that groups related image parts into communities. A community-driven graph is then constructed that captures spatial and feature relationships between communities while a label graph captures correlations between image labels. Finally, mapping the image level labels to appropriate communities is formulated as a convex optimization problem. The proposed approach does not require location information for image level labels and can be trained using partially labeled datasets. Compared to the state-of-the-art weakly supervised approaches, we achieve a significant performance improvement of 9% on MSRC-21 dataset and 11% on LabelMe dataset, while being more than 300 times faster.
A unified approach to boundary perception: edges, textures, and illusory contours

A model consisting of a multistage system which extracts and groups salient features in the image at different spatial scales (or frequencies) is used. In the first stage, a Gabor wavelet decomposition provides a representation of the image which is orientation selective and has optimal localization properties in space and frequency. This decomposition is useful in detecting significant features such as step and line edges at different scales and orientations in the image. Following the wavelet transformation, local competitive interactions are introduced to reduce the effects of noise and changes in illumination. Interscale interactions help in localizing the line ends and corners, and play a crucial role in boundary perception. The final stage groups similar features, aiding in boundary completion. The different stages can be identified with processing by simple, complex, and hypercomplex cells in the visual cortex of mammals. Experimental results demonstrate the performance of this model in detecting boundaries (both real and illusory) in real and synthetic images.
Generalized simultaneous registration and segmentation

Simultaneous registration and segmentation (SRS) provides a powerful framework for tracking an object of interest in an image sequence. The state-of-the-art SRS-based tracking methods assume that the illumination is maintained constant across consecutive frames. However, this assumption does not hold in many natural image sequences due to dynamic light source and shadows. We propose a generalized model for SRS-based tracking in this paper to account for non-uniform additive illumination changes. More specifically, we introduce two new terms in the SRS energy functional which address the above mentioned problem. The first term couples the shape-based cue and intensity-based cue to establish a correspondence between them. The second term compensates for the illumination change which is complementary to the first term. We demonstrate that the proposed SRS energy functional yields superior performance over the state-of-the-art SRS-based methods for various indoor and outdoor image sequences.
Registration of 3-D multimodality brain images by curve matching

The 3D multimodality brain image registration algorithm proposed here is based on the matching of feature curves which are defined by the intersections of the interhemispherical fissure plane (IFP) and the skull surface. The IFP as detected by a principal axes technique while the skull boundary in each scan is extracted by tracing the outermost closed contour. The feature curves care rotated to lie on 3D planes which are parallel to each other so that the problem is reduced to a 2D curve matching task. An MRI-PET image registration result is presented.<<ETX>>
Tracing Curvilinear Structures in Live Cell Images

Tracing of curvilinear structures is one of the fundamental tools in the quantitative analysis of biological images, for extracting information about structures such as blood vessels, neurons, microtubules, and similar entities. Due to the limitations in biological sample preparation and fluorescence imaging, typical images in live cell studies exhibit severe noise and considerable clutter. These images are manually analyzed through a laborious and approximate set of quantification tasks. In this paper, we describe a constrained optimization method for extracting curvilinear structures from live cell fluorescence images. We show that the proposed method is largely insensitive to frequent intersections, intensity variations along the curve, and generates successful traces within noisy regions. We demonstrate the results of our approach on live cell microtubule images.
Subset selection for active object recognition

This paper presents an algorithm for constructing object representations suitable for recognition. The system automatically selects a representative subset of the views of the object while constructing the eigenspace basis. These views are actively located for object identification and pose determination. All processing is performed on-line. The camera is actively positioned during both representation and recognition. When tested with 240 views for each of seven objects, the system achieves 100% accurate object recognition and pose determination. These results are shown to degrade gracefully as conditions deteriorate.
Eye-CU: Sleep pose classification for healthcare using multimodal multiview data

Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expected and estimated labels. The new system, Eye-CU, is an affordable multi-sensor modular system for unobtrusive data collection and analysis in healthcare. Experimental results indicate that the performance of cc-LS matches the performance of existing methods in ideal scenarios. This method outperforms the latest techniques in challenging scenarios by 13% for those with poor illumination and by 70% for those with both poor illumination and occlusions. Results also show that a reduced Eye-CU configuration can classify poses without pressure information with only a slight drop in its performance.
Steganalysis for Markov cover data with applications to images

The difficult task of steganalysis, or the detection of the presence of hidden data, can be greatly aided by exploiting the correlations inherent in typical host or cover signals. In particular, several effective image steganalysis techniques are based on the strong interpixel dependencies exhibited by natural images. Thus, existing theoretical benchmarks based on independent and identically distributed (i.i.d.) models for the cover data underestimate attainable steganalysis performance and, hence, overestimate the security of the steganography technique used for hiding the data. In this paper, we investigate detection-theoretic performance benchmarks for steganalysis when the cover data are modeled as a Markov chain. The main application explored here is steganalysis of data hidden in images. While the Markov chain model does not completely capture the spatial dependencies, it provides an analytically tractable framework whose predictions are consistent with the performance of practical steganalysis algorithms that account for spatial dependencies. Numerical results are provided for image steganalysis of spread-spectrum and perturbed quantization data hiding.
Stochastic Learning Networks For Texture Segmentation*

In this paper we describe Neural Network based algorithms for the segmentation of textured gray level images. We for- mulate the problem as one of minimizing an energy function, derived through the representation of textures as Markov Random Fields (MRF). The texture intensity array is modelled as a Gauss Markov Random Field (GMRF) and an Ising model is used to characterize the label distribution. The resulting nonconvex energy function is minimized using a Hopfield neural network. The solution obtained is a local optimum in general and may not be satisfactory in many cases. Although stochastic algorithms like simulated annealing have a potential of finding the global optimum, they are computationally expensive. We suggest an alternate approach based on the theory of learning automata which introduces stochastic learning into the iterations of the Hopfield network. A probability distribution over the possible label configurations is defined and the probabilities are updated depending on the final stable states reached by the neural network. This iterated hill climbing algorithm combines the fast convergence of the deterministic relaxation with the sustained exploration of the stochastic algorithms. The perfor- mance of this rule in classifying some real textured images is given.
A semantic representation for image retrieval

Robust semantic labeling of image regions is a basic problem in representing and retrieving image/video content. We propose an SVM-MRF framework to model features and their spatial distributions, leading towards a "semantic" representation. Eigenfeatures of Gabor wavelet features and Gaussian mixture model are used for feature clustering. Since similar feature vectors in one cluster can come from several different semantic classes, SVM is applied to represent conditioned feature vector distributions within each cluster, and a Markov random field is used to model the spatial distributions of the semantic labels. A semantic layout representation is proposed to describe the semantics of the images. Experiments show that this method can improve semantic labeling and is useful in similarity search.
Robust biological image sequence analysis using graph based approaches

Robust methods for segmentation and tracking are critical for quantitative biology. We give an overview of our recent work on graph based methods for various microscopy image analysis, including tracing over 3-D electron microscopy image stacks, tracking in time-lapse confocal image sequences, and 3D segmentation. We present results on a variety of datasets such as 3-D confocal membrane volumes of the ascidian Ciona, electron micrograph stacks from the rabbit retina, and bright field microscopy time sequence data from mouse retina.
Uniform field breakdown in SF/sub 6/ gas at high pressures under lightning and switching surge voltages

Experiments were conducted to determine the 50% flashover voltages (V/sub 50/) over the pressure range of 0.14 to 0.45 MPa absolute, using lightning (1.2/50 mu s) and switching (100/2500 mu s) impulse voltages, under uniform electric fields having two electrodes of Rogowski profile, with a gap spacing varying from 4 to 30 mm. Results indicate that the breakdown strength of SF/sub 6/ gas exhibits slight saturation at higher pressures (above 0.3 MPa). The effect is higher for switching surge voltages than for impulse voltages. The polarity effect is observed both under lightning and switching surge voltages and is more pronounced at higher gas pressures and larger gap spacings. The V/sub 50/ values with switching surge voltages are generally lower than the corresponding lightning impulse voltage values.<<ETX>>
From Where and How to What We See

Eye movement studies have confirmed that overt attention is highly biased towards faces and text regions in images. In this paper we explore a novel problem of predicting face and text regions in images using eye tracking data from multiple subjects. The problem is challenging as we aim to predict the semantics (face/text/background) only from eye tracking data without utilizing any image information. The proposed algorithm spatially clusters eye tracking data obtained in an image into different coherent groups and subsequently models the likelihood of the clusters containing faces and text using a fully connected Markov Random Field (MRF). Given the eye tracking data from a test image, it predicts potential face/head (humans, dogs and cats) and text locations reliably. Furthermore, the approach can be used to select regions of interest for further analysis by object detectors for faces and text. The hybrid eye position/object detector approach achieves better detection performance and reduced computation time compared to using only the object detection algorithm. We also present a new eye tracking dataset on 300 images selected from ICDAR, Street-view, Flickr and Oxford-IIIT Pet Dataset from 15 subjects.
LLRT based detection of LSB hiding

In this paper we consider a hypothesis testing approach for detection of hiding in the least significant bit (LSB). This steganalysis problem is a composite hypothesis testing problem. We state a regularity condition on the image histogram, which reduces this problem to a simple hypothesis testing problem. We then develop a number of simple practical tests based on the estimation of the optimal log likelihood ratio statistic. We show that our tests significantly outperform Stegdetect, a popular hypothesis test available in the literature. Our approach also leads to good estimates of the hiding rate.
Probabilistic subspace-based learning of shape dynamics modes for multi-view action recognition

We propose a human action recognition algorithm by capturing a compact signature of shape dynamics from multi-view videos. First, we compute ℜ transforms and its temporal velocity on action silhouettes from multiple views to generate a robust low level representation of shape. The spatio-temporal shape dynamics across all the views is then captured by fusion of eigen and multiset partial least squares modes. This provides us a lightweight signature which is classified using a probabilistic subspace similarity technique by learning inter-action and intra-action models. Quantitative and qualitative results of our algorithm are reported on MuHAVi a publicly available multi-camera multi-action dataset.
Learning top down scene context for visual attention modeling in natural images

Top down image semantics play a major role in predicting where people look in images. Present state-of-the-art approaches to model human visual attention incorporate high level object detections signifying top down image semantics in a separate channel along with other bottom up saliency channels. However, multiple objects in a scene are competing to attract our attention and this interaction is ignored in current models. To overcome this limitation, we propose a novel object context based visual attention model which incorporates the co-occurrence of multiple objects in a scene for visual attention modeling. The proposed regression based algorithm uses several high level object detectors for faces, people, cars, text and understands how their joint presence affects visual attention. Experimental results on the MIT eye tracking dataset demonstrates that the proposed method outperforms other state-of-the-art visual attention models.
NeTra: a toolbox for navigating large image databases

We present an implementation of NeTra, a prototype image retrieval system that uses color texture, shape and spatial location information in segmented image database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object or region based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as "retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper one-third of the image" where the individual objects could be regions belonging to different images.
Membrane segmentation via active learning with deep networks

Segmentation is a key component of several bio-medical image processing systems. Recently, segmentation methods based on supervised learning such as deep convolutional networks have enjoyed immense success for natural image datasets and biological datasets alike. These methods require large volumes of data to avoid overfitting which limits their applicability. In this work, we present a transfer learning mechanism based on active learning which allows us to utilize pre-trained deep networks for segmenting new domains with limited labelled data. We introduce a novel optimization criterion to allow feedback on the most uncertain, yet abundant image patterns thus provisioning for an expert in the loop albeit with minimum amount of guidance. Our experiments demonstrate the effectiveness of the proposed method in improving segmentation performance with very limited labelled data.
Distributed particle filter tracking with online multiple instance learning in a camera sensor network

This paper proposes a distributed algorithm for object tracking in a camera sensor network. At each camera node, an efficient online multiple instance learning algorithm is used to model object's appearance. This is integrated with particle filter for camera's image plane tracking. To improve the tracking accuracy, each camera node shares its particle states with others and fuses multi-camera information locally. In particular, particle weights are updated according to the fused information. Then, appearance model is updated with the re-weighted particles. The effectiveness of the proposed algorithm is demonstrated on human tracking in challenging environments.
Rotation-invariant texture classification using a complete space-frequency model

A method of rotation-invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving microfeatures. From these microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are derived from the estimated selected parameters of the micromodel. Classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures. In experiments, comparatively high correct classification rates were obtained using large sample sets.
Robust image-adaptive data hiding using erasure and error correction

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. We propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as compression and limited amounts of image tampering and image resizing. The three main findings are as follows. 1) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use image-adaptive criteria in addition to statistical criteria based on information theory. 2) The use of local criteria to choose where to hide data can potentially cause desynchronization of the encoder and decoder. This synchronization problem is solved by the use of powerful, but simple-to-implement, erasures and errors correcting codes, which also provide robustness against a variety of attacks. 3) For simplicity, scalar quantization-based hiding is employed, even though information-theoretic guidelines prescribe vector quantization-based methods. However, an information-theoretic analysis for an idealized model is provided to show that scalar quantization-based hiding incurs approximately only a 2-dB penalty in terms of resilience to attack.
An axiomatic approach to corner detection

This paper presents an axiomatic approach to corner detection. In the first part of the paper we review five currently used corner detection methods (Harris-Stephens, Forstner, Shi-Tomasi, Rohr, and Kenney et al ) for graylevel images. This is followed by a discussion of extending these corner detectors to images with different pixel dimensions such as signals (pixel dimension one) and tomographic medical images (pixel dimension three) as well as different intensity dimensions such as color or LADAR images (intensity dimension three). These extensions are motivated by analyzing a particular example of optical flow in pixel and intensity space with arbitrary dimensions. Placing corner detection in a general setting enables us to state four axioms that any corner detector might reasonably be required to satisfy. Our main result is that only the Shi-Tomasi (and equivalently the Kenney et al. 2-norm detector) satisfy all four of the axioms.
Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.