nils-herrmann/clean_scirepeval huggingface dataset:
Eye tracking assisted extraction of attentionally important objects from videos

Visual attention is a crucial indicator of the relative importance of objects in visual scenes to human viewers. In this paper, we propose an algorithm to extract objects which attract visual attention from videos. As human attention is naturally biased towards high level semantic objects in visual scenes, this information can be valuable to extract salient objects. The proposed algorithm extracts dominant visual tracks using eye tracking data from multiple subjects on a video sequence by a combination of mean-shift clustering and Hungarian algorithm. These visual tracks guide a generic object search algorithm to get candidate object locations and extents in every frame. Further, we propose a novel multiple object extraction algorithm by constructing a spatio-temporal mixed graph over object candidates. Bounding box based object extraction inference is performed using binary linear integer programming on a cost function defined over the graph. Finally, the object boundaries are refined using grabcut segmentation. The proposed technique outperforms state-of-the-art video segmentation using eye tracking prior and obtains favorable object extraction over algorithms which do not utilize eye tracking data.
Peer group filtering and perceptual color image quantization

In the first part of this work, peer group filtering (PGF), a nonlinear algorithm for image smoothing and impulse noise removal in color images is presented. The algorithm replaces each image pixel with the weighted average of its peer group members, which are classified based on the color similarity of the neighboring pixels. Results show that it effectively removes the noise and smooths the color images without blurring edges and details. In the second part of the work, PGF is used as a preprocessing step for color quantization. Local statistics obtained after PGF are used as weights in the quantization to suppress color clusters in detailed regions, since human perception is less sensitive to the differences in these areas. As a result, very coarse quantization can be obtained while preserving the color information in the original images. This can be useful in color image segmentation and color image retrieval applications.
Browsing large satellite and aerial photographs

Image content based retrieval in the Alexandria digital library project has focussed on texture and color features for querying the database. A robust texture feature extraction algorithm and a fast segmentation scheme have been developed. The texture features are computed by filtering the image with a bank of Gabor filters. This is followed by a clustering scheme to create a texture based feature dictionary, which is then used to search and retrieve similar looking patterns from other images. Experimental results demonstrate the robustness of the overall system in searching over a large collection of airphotos and in selecting a surprisingly diverse collection of geographic features such as housing developments, parking lots, highways, and airports.
A robust method for detecting image features with application to face recognition and motion correspondence

The authors present an approach to feature detection, which is a fundamental issue in many intermediate-level vision problems such as stereo, motion correspondence, image registration, etc. The approach is based on a scale-interaction model of the end-inhibition property exhibited by certain cells in the visual- cortex of mammals. These feature detector cells are responsive to short lines, line endings, corners and other such sharp changes in curvature. In addition, this method also provides a compact representation of feature information which is useful in shape recognition problems. Application to face recognition and motion correspondence are illustrated.<<ETX>>
Image segmentation using multi-region stability and edge strength

A novel scheme for image segmentation is presented. An image segmentation criterion is proposed that groups similar pixels together to form regions. This criterion is formulated as a cost function. Using gradient-descent methods, which lead to a curve evolution equation that segments the image into multiple homogenous regions, minimizes this cost function. Homogeneity is specified through a pixel-to-pixel similarity measure, which is defined by the user and can be adaptive based on the current application. To improve the performance of the system, an edge function is also used to adjust the speed of the competing curves. The proposed method can be easily applied to vector valued images such as texture and color images without a significant addition to computational complexity.
Spatio-temporal relationships and video object extraction

An object-based representation for video data can facilitate video search and content analysis. Detecting physical meaningful video object is a challenging open issue, and requires intelligent spatio-temporal segmentation and tracking. Normally, this is done through spatio-temporal segmentation and region tracking. In this work, some of the practical issues of segmentation and tracking problems are addressed. Due to the limitation of using low-level visual features in the segmentation, the tracked regions are more likely to be fragmented parts of some meaningful objects. However if a collection of video shots that contain a particular object of interest are given, spatio-temporal correlations would exist between the neighboring regions of the object. A method of mining association rules is used to discover these patterns and thus to find possible objects in the scene. Initial experimental results of this approach are shown.
Issues concerning dimensionality and similarity search

Effectiveness and efficiency are two important concerns in using multimedia descriptors to search and access database items. Both are affected by the dimensionality of the descriptors. While higher dimensionality generally increases effectiveness, it drastically reduces efficiency of storage and searching. With regard to effectiveness, relevance feedback is known to be a useful tool to squeeze information from a descriptor. However, not much has been done toward enabling relevance feedback computation using high-dimensional descriptors over a large multimedia dataset. In this context, we have developed new methods that enable us to a) reduce the dimensionality of Gabor texture descriptors without losing on effectiveness, and b) perform fast nearest neighbor search based on the information available during each iteration of a relevance feedback step. Experimental results are presented on real datasets.
Scalable spatial event representation

This work introduces a conceptual representation for complex spatial arrangements of image features in large multimedia datasets. A novel data structure, termed the spatial event cube (SEC), is formed from the co-occurrence matrices of perceptually classified features with respect to specific spatial relationships. A visual thesaurus constructed using supervised and unsupervised learning techniques is used to label the image features. SECs can be used to not only visualize the dominant spatial arrangements of feature classes but also discover non-obvious configurations. SECs also provide the framework for high-level data mining techniques such as using the generalized association rule approach. Experimental results are provided for a large dataset of aerial images.
Deformable trellis: open contour tracking in bio-image sequences

This paper presents an open contour tracking method that employs an arc-emission hidden Markov model (HMM). The algorithm encodes the shape information of the structure in a spatially deformable trellis model that is iteratively modified to account for observations in subsequent frames. As the open contour is determined on the trellis of an HMM, a dynamic programming procedure reduces the computational complexity to linear in the length of the structure (or contour). The method was developed for tracking general curvilinear structures, and tested on subcellular image sequences, where microtubules grow, shrink and undergo lateral motion from frame to frame. Microtubule length changes are modeled by the addition of appropriate transient and absorbing states to the HMM. Our results provide experimental evidence for the proposed algorithm's capability to track non-rigid curvilinear objects in challenging environments in terms of noise and clutter.
Redundancy in All Pairs Fast Marching Method

In this paper, we analyze the redundancy in calculating all pairs of geodesic distances on a rectangular grid. Fast marching method is an efficient way to estimate the geodesic distances from a point. But when calculated for all the points on the grid, this introduces certain redundancy. Our analysis shows that over 90% of the distances are actually recalculated. We propose a novel solution which exploits this redundancy to reduce the number of distances evaluated using the fast marching method and enforces the symmetry of the distance matrix. Experimental results show the improved accuracy obtained with our implementation.
Interactive segmentation using curve evolution and relevance feedback

We propose in this paper an interactive segmentation algorithm based on curve evolution techniques. The task of automated segmentation has proven to be highly complex and application dependent. User's knowledge can be used to alleviate the problem. In this paper, we propose the use of a recently developed curve evolution technique B. Sumengen et al., (2003), augmented with a relevance feedback phase through user interaction. After the initial automatic segmentation is computed, the user presents his positive/negative feedback via a simple user interface. Segmentation parameters are then adapted locally to reflect user's requirements. Experimental results show the usefulness of the proposed approach in interactive segmentation tasks.
Object localization using texture motifs and Markov random fields

This work presents a novel approach to object localization in complex imagery. In particular, the spatial extents of objects characterized by distinct spatial signatures at multiple scales are estimated by using statistical models to control a simple region growing process. Texture motifs are used to model the spatial signatures at the smallest, or pixel, scale. Markov random fields are used to model the spatial signatures at the larger, or motif, scale. These models are used to iteratively expand a bounding box to approximate the spatial extent of an object. The approach is applied to localizing geo-spatial objects in high-resolution panchromatic aerial imagery.
Patient Data Management System — Periodic Model using GSM

In monitoring a patient's real-time vital signs through Body Area Networks (BAN), rich data sources are communicated to medical practitioners. It is essential that data is delivered in timely-context aware manner. In this paper a system is designed for patients with cardiac disorders, with an emphasis on the design of the sensing device and communication scheme chosen. Several existing wearable physiological devices (Patient Sensing Device — PSD) used in the Healthcare systems are bulky in design and are not flexible and comfortable for the elderly patients. Presented is a unique flexible, as well as detachable PSD for the comfort of patients. Also discussed is a model for transmission, storage and processing of patient related data, which ensures periodic logging of patient data without saturating communication networks. A proof of concept prototype has been developed and implemented to enable transmission of Electrocardiogram (ECG) signal and body temperature of a patient, which can be expanded to include other vital signs. Communication between a mobile smart-phone and the ECG and temperature acquisition apparatus is implemented Bluetooth. The presented Data Management System — Periodic Model (DMS — PM) is designed to manage wireless interface of sensor units with the patient database at a Medical Service Provider (MSP) through a Personal Digital Assistant (PDA) or a Smart phone making use of the existing GSM network.
Multiple view discriminative appearance modeling with IMCMC for distributed tracking

This paper proposes a distributed multi-camera tracking algorithm with interacting particle filters. A robust multi-view appearance model is obtained by sharing training samples between views. Motivated by incremental learning and [1], we create an intermediate data representation between two camera views with generative subspaces as points on a Grassmann manifold, and sample along the geodesic between training data from two views to uncover the meaningful description due to viewpoint changes. Finally, a Boosted appearance model is trained using the projected training samples on to these generative subspaces. For each object, a set of two particle filters i.e., local and global is used. The local particle filter models the object motion in the image plane. The global particle filter models the object motion in the ground plane. These particle filters are integrated into a unified Interacting Markov Chain Monte Carlo (IMCMC) framework. We show the manner in which we induce priors on scene specific information into the global particle filter to improve tracking accuracy. The proposed algorithm is validated with extensive experimentation in challenging camera network data, and compares favorably with state of the art object trackers.
Color image segmentation

In this work, a new approach to fully automatic color image segmentation, called JSEG, is presented. First, colors in the image are quantized to several representing classes that can be used to differentiate regions in the image. Then, image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values correspond to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale J-images. Experiments show that JSEG provides good segmentation results on a variety of images.
A correlation-based model prior for stereo

All non-trivial stereo problems need model priors to deal with ambiguities and noise perturbations. To meet requirements of increasingly demanding tasks such as modeling for rendering, a proper model prior should impose preference on the true scene structure, while avoiding artificial bias such as fronto-parallel. We introduce a geometric model prior based on a novel technique we call kernel correlation. Maximizing kernel correlation is shown to be equal to distance minimization in the M-estimator sense. As a model prior, kernel correlation is demonstrated to have good properties that can result in renderable, very smooth and accurate depth map. The results are evaluated both qualitatively by view synthesis and quantitatively by error analysis.
An automatic feature based model for cell segmentation from confocal microscopy volumes

We present a model for the automated segmentation of cells from confocal microscopy volumes of biological samples. The segmentation task for these images is exceptionally challenging due to weak boundaries and varying intensity during the imaging process. To tackle this, a two step pruning process based on the Fast Marching Method is first applied to obtain an over-segmented image. This is followed by a merging step based on an effective feature representation. The algorithm is applied on two different datasets: one from the ascidian Ciona and the other from the plant Arabidopsis. The presented 3D segmentation algorithm shows promising results on these datasets.
Image segmentation via functionals based on boundary functions

A general variational framework for image approximation and segmentation is introduced 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. 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 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.
Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Many features used in the analysis of pathology imagery are inspired by grading features defined by clinical pathologists as important for diagnosis and characterization. A large majority of these features are features of cell nuclei; as such, there is often the desire to segment the imagery into individual nuclei prior to feature extraction and further analysis. In this paper we present an analysis of the utility of imperfectly segmented cell nuclei for classification of H&E stained histopathology imagery of breast tissue. We show the object- and image-level classification performance using these imperfectly segmented nuclei in a benign versus malignant decision. Results indicate that very good classification accuracies can be achieved with imperfectly segmented nuclei and further that perfect nuclei segmentation does not necessarily guarantee better classification accuracy.
VISNET: A distributed vision testbed

We introduce UCSBpsilas visual sensor network (VISNET) and discuss current research being conducted with the system. VISNET is a ten-node experimental camera network at UCSB used for various vision-related research. The mission of VISNET is to provide an easy-to-use multi-node camera network to the vision research community at UCSB. This paper briefly discusses design and setup considerations before discussing current research. Current research includes operation visualization, camera network calibration, tracked object modeling, and multiple object / multiple camera tracking.
Particle Filter Tracking with Online Multiple Instance Learning

This paper addresses the problem of object tracking by learning a discriminative classifier to separate the object from its background. The online-learned classifier is used to adaptively model object's appearance and its background. To solve the typical problem of erroneous training examples generated during tracking, an online multiple instance learning (MIL) algorithm is used by allowing false positive examples. In addition, particle filter is applied to make best use of the learned classifier and help to generate a better representative set of training examples for the online MIL learning. The effectiveness of the proposed algorithm is demonstrated in some challenging environments for human tracking.
Double embedding in the quantization index modulation framework

Quantization index modulation (QIM) is a commonly used data hiding technique where a single bit is embedded per coefficient. Here, we propose the use of double embedding in the QIM framework where a single coefficient is modified twice, using two quantizers, to embed two bits. The motivation behind substituting single embedding with double embedding in the QIM framework for a certain steganographic scheme is to increase its hiding rate without significantly increasing the embedding distortion and the stego scheme's detectability against steganalysis. We empirically determine the best way to couple the double embedding framework with a repeat accumulate code based error correction scheme. For moderate noise levels, the use of double embedding is seen to be significantly advantageous over single embedding.
Color image embedding using multidimensional lattice structures

This paper describes a robust data embedding scheme which uses noise resilient channel codes based on a multidimensional lattice structure. Compared to prior work in digital watermarking, the proposed scheme can handle a significantly larger quantity of signature data such as gray-scale or color images. A trade-off between the quantity of hidden data and the quality of the watermarked image is achieved by varying the number of quantization levels for the signature, and a scale factor for data embedding. Experimental results on signature recovery from JPEG compressed watermarked images show that good quality reconstruction is possible even when the images are lossy compressed by as much as 85%. Potential applications of this method include, in addition to watermarking, digital data hiding for security and for bit stream control and manipulation.
Anatomical parts-based regression using non-negative matrix factorization

Non-negative matrix factorization (NMF) is an excellent tool for unsupervised parts-based learning, but proves to be ineffective when parts of a whole follow a specific pattern. Analyzing such local changes is particularly important when studying anatomical transformations. We propose a supervised method that incorporates a regression constraint into the NMF framework and learns maximally changing parts in the basis images, called Regression based NMF (RNMF). The algorithm is made robust against outliers by learning the distribution of the input manifold space, where the data resides. One of our main goals is to achieve good region localization. By incorporating a gradient smoothing and independence constraint into the factorized bases, contiguous local regions are captured. We apply our technique to a synthetic dataset and structural MRI brain images of subjects with varying ages. RNMF finds the localized regions which are expected to be highly changing over age to be manifested in its significant basis and it also achieves the best performance compared to other statistical regression and dimensionality reduction techniques.
On the Rayleigh nature of Gabor filter outputs

Texture has been recognized as an important visual primitive in image analysis. A widely used texture descriptor, which is part, of the MPEG-7 standard, is that computed using multiscale Gabor filters. The high dimensionality and computational complexity of this descriptor adversely affect the efficiency of content-based retrieval systems. We propose a modified texture descriptor that has comparable performance, but with nearly half the dimensionality and less computational expense. This gain is based on a claim that the distribution of (absolute values of) filter outputs have a strong tendency to be Rayleigh. Experimental results show that the dimensionality can be reduced by almost 50%, with a tradeoff of less than 3% on the error rate. Furthermore, it is easy to compute the new feature using the old, one, without having to repeat the computationally expensive filtering step. We also propose a new normalization method that improves similarity retrieval and indexing efficiency.
A Nonconservative Flow Field for Robust Variational Image Segmentation

We introduce a robust image segmentation method based on a variational formulation using edge flow vectors. We demonstrate the nonconservative nature of this flow field, a feature that helps in a better segmentation of objects with concavities. A multiscale version of this method is developed and is shown to improve the localization of the object boundaries. We compare and contrast the proposed method with well known state-of-the-art methods. Detailed experimental results are provided on both synthetic and natural images that demonstrate that the proposed approach is quite competitive.