nils-herrmann/clean_scirepeval huggingface dataset:
Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal Alignment

Study of the molecular control of organ growth requires establishment of the causal relationship between gene expression and cell behaviors. We seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live-cell imaging techniques allow us to observe real-time organ primordia growth and gene expression dynamics at cellular resolution. In this paper, we propose a framework for the measurement of growth features at the 3D reconstructed surface of organ primordia, as well as algorithms for robust time alignment of primordia. We computed areas and deformation values from reconstructed 3D surfaces of individual primordia from live-cell imaging data. Based on these growth measurements, we applied a multiple feature landscape matching (LAM-M) algorithm to ensure a reliable temporal alignment of multiple primordia. Although the original landscape matching (LAM) algorithm motivated our alignment approach, it sometimes fails to properly align growth curves in the presence of high noise/distortion. To overcome this shortcoming, we modified the cost function to consider the landscape of the corresponding growth features. We also present an alternate parameter-free growth alignment algorithm which performs as well as LAM-M for high-quality data, but is more robust to the presence of outliers or noise. Results on primordia and guppy evolutionary growth data show that the proposed alignment framework performs at least as well as the LAM algorithm in the general case, and significantly better in the case of increased noise.
Learning a sparse dictionary of video structure for activity modeling

We present an approach which incorporates spatiotemporal features as well as the relationships between them, into a sparse dictionary learning framework for activity recognition. We propose that the dictionary learning framework can be adapted to learning complex relationships between features in an unsupervised manner. From a set of training videos, a dictionary is learned for individual features, as well as the relationships between them using a stacked predictive sparse decomposition framework. This combined dictionary provides a representation of the structure of the video and is spatio-temporally pooled in a local manner to obtain descriptors. The descriptors are then combined using a multiple kernel learning framework to design classifiers. Experiments have been conducted on two popular activity recognition datasets to demonstrate the superior performance of our approach on single person as well as multi-person activities.
Managing Redundant Content in Bandwidth Constrained Wireless Networks

Images/videos are often uploaded in situations like disasters. This can tax the network in terms of increased load and thereby upload latency, and this can be critical for response activities. In such scenarios, prior work has shown that there is significant redundancy in the content (e.g., similar photos taken by users) transferred. By intelligently suppressing/deferring transfers of redundant content, the load can be significantly reduced, thereby facilitating the timely delivery of unique, possibly critical information. A key challenge here however, is detecting “what content is similar,” given that the content is generated by uncoordinated user devices. Toward addressing this challenge, we propose a framework, wherein a service to which the content is to be uploaded first solicits metadata (e.g., image features) from any device uploading content. By intelligently comparing this metadata with that associated with previously uploaded content, the service effectively identifies (and thus enables the suppression of) redundant content. Our evaluations on a testbed of 20 Android smartphones and via ns3 simulations show that we can identify similar content with a 70% true positive rate and a 1% false positive rate. The resulting reduction in redundant content transfers translates to a latency reduction of 44 % for unique content.
Exploiting local structure for tracking plant cells in noisy images

In this paper, we present a local graph matching based method for tracking cells and cell divisions in noisy images. We work with plant cells, where the cells are tightly clustered in space and computing correspondences across time can be very challenging. The local graph matching method is able to track the cells and cell divisions even when significant portions of the images are corrupted due to sensor noise in the imaging process. The geometric structure and topology of the cells' relative positions are efficiently exploited to solve the tracking problem using the local graph matching technique. Using this method we can track almost all of the properly segmented cells, even when some of those images are highly noisy.
Modeling Time-Varying Illumination Patterns in Video

Recreating the temporal illumination variations of natural scenes has great potential for realistic synthesis of video sequences. In this paper, we present a 3D (model-based) approach that achieves this goal. The approach requires a training sequence to learn the time-varying illumination models, which can then be used for synthesis in another sequence. The motion and illumination parameters in the training sequence are estimated alternately by projecting onto appropriate basis functions of a bilinear space defined in terms of the 3D surface normals of the objects. The motion is represented in terms of 3D translation and rotation of the object centroid in the camera frame, and the illumination is represented using a spherical harmonics linear basis. We show video synthesis results using the proposed approach.
Capturing high fidelity images in PTZ camera networks

The decreasing cost and size of video sensors has led to camera networks becoming pervasive in our lives. However, the ability to analyze these images effectively is very much a function of the quality of the acquired images. In this paper we consider the problem of automatically controlling the fields of view of individual pan, tilt, zoom (PTZ) cameras in a camera network leading to improved situation awareness (e.g. where and what are the critical targets and events) in a region of interest. The network of cameras attempts to observe the entire region of interest at some minimum resolution while opportunistically acquiring high resolution images of critical events in real time. This is achieved by understanding the performance of video analysis tasks and designing camera control strategies to improve these tasks through the measurement of the quality of the source imagery. Optimization strategies, along with a distributed implementation, are proposed. The proposed methods bring together computer vision and network control ideas. Performance of the proposed methodologies discussed herein have been evaluated on a real life wireless network of pan, tilt and zoom capable cameras.
Identification of humans using gait

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.
Learning Illumination Models While Tracking

In this paper we present a method for estimation of 3D motion of a rigid object from a video sequence, while simultaneously learning the parameters of an illumination model that describe the lighting conditions under which the video was captured. This is achieved by alternately estimating motion and illumination parameters in a recently proposed mathematical framework for integrating the effects of motion, illumination and structure. The motion is represented in terms of translation and rotation of the object centroid, and the illumination is represented using a spherical harmonics linear basis. The method does not assume any model for the variation of the illumination conditions - lighting can change slowly or drastically, locally or globally. Also, it can be composed of combinations of point and extended sources. For multiple cameras viewing an object, we derive a new photometric constraint that relates the illumination parameters in two or more independent video sequences. This constraint allows verification of the illumination parameters obtained from multiple views and synthesis of new views under the same lighting conditions. We demonstrate the effectiveness of our algorithm in tracking under severe changes of lighting conditions.
Exploiting Spatio-Temporal Scene Structure for Wide-Area Activity Analysis in Unconstrained Environments

Surveillance videos in unconstrained environments typically consist of long duration sequences of activities which occur at different spatio-temporal locations and can involve multiple people acting simultaneously. Often, the activities have contextual relationships with one another. Although context has been studied in the past for the purpose of activity recognition, the use of context in recognition of activities in such challenging environments is relatively unexplored. In this paper, we propose a novel method for capturing the spatio-temporal context between activities in a Markov random field. The structure of the MRF is improvised upon during test time and not predefined, unlike many approaches that model the contextual relationships between activities. Given a collection of videos and a set of weak classifiers for individual activities, the spatio-temporal relationships between activities are represented as probabilistic edge weights in the MRF. This model provides a generic representation for an activity sequence that can extend to any number of objects and interactions in a video. We show that the recognition of activities in a video can be posed as an inference problem on the graph. We conduct experiments on the publicly available UCLA office dataset and the VIRAT dataset, to demonstrate the improvement in recognition accuracy using our proposed model as opposed to recognition using state-of-the-art features on individual activity regions.
Towards a view invariant gait recognition algorithm

Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognizable when extracted from a side-view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera image plane. However, it is not realistic to expect this assumption to be valid in most real-life scenarios. Hence, it is important to develop methods whereby the side-view can be generated from any other arbitrary view in a simple, yet accurate, manner. This is the main theme of the paper. We show that if the person is far enough from the camera, it is possible to synthesize a side view (referred to as canonical view) from any other arbitrary view using a single camera. Two methods are proposed for doing this: (i) using the perspective projection model; (ii) using the optical flow based structure from motion equations. A simple camera calibration scheme for this method is also proposed. Examples of synthesized views are presented. Preliminary testing with gait recognition algorithms gives encouraging results. A by-product of this method is a simple algorithm for synthesizing novel views of a planar scene.
3D Neuron Tip Detection in Volumetric Microscopy Images

This paper addresses the problem of 3D neuron tips detection in volumetric microscopy image stacks. We focus particularly on neuron tracing applications, where the detected 3D tips could be used as the seeding points. Most of the existing neuron tracing methods require a good choice of seeding points. In this paper, we propose an automated neuron tips detection method for volumetric microscopy image stacks. Our method is based on first detecting 2D tips using curvature information and a ray-shooting intensity distribution model, and then extending it to the 3D stack by rejecting false positives. We tested this method based on the V3D platform, which can reconstruct a neuron based on automated searching of the optimal ¡®paths' connecting those detected 3D tips. The experiments demonstrate the effectiveness of the proposed method in building a fully automatic neuron tracing system.
Towards a Multi-Terminal Video Compression Algorithm Using Epipolar Geometry

We present a novel distributed video coding algorithm based on transform coding of distributed sources and exploiting the geometrical relationships between the location of the sensors. The geometry is used to align the video sequences and distributed quantization of transform coefficients is used to eliminate spatial and inter-sensor redundancy. In contrast with most of the current video compression standards which only exploit spatial and temporal redundancy within each video sequence, we also consider the significant redundancy between the sequences. Results demonstrate that our algorithm yields a significant saving in bit rate on the overlapping portion of multiple views
Optimal Landmark Selection for Registration of 4D Confocal Image Stacks in Arabidopsis

Technologically advanced imaging techniques have allowed us to generate and study the internal part of a tissue over time by capturing serial optical images that contain spatio-temporal slices of hundreds of tightly packed cells. Image registration of such live-imaging datasets of developing multicelluar tissues is one of the essential components of all image analysis pipelines. In this paper, we present a fully automated 4D(X-Y-Z-T) registration method of live imaging stacks that takes care of both temporal and spatial misalignments. We present a novel landmark selection methodology where the shape features of individual cells are not of high quality and highly distinguishable. The proposed registration method finds the best image slice correspondence from consecutive image stacks to account for vertical growth in the tissue and the discrepancy in the choice of the starting focal point. Then, it uses local graph-based approach to automatically find corresponding landmark pairs, and finally the registration parameters are used to register the entire image stack. The proposed registration algorithm combined with an existing tracking method is tested on multiple image stacks of tightly packed cells of Arabidopsis shoot apical meristem and the results show that it significantly improves the accuracy of cell lineages and division statistics.
The joint illumination and motion space of video sequences

It has been proved that the set of all Lambertian reflectance functions obtained with arbitrarily distant light sources lies close to a 9D linear subspace. We extend this result from still images to video sequences. We show that the set of all Lambertian reflectance functions of a moving object at any position, illuminated by arbitrarily distant light sources, lies close to a bilinear subspace consisting of nine illumination variables and six motion variables. This result implies that, when the position and 3D model of an object at one instance of time is known, the reflectance images at future time instances can be estimated using the bilinear subspace. This is based on the fact that, given the illumination direction, the image of a moving surface cannot change suddenly over a short time period. We apply our theory to synthesize video sequences of a 3D face with various combinations of motion and illumination directions.
Context Aware Active Learning of Activity Recognition Models

Activity recognition in video has recently benefited from the use of the context e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled and entirely available at the outset. In contrast, we formulate a continuous learning framework for context aware activity recognition from unlabeled video data which has two distinct advantages over most existing methods. First, we propose a novel active learning technique which not only exploits the informativeness of the individual activity instances but also utilizes their contextual information during the query selection process, this leads to significant reduction in expensive manual annotation effort. Second, the learned models can be adapted online as more data is available. We formulate a conditional random field (CRF) model that encodes the context and devise an information theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative query instances, which need to be labeled by a human. These labels are combined with graphical inference techniques for incrementally updating the model as new videos come in. Experiments on four challenging datasets demonstrate that our framework achieves superior performance with significantly less amount of manual labeling.
Dynamic modeling of streaklines for motion pattern analysis in video

Natural videos consist of multiple objects interacting with each other in complex ways. Analysis of such videos is a very challenging problem. The underlying patterns of motion contain valuable information about the activities and hence, it is necessary to devise efficient mechanisms for extraction and analysis of these patterns. Tracking is often infeasible in these scenarios. Recently, flow-based modeling methods based on the concept of streaklines in fluid mechanics have been proposed to deal with videos of crowds. In this paper, we show how the streaklines representation can be combined with dynamical systems modeling approaches in order to analyze the motion patterns in a wide range of natural videos. This combination provides a powerful tool to identify similar segments in a video that exhibit similar motion patterns. We show results on challenging datasets by demonstrating unsupervised clustering of similar motion patterns (which often represent similar activity patterns) and detection of changes in the video.
Distributed Multi-Target Tracking and Data Association in Vision Networks

Distributed algorithms have recently gained immense popularity. With regards to computer vision applications, distributed multi-target tracking in a camera network is a fundamental problem. The goal is for all cameras to have accurate state estimates for all targets. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. Vision-based distributed multi-target state estimation has at least two characteristics that distinguishes it from other applications. First, cameras are directional sensors and often neighboring sensors may not be sensing the same targets, i.e., they are naive with respect to that target. Second, in the presence of clutter and multiple targets, each camera must solve a data association problem. This paper presents an information-weighted, consensus-based, distributed multi-target tracking algorithm referred to as the Multi-target Information Consensus (MTIC) algorithm that is designed to address both the naivety and the data association problems. It converges to the centralized minimum mean square error estimate. The proposed MTIC algorithm and its extensions to non-linear camera models, termed as the Extended MTIC (EMTIC), are robust to false measurements and limited resources like power, bandwidth and the real-time operational requirements. Simulation and experimental analysis are provided to support the theoretical results.
Wide baseline image registration using prior information

Establishing correspondence between features in two images of the same scene taken from different viewing angles in a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, 3D model alignment, creation of panoramic views etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching 2D shapes of the different features of the face. A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellations of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.
A measure of deformability of shapes, with applications to human motion analysis

In this paper we develop a theory for characterizing how deformable a shape is. We define a term called "deformability index" for shapes. The deformability index is computed from the tracked positions of a sequence of deformable shapes, using a scaled orthographic camera projection model. Our method assumes that a deformable shape sequence can be represented by a linear combination of basis shapes, where the weights assigned to each basis shape changes with time. The tracked points obtained from the shape sequence is transformed to a 3D shape space. Using statistical models to separate out the "true" deformations from those induced by noise in the trajectories, the dimension of this shape space is estimated using spectral analysis methods. The dimension of this shape space determines the number of basis shapes needed to represent the shape sequence, which, in turn, determines the deformability index. Rigid 3D transformations of the shape are taken into account in estimating the deformability index; however, the method does not require estimation of 3D structure or motion. Experimental results are shown using motion capture data as well as real imagery of different human activities. The results show that the deformability index is in accordance with our intuitive judgement and corroborates certain hypotheses in human movement analysis studies.