nils-herrmann/clean_scirepeval huggingface dataset:
A lightweight multiview tracked person descriptor for camera sensor networks

We present a simple multiple view 3D model for object tracking and identification in camera networks. Our model is composed of 8 distinct views in the interval [0, 7pi/4]. Each of the 8 parts describes the person's appearance from that particular viewpoint. The model contains both color and structure information about each view which are assembled into a single entity and is meant as a simple, lightweight object representation for use in camera sensor networks. It is versatile in that it can be gradually assembled on-line while a person is tracked. The model's ease of use and effectiveness for identification in surveillance video is demonstrated.
Multi-Focus Imaging using Local Focus Estimation and Mosaicking

We propose an algorithm to generate one multi-focus image from a set of images acquired at different focus settings. First images are registered to avoid large misalignments. Each image is tiled with overlapping neighborhoods. Then, for each region the tile that corresponds to the best focus is chosen to construct the multi-focus image. The overlapping tiles are then seamlessly mosaicked. Our approach is presented for images from optical microscopes and hand held consumer cameras, and demonstrates robustness to temporal changes and small misalignments. The implementation is computationally efficient and gives good results.
Retrieval of Images with Objects of Specific Size, Location, and Spatial Configuration

An approach to image retrieval using spatial configurations is presented. The goal is to search the database for images that contain similar objects (image-patches) with a given configuration, size and position. The proposed approach consists of creating localized representations robust to segmentation variations, and a sub-graph matching method to compare the query with the database items. Localized object representations are created using a community detection method that groups visually similar segments. Extensive experimental results on three challenging datasets are provided to demonstrate the feasibility of the approach.
UAVSensor Fusion with Latent-Dynamic Conditional Random Fields in Coronal Plane Estimation

We present a real-time body orientation estimation in a micro-Unmanned Air Vehicle video stream. This work is part ofafully autonomous UAVsystem which can maneuver to face a single individual in challenging outdoor environments. Our body orientation estimation consists of the following steps: (a) obtaining a set ofvisual appearance models for each body orientation, where each model is tagged with a set of scene information (obtained from sensors), (b) exploiting the mutual information of on-board sensors using latent-dynamic conditional random fields (WCRF), (c) Characterizing each visual appearance model with the most discriminative sensor information, (d) fast estimation ofbody orientation during the test flights given theWCRF parameters and the corresponding sensor readings. The key aspects of our approach is to add sparsity to the sensor readings with latent variables followed by long range dependency analysis. Experimental results obtained over real-time video streams demonstrate a significant improvement in both speed (l5-fps) and accuracy (72%) compared to the state of the art techniques that only rely on visual data. Video demonstration ofour autonomous flights (both from ground view and aerial view) are included in the supplementary material.
Model-based detection and correction of corrupted wavelet coefficients

Image decomposition based on the discrete wavelet transform (DWT) has been proposed for efficient storage and progressive transmission of images for visual browsing in digital image libraries. Although the compression aspects of the DWT have been carefully researched, reconstruction errors due to corrupted wavelet coefficients have received less attention. In this paper we consider the problem of bit errors affecting uniformly quantized wavelet coefficients. The proposed method, which is based on a local image model, simultaneously detects and masks corrupted wavelet coefficients.
A robust data hiding technique using multidimensional lattices

Describes a data hiding technique which uses noise-resilient channel codes based on multidimensional lattices. A trade-off between 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 show that the watermarked image is transparent to embedding for large amounts of hidden data, and the quality of the extracted signature is high even when the watermarked image is subjected to up to 75% wavelet compression and 85% JPEG lossy compression. These results can be combined with a private key-based scheme to make unauthorized retrieval practically impossible, even with the knowledge of the algorithm.
An efficient color representation for image retrieval

A compact color descriptor and an efficient indexing method for this descriptor are presented. The target application is similarity retrieval in large image databases using color. Colors in a given region are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the region. A similarity measure similar to the quadratic color histogram distance measure is defined for this descriptor. The representative colors can be indexed in the three-dimensional (3-D) color space thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The matches from all of the query colors are then combined to obtain the final retrievals. An efficient indexing scheme for fast retrieval is presented. Experimental results show that this compact descriptor is effective and compares favorably with the traditional color histogram in terms of overall computational complexity.
Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking

In the future, the video-enabled camera will be the most pervasive type of sensor in the Internet of Things. Such cameras will enable continuous surveillance through heterogeneous camera networks consisting of fixed camera systems as well as cameras on mobile devices. The challenge in these networks is to enable efficient video analytics: the ability to process videos cheaply and quickly to enable searching for specific events or sequences of events. In this paper, we discuss the design and implementation of Kestrel, a video analytics system that tracks the path of vehicles across a heterogeneous camera network. In Kestrel, fixed camera feeds are processed on the cloud, and mobile devices are invoked only to resolve ambiguities in vehicle tracks. Kestrel's mobile device pipeline detects objects using a deep neural network, extracts attributes using cheap visual features, and resolves path ambiguities by careful association of vehicle visual descriptors, while using several optimizations to conserve energy and reduce latency. Our evaluations show that Kestrel can achieve precision and recall comparable to a fixed camera network of the same size and topology, while reducing energy usage on mobile devices by more than an order of magnitude.
Robust Segmentation Based Tracing Using an Adaptive Wrapper for Inducing Priors

Segmentation based tracing algorithms detect the extent and borders of an object in a given frame IZ by propagating results from frames . Although application specific tracers have been forthcoming, techniques that automatically adapt across applications have been less explored. We approach this problem by learning a prior model on topological dynamics that encourages segmentation transitions across frames that are most likely for a given application. Further, we augment a generic tracing technique with a locality sensitive prior derived from dense optic flow fields for deformation guidance. The proposed approach comprises two stages where the generic tracer initially yields multiple segmentation transitions when its parameters are perturbed, and the learnt topology prior subsequently propagates high scoring segmentations. Because the learnt topology model wraps around a generic tracer and adapts it by setting its free parameters, the need for careful parameter tuning is completely obviated. Through extensive experimental validation in surveillance, biological and medical image datasets, we verify the applicability of the proposed model while demonstrating good tracing performance under severe clutter.
Pairwise Similarities across Images for Multiple View Rigid/Non-Rigid Segmentation and Registration

A variational approach for the background/foreground segmentation of multiple views of the same scene is presented. The main novelty is the introduction of cost functions based on pairwise similarity between pixels across different images. These cost functions are minimized within a level set framework. In addition, a warping model (rigid or non-rigid) between the emerging foregrounds in the different views is imposed, thus avoiding the introduction of a specific shape term in the cost function to handle occlusions. The thin plate spline (TPS) warping is for the first time employed within the level set framework to model non- rigid deformations. The minimization of these cost functions leads to simultaneous segmentation and registration of the different views. Examples of segmentations of a variety of objects are shown and possible applications are proposed.
Learning bottom-up text attention maps for text detection using stroke width transform

Humans have a remarkable ability to quickly discern regions containing text from other noisy regions in images. The primary contribution of this paper is to learn a model to mimic this behavior and aid text detection algorithms. The proposed approach utilizes multiple low level visual features which signify visually salient regions and learns a model to eventually provide a text attention map which indicates potential text regions in images. In the next stage, a text detector using stroke width transform only focusses on these selective image regions achieving dual benefits of reduced computation time and better detection performance. Experimental results on the ICDAR 2003 text detection dataset demonstrate that the proposed method outperforms the baseline implementation of stroke width transform, and the generated text attention maps compare favorably with human fixation maps on text images.
Reversible wavelet and spectral transforms for lossless compression of color images

A new lossless image compression method for progressive-resolution transmission of color images is proposed. It is based on spatial and spectral transforms. Reversible wavelet transforms are performed across the red, green, and blue color components first. Then adaptive spectral transforms are applied to the associated color subbands. Simulation results indicate that the proposed algorithm can achieve bit rates that are about 20% lower than results obtained with comparable lossless image compression techniques supporting progressive-resolution transmission.
Marine Biodiversity Classification Using Dropout Regularization

Coastal marine ecosystems are highly productive and diverse, but biodiversity of underwater habitats is poorly described due to logistical and financial limitations of diving and submersible operations. Imagery is a promising way to address this challenge, but the complexity of diverse organisms thwarts simple automated analysis. We consider the problem of automated annotation of complex communities of sessile marine invertebrates and macroalgae in order to automate percent coverage estimation. We propose an efficient fusion technique amongst diverse classifiers based on the idea of "dropout" in machine learning. We use dropout technique to weight each classifier implicitly and for each specie we optimize the region of interest (ROI) for highest accuracy. The preliminary results are promising and show 20% increase in average accuracy (over 30 species) when compared with the best base performance of Random Forest classifiers. The data set along with human "ground truth" annotations are available to the public.
A Variational Approach to Exploit Prior Information in Object-Background Segregation: Application to Retinal Images

One of the main challenges in image segmentation is to adapt prior knowledge about the objects/regions that are likely to be present in an image, in order to obtain more precise detection and recognition. Typical applications of such knowledge-based segmentation include partitioning satellite images and microscopy images, where the context is generally well defined. In particular, we present an approach that exploits the knowledge about foreground and background information given in a reference image, in segmenting images containing similar objects or regions. This problem is presented within a variational framework, where cost functions based on pair-wise pixel similarities are minimized. This is perhaps one of the first attempts in using non-shape based prior information within a segmentation framework. We validate the proposed method to segment the outer nuclear layer (ONL) in retinal images. This approach successfully segments the ONL within an image and enables further quantitative analysis.
Unified probabilistic framework for simultaneous detection and tracking of multiple objects with application to bio-image sequences

We present a detection based tracking algorithm for tracking melanosomes (organelles containing melanin) in time lapse image sequences imaged using bright field microscopy. Due to heavy imaging noise detecting all the melanosomes accurately in every frame is difficult. Therefore, two sets of imperfect detections are used in a unified probabilistic approach to simultaneously perform melanosome detection and tracking. We propose a novel iterative algorithm which jointly estimates the optimal set of detections and track results in every iteration from the previous tracks and detections. Our algorithm obtains significantly better tracking results than the state of the art tracking-by-detection algorithm.
Evaluation and benchmark for biological image segmentation

This paper describes ongoing work on creating a benchmarking and validation dataset for biological image segmentation. While the primary target is biological images, we believe that the dataset would be of help to researchers working in image segmentation and tracking in general. The motivation for creating this resource comes from the observation that while there are a large number of effective segmentation methods available in the research literature, it is difficult for the application scientists to make an informed choice as to what methods would work for her particular problem. No one single tool exists that is effective on a diverse set of application contexts and different methods have their own strengths and limitations. We describe below three different classes of data, ranging in scale from subcellular to cellular to tissue level images, each of which pose their own set of challenges to image analysis. Of particular value to the image processing researchers is that the data comes with associated ground truth information that can be used to evaluate the effectiveness of different methods. The analysis and evaluation are also integrated into a database framework that is available online at http://dough.ece.ucsb.edu.
Design and implementation of a wide area, large-scale camera network

We describe a wide area camera network on a campus setting, the SCALLOPSNet (Scalable Large Optical Sensor Network). It covers with about 100 stationary cameras an expansive area that can be divided into three distinct regions: inside a building, along urban paths, and in a remote natural reserve. Some of these regions lack connections for power and communications, and, therefore, necessitate wireless, battery-powered camera nodes. In our exploration of available solutions, we found existing smart cameras to be insufficient for this task, and instead designed our own battery-powered camera nodes that communicate using 802.11b. The camera network uses the Internet Protocol on either wired or wireless networks to communicate with our central cluster, which runs cluster and cloud computing infrastructure. These frameworks like Apache Hadoop are well suited for large distributed and parallel tasks such as many computer vision algorithms. We discuss the design and implementation details of this network, together with the challenges faced in deploying such a large scale network on a research campus. We plan to make the datasets available for researchers in the computer vision community in the near future.
Estimating Steganographic Capacity for Odd-Even Based Embedding and its Use in Individual Compensation

We present a method to compute the steganographic capacity for images, with odd-even based hiding in the quantized discrete cosine transform domain. The method has been generalized for varying orders of co-occurrence statistics for statistical restoration based steganography. We further utilize this capacity estimate to hide the maximum possible data per individual frequency stream, while ensuring that the first order histograms of individual frequency coefficients remain matched. We also show that certain frequency components are more useful for steganalysis after first order statistical restoration is performed for a certain band of select frequencies.
Conditional iterative decoding of Two Dimensional Hidden Markov Models

Two dimensional hidden markov models (2D-HMMs) provide substantial benefits for many computer vision and image analysis applications. Many fundamental image analysis problems, including segmentation and classification, are target applications for the 2D- HMMs. As opposed to the i.i.d. assumption of the image observations, the naturally existing spatial correlations can be readily modeled by solving the 2D-HMM decoding problem. However, computational complexity of the 2D-HMM decoding grows exponentially with the image size and is known to be NP-hard. In this paper, we present a conditional iterative decoding (CID) algorithm for the approximate decoding of 2D-HMMs. We compare the performance of the CID algorithm to the Turbo-HMM (T-HMM) decoding algorithm and show that CID gives promising results. We demonstrate the proposed algorithm on modeling spatial deformations of human faces in recognizing people across their different facial expressions.
A Condition Number for Point Matching with Application to Registration and Postregistration Error Estimation

Selecting salient points from two or more images for computing correspondence is a well-studied problem in image analysis. This paper describes a new and effective technique for selecting these tiepoints using condition numbers, with application to image registration and mosaicking. Condition numbers are derived for point-matching methods based on minimizing windowed objective functions for 1) translation, 2) rotation-scaling-translation (RST), and 3) affine transformations. Our principal result is that the condition numbers satisfy K/sub Trans/ /spl les/ K/sub RST/ /spl les/ K/sub Affine/. That is, if a point is ill-conditioned with respect to point-matching via translation, then it is also unsuited for matching with respect to RST and affine transforms. This is fortunate since K/sub Trans/ is easily computed whereas K/sub RST/ and K/sub Affine/ are not. The second half of the paper applies the condition estimation results to the problem of identifying tiepoints in pairs of images for the purpose of registration. Once these points have been matched (after culling outliers using a RANSAC-like procedure), the registration parameters are computed. The postregistration error between the reference image and the stabilized image is then estimated by evaluating the translation between these images at points exhibiting good conditioning with respect to translation. The proposed method of tiepoint selection and matching using condition number provides a reliable basis for registration. The method has been tested on a large number of diverse collection of images - multidate Landsat images, aerial images, aerial videos, and infrared images. A Web site where the users can try our registration software is available and is being actively used by researchers around the world.
Context-aware graph modeling for object search and retrieval in a wide area camera network

This paper addresses the problem of context-aware object search and retrieval in a wide area distributed camera network. With the proliferation of smart cameras in urban networks, it is a challenge to process this big data in an efficient manner. A novel graph based model is proposed to represent relationships, and for search and retrieval tasks. This representation exploits the fact that objects occurring in close spatial-temporal proximity are not completely independent and serve as context for each other. Additional information such as appearance and scene context can also be encoded into the graph model to improve the overall accuracy. A manifold ranking strategy is used to order the items based on similarity with an emphasis on diversity. Extensive experimental results on a ten camera network are presented.
Rotation-invariant texture classification using modified Gabor filters

A method of rotation invariant texture classification based on a joint space-frequency model is introduced. Multiresolution filters, based on a truly analytic form of a polar 2-D Gabor (1946) wavelet, are used to compute spatial frequency-specific but spatially localized microfeatures. These microfeatures constitute an approximate basis set for the representation of the texture sample. The essential characteristics of a texture sample, its macrofeatures, are derived from the statistics of its microfeatures. A texture is modeled as a multivariate Gaussian distribution of macrofeatures. Classification is based on a rotation invariant subset of macrofeatures.
PixNet: A Localized Feature Representation for Classification and Visual Search

This paper presents a novel localized visual image feature motivated by image segmentation. The proposed feature embeds relative spatial information by learning different image parts while having a compact representation. First, an attributed graph representation of an image is created based on segmentation and localized image features. Subsequently, communities of image regions are discovered based on their spatial and visual characteristics over all images. The community detection problem is modeled as a spectral graph partitioning problem. This results in finding meaningful image part groupings . A histogram of communities forms a robust and spatially localized representation for each image in the database. Such a region-based representation enables one to search for queries that might not have been possible with global image representations. We apply this representation to image classification and search and retrieval tasks. Extensive experiments on three challenging datasets, including the large-scale ImageNet dataset, demonstrate that the proposed representation achieves promising results compared to the current state-of-the-art methods.
Affine-invariant curve matching

In this paper, we propose, an affine-invariant-method for describing and matching curves. This is important since affine transformations are often used to model perspective distortions. More specifically, we propose a new definition of the shape of a curve that characterizes a curve independently of the effects introduced by affine distortions. By combining this definition with a rotation-invariant shape descriptor, we show how it is possible to describe a curve in an intrinsically affine-invariant manner. To validate our procedure we built a database of shapes subject to perspective distortions and plotted the precision-recall curve for this dataset. Finally an application of our method is shown in the context of wide baseline matching.