nils-herrmann/clean_scirepeval huggingface dataset:
Tracking areas of interest for content-based functionalities in segmentation-based video coding

This paper presents a technique for tracking areas of interest in the framework of segmentation-based video coding. The technique is independent of the type of segmentation technique used in the coding approach. Therefore, it can also be used in block-based coding schemes. The algorithm relies on a double segmentation of the image based on morphological tools. This double segmentation permits to obtain the position and shape of the previous area of interest in the current image. In order to demonstrate the potentialities of this algorithm, it is applied in a specific coding scheme so that content-based selective coding is allowed.
Tracking of generic objects for video object generation

A tracking technique for creating generic video objects is presented. It assumes that the object in the initial image has been previously defined by an object partition. The object tracking relies on the concept of partition projection. The projection of a partition accommodates the previous partition information into the current image. The object partition is re-segmented so a texture partition is created. The texture partition guarantees the spatial homogeneity of each region and it is projected on the following image using motion information. Projected regions are used as markers and, to validate the position of the markers, they are fit into an intra fine partition of the current image. The texture partition of the current image is obtained by a modified watershed algorithm based on the fit markers. Finally, the current object partition is obtained by a labeling process.
Interpolation and extrapolation of image partitions using Fourier descriptors: application to segmentation-based coding schemes

This paper presents an interpolation/extrapolation technique for sequence partitions. It consists of four steps: region parametrization, region interpolation, region ordering and partition creation. The evolution of each region is divided into two types: regular motion and random deformations. Both types of evolution are parametrized by means of the Fourier descriptors of the regions and they are separately interpolated in the Fourier domain. The final interpolated partition is built from the ordered combination of the interpolated regions, using morphological tools.
Partition tree for a segmentation-based video coding system

In this paper we describe a very low bit rate video coding system that belongs to the class of object based coding systems. The system takes advantage of homogeneity in both gray level and motion, trying to use the most adequate criterion for defining a partition in order to obtain the best compromise between cost and quality. In order to do so, multiple partitions are generated for every image in a hierarchical way, from the coarsest one to the finest one. Then, a decision step will chose which parts of the image need to be coded with regions from one partition or from another. The aim of the paper is to study the features that this multiple partition must have, and propose a way to create it.
A segmentation-based coding system allowing manipulation of objects (SESAME)

We present a coding scheme that achieves, for each image in the sequence, the best segmentation in terms of rate-distortion theory. It is obtained from a set of initial regions and a set of available coding techniques. The segmentation combines spatial and motion criteria. It selects at each area of the image the most adequate criterion for defining a partition in order to obtain the best compromise between cost and quality. In addition, the proposed scheme is very suitable for addressing content-based functionalities.
A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery

An automatic approach for high accuracy registration of multisensor and multitemporal remote sensing images is presented. It avoids the use of ground control points, while exploiting the maximum reliable information in both images. Features to be used for image registration are those contours in both images that have been classified as coastline (reliable information). The automatic contour-based approach is summarized by the following steps: (i) reference image coastline extraction; (ii) sensed image gradient energy map estimation; (iii) contour matching, mapping function estimation and transformation of the sensed images. The algorithm proposed is automatic and of significant value in an operational context. Several experimental results for single sensor imagery (AVHRR) from different viewpoints and dates as well as multisensor imagery (AVHRR-SeaWiFS) have verified the robustness and accuracy of the proposed automatic registration algorithm, demonstrating its capability of registering satellite images of coastal areas within one pixel.
Composite Object Detection in Video Sequences: Application to Controlled Environments

This paper presents a set of techniques for the detection of composite objects in video recordings of a controlled environment. Firstly, a selective region-based analysis is performed by tuning the algorithm to the perceptual characteristics of the object in the environment. Secondly, the controlled perceptual and semantic variabilities of the object are addressed by the detection analysis thanks to a frame by frame update of the object models, and by allowing multiple models for a single object. The proposed techniques are illustrated in the detection of laptops from a zenithal view in a smart room.
Supervised Evaluation of Image Segmentation and Object Proposal Techniques

This paper tackles the supervised evaluation of image segmentation and object proposal algorithms. It surveys, structures, and deduplicates the measures used to compare both segmentation results and object proposals with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the quality of these measures, eight state-of-the-art object proposal techniques are analyzed and two quantitative meta-measures involving nine state of the art segmentation methods are presented. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion of the performed experiments, this paper proposes the tandem of precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available.
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five seconds per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.
Contour detection using Binary Partition Trees

Contour detection is a hard, challenging, and of paramount importance problem in image processing. State-of-the-art algorithms are approaching human performance but usually entail complex and tailored image models and arduous training.  Tree is a hierarchical region-based image model that has been proven to have a wide range of applications in image filtering, information retrieval, object detection, etc. In this paper we propose a contour detection technique based on this versatile image model, extracting the contour information available in the tree yet outperforming one of the most widely-used contour detector.
Hierarchical fusion of color and depth information at partition level by cooperative region merging

A high level scheme for information fusion to create hierarchical region-based image representations based on a region merging process is presented. The strategy is based on an iterative evolution where the different merging criteria work independently and cooperate at the partition level to obtain a further consensus that increases the reliability of the resulting partitions. This cooperative scheme is applied to the creation of hierarchical region-based representations of the image based on color and depth information. The proposed technique is compared with approaches using only one source of information or linear combinations of both, in datasets with ground truth as well as estimated disparity information.
Precise upwelling and filaments automatic extraction from multisensorial imagery

The upward movement of cool and nutrient-rich waters towards the surface leads to horizontal alterations in the distribution of physical, chemical and biological properties. Remote sensing is being extensively applied to detect such coastal upwellings; however, the enormous amount of data daily generated obliges to develop automatic detection and prediction tools. The problem of identifying oceanographic mesoscale structures has been studied using a variety of image processing techniques, however, the outstanding difficulties encountered in the traditional approaches are the presence of noise, mainly due to the clouds and other atmospheric phenomena; the fact that gradients are weak and provide excess of information; the strong morphological variation that impedes an accurate geometric representation and the absence of a valid analytical model for the structures. In this context, the proposed automatic upwelling extraction methodology overcomes the preceding detection inconveniences and achieves a highly accurate structure detection and identification. This automatic technique has been applied to the detection and feature extraction of coastal upwellings and filaments in the northwest African coast, the Alboran Sea and Cape Ghir using imagery from the AVHRR/2&3, SeaWiFS and MODIS sensors. The system has proven to be very effective and robust in a wide variety of climate conditions.
A Hierarchical Trajectory-Based Representation for Video

This paper proposes the Trajectory Tree as a hierarchical representation for video sequences containing multiple layers of resolution and various degrees of spatio-temporal consistency. We present a technique for generating an initial set of trajectories through partition tracking. Trajectories are formed of temporally linked regions homogeneous in color and coherent in terms of affine motion models. Next, merging algorithms exploiting homogeneity criteria over multiple frames are applied towards the construction of hierarchical representations. Mechanisms for guaranteeing spatio-temporal continuity are introduced, including temporal splitting and long-term color-based merging. The final hierarchy is constructed with the sequential application of novel long-term affine-based and translation-based motion similarity measures. The use of global motion characteristics allows the consistent segmentation and tracking of moving objects and their sub-regions, including cases of non-rigid motion and objects which stop moving for several frames. Experimental results are presented for the Table Tennis, Foreman, Coastguard, Mother and Daughter and Speedway sequences.
Hierarchical segmentation of vegetation areas in high spatial resolution images by fusion of multispectral information

A new region-based methodology for the automated extraction and hierarchical segmentation of vegetation areas into high spatial resolution images is proposed. This approach is based on the iterative and cooperative fusion of the independent segmentation results of equal or different resolution spectral bands, combined with an unsupervised classification into vegetation and no-vegetation regions. The result is a hierarchy of partitions with most relevant information at different levels of resolution of the vegetation areas. In addition, the high flexibility of the scheme allows different configurations depending on the final purpose. For instance, considering the size of the vegetation areas into the hierarchy, or prioritizing the information into the high resolution panchromatic band to improve the accuracy of both vegetation extraction and segmentation. This general tool for vegetation analysis is tested into high spatial resolution images from IKONOS and QuickBird satellites.
On Building a Hierarchical Region-Based Representation for Generic Image Analysis

This paper studies the procedure to create a hierarchical region-based image representation aiming at generic image analysis. This study is carried out in the context of bottom-up segmentation algorithms and, specifically, using the Binary Partition Tree implementation. The different steps necessary to create a hierarchical region-based representation are analyzed; namely, (i) the creation of the initial partition in the hierarchy, which is split into the definition of the initial merging criterion and the proposal of a stopping criterion, and (ii) the merging criteria used to produce the different regions in the final hierarchical representation. For both steps, the proposed approach is assessed and compared with previous existing ones over a large data set using well-established partition-based metrics.
Coding-oriented segmentation based on Gibbs-Markov random fields and human visual system knowledge

A new segmentation algorithm for still black and white images is introduced. This algorithm forms the basis of a region-oriented sequence coding technique, currently under development. The algorithm models the human mechanism of selecting regions both by their interior characteristics and their boundaries. This is carried out in two different stages: with a preprocessing that takes into account only gray level information, and with a stochastic model for segmented images that uses both region interior and boundary information. In the stochastic model, the gray level information within the regions is modeled by stationary Gaussian processes, and the boundary information by a Gibbs-Markov random field (GMRF). The segmentation is carried out by finding the most likely realization of the joint process (maximum a posteriori criterion), given the preprocessed image. For decreasing the computational load while avoiding local maxima in the probability function, suboptimal versions of the algorithm are proposed.<<ETX>>
A motion-based binary partition tree approach to video object segmentation

This paper describes an approach for generating binary partition tree [P. Salembier and L. Garrido, 2000] representations and video object segmentations using a novel region merging strategy based on motion similarity measures of multiple frames of an image sequence. The system operates over color-homogeneous regions, tracked across frames of a shot, representing an over-segmentation of the objects. A long-term motion similarity measure is introduced for region merging, offering accurate segmentation of objects and extending temporal consistency between the tracked partitions to hierarchical representations of every frame within the shot. Experimental results are presented, illustrating the usefulness of the approach.
Region merging parameter dependency as information diversity to create sparse hierarchies of partitions

Region merging techniques usually include parameters that may be used to optimize or adapt the algorithm to a specific image type. Although, an appropriate tuning may provide a significant improvement, it also introduces a severe performance dependency on the parameter setting. The goal of this work is to transform the parameter dependency into an increase of accuracy and stability of the segmentation results. The idea is to use different parameter settings as specific type of diversity in an information fusion process based on a cooperative region merging approach. The potential of this parameter removal strategy is objectively evaluated on a set of state-of-the-art information theoretical region merging techniques for the removal of parameters: (i) in the region model, and (ii) in the merging order.
Silhouette-based probabilistic 2D human motion estimation for real-time applications

This paper presents a novel technique for 2D human motion estimation using a single non calibrated camera. The user's five crucial human features (head, hands and feet) are extracted, labeled and tracked, after silhouette segmentation. The crucial points candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region (silhouette) following the silhouette boundary. Selected crucial points are then classified as head, hands or feet using a probabilistic approach weighted by a prior human model. The system can run at 50 Hz paces on standard personal computers.
Pixel and sub-pixel accuracy in satellite image georeferencing using an automatic contour matching approach

This paper presents a technique for a fully automatic and operational geometric correction system capable of georeferencing satellite images with high accuracy. A simple Keplerian orbital satellite model is considered and mean orbital elements are given as input from ephemeris data. To correct the systematic errors caused by these simplifications, nonzero values for the spacecraft roll, pitch and yaw and failures in the satellite internal clock, an automatic global contour matching approach is proposed. It has three main steps: (i) estimation of the gradient energy map (edges) and detection of the cloudless (reliable) areas; (ii) initialization of the contour positions; (iii) obtaining the transformation parameters (affine model) by means of a global contour optimization approach. Three different algorithms are proposed for optimization. The performance of the overall technique is assessed using AVHRR and multisensor AVHRR-SeaWiFS imagery.
Detection of semantic objects using description graphs

This paper presents a technique to detect instances of classes (objects) according to their semantic definition in the form of a description graph. Classes are defined as combinations of instances of lower level semantic classes and allow the definition of a semantic tree that organizes classes in semantic levels. At the bottom level of the semantic tree, classes are defined by a perceptual model containing a list of low-level descriptors. The proposed detection algorithm follows a bottom-up/top-down approach, building semantic trees on a region-based representation of the media. The flexibility of the approach is assessed on different examples of planar objects, such as frontal faces, groups of islands, flags and traffic signs.
Prefetching and Caching Strategies for Remote and Interactive Browsing of JPEG2000 Images

This paper considers the issues of scheduling and caching JPEG2000 data in client/server interactive browsing applications, under memory and channel bandwidth constraints. It analyzes how the conveyed data have to be selected at the server and managed within the client cache so as to maximize the reactivity of the browsing application. Formally, to render the dynamic nature of the browsing session, we assume the existence of a reaction model that defines when the user launches a novel command as a function of the image quality displayed at the client. As a main outcome, our work demonstrates that, due to the latency inherent to client/server exchanges, a priori expectation about future navigation commands may help to improve the overall reactivity of the system. In our study, the browsing session is defined by the evolution of a rectangular window of interest (WoI) along the time. At any given time, the WoI defines the position and the resolution of the image data to display at the client. The expectation about future navigation commands is then formalized based on a stochastic navigation model, which defines the probability that a given WoI is requested next, knowing previous WoI requests. Based on that knowledge, several scheduling scenarios are considered. The first scenario is conventional and transmits all the data corresponding to the current WoI before prefetching the most promising data outside the current WoI. Alternative scenarios are then proposed to anticipate prefetching, by scheduling data expected to be requested in the future before all the current WoI data have been sent out. Our results demonstrate that, for predictable navigation commands, anticipated prefetching improves the overall reactivity of the system by up to 30% compared to the conventional scheduling approach. They also reveal that an accurate knowledge of the reaction model is not required to get these significant improvements
Hierarchical Partition-Based Representations for Image Sequences using Trajectory Merging Criteria

This paper describes a hierarchical analysis framework for image sequences. Region merging schemes traditionally used in the construction of partition hierarchies are extended to multiple frames using trajectory merging criteria. The merging criteria assess homogeneity among features throughout the entire sequence to recursively create partitions in the spatio-temporal domain. We propose similarity measures using long-term affine and translational motion features. Furthermore, the analysis of connectivity relations and the algorithm implementation over trajectory adjacency graphs allow the generation of partition sets containing temporally consistent objects characterized by coherent motion. Lastly, we introduce the novel trajectory tree as a single, hierarchical representation of the partitions generated for the complete sequence. Experimental results are provided, illustrating the usefulness of the approach.
Caption text extraction for indexing purposes using a hierarchical region-based image model

This paper presents a technique for detecting caption text for indexing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image description which, in our case, is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking advantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects.
Multi-grid chain coding of binary shapes

This paper presents a chain code based approach to efficiently code binary shape information of video objects, in the context of object-based video coding. The proposed method tries to meet some of the requirements of the MPEG-4 standard, currently under development, notably efficient coding, and low delay. This approach allows several modes of operation depending on the application requirements, notably lossless, near-lossless, and lossy coding modes. For the lossless case a pure differential chain code method is proposed while for the near-lossless case a multi-grid chain code (MGCC) technique is adopted. Also both INTRA and INTER prediction modes can be used. For the INTER mode, motion compensation is applied without coding the residues. The MGCC is a near-lossless contour coding technique using a contour description based on edges, which combines both contour prediction and contour simplification.