nils-herrmann/clean_scirepeval huggingface dataset:
Optical flow estimation on Connection-Machine 2

The gradient-based methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. The structure of the constraint equation make the problem ill-posed so in the past solutions have been proposed some solutions based on regularization. On the contrary, under the assumption that in the immediate neighborhood of a pixel the optical flow field is smooth, the constraint equations in that neighborhood should have a common solution, in this case the problem is not ill-posed. Following this reasoning, an algorithm for evaluating the optical flow, which is suitable for parallel implementations of selected algorithms from the literature, for optical flow estimation, are presented with the intention of comparing their complexity and performance with respect to the proposed approach on a Connection Machine-2.
Integrating object oriented programming paradigm concepts in designing a vision and pattern recognition system architecture

A fully object-oriented approach to a general-purpose object-recognition system is presented. This, together with a blackboard architecture, provides a flexible and easily maintainable system. The knowledge information is distributed into two object-oriented databases: one, hierarchically structures into three layers according to visual abstractions, contains the object models (the knowledge source of the recognition process); the other, which reflects in the most natural way the problem domain knowledge, helps in the pruning of the decision trees and allows for the analysis of the behavior of the objects.<<ETX>>
Automatic trademark detection and recognition in sport videos

In this paper we describe a system for automatic detection and recognition of trademarks in sports videos. We propose a compact representation of trademarks based on SIFT feature points and a matching algorithm to robustly detect and retrieve trademarks in a variety of different sports video types. Trademark localization is performed through robust clustering of matched feature points in the video frame. A supervised machine learning approach is used to automatically adapt the similarity threshold used to assess the trademark matches. Experimental results are provided, along with an analysis of the precision and recall. Results show that our proposed technique is efficient and effectively detects and classifies trademarks.
Person Re-Identification by Iterative Re-Weighted Sparse Ranking

In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.
User interest profiling using tracking-free coarse gaze estimation

Understanding where people attention focuses is a challenging and extremely valuable task that can be solved using computer vision technologies. In this paper we address this problem on surveillance-like scenarios, where head and body imagery are usually low resolution. We propose a method to profile the attention of people moving in a known space. We exploit coarse gaze estimation and a novel model based on optical flow to improve attention prediction without the need of a tracker. Removing the tracker dependency makes the method applicable also on highly crowded scenarios. The proposed method is able to obtain comparable performance with respect to state of the art solutions in terms of Mean Average Angular Error (MAAE) on the TownCentre dataset. We also test our approach on the publicly available MuseumVisitors dataset showing an improvement both in terms of MAAE and in terms of accuracy in the estimation of visitors' profile.
Florence faces: A dataset supporting 2D/3D face recognition

This article describes a new dataset under construction at the Media Integration and Communication Center and the University of Florence. The dataset consists of high-resolution 3D scans of human faces from each subject, along with several video sequences of varying resolution and zoom level. Each subject is recorded in a controlled setting in HD video, then in a less-constrained (but still indoor) setting using a standard, PTZ surveillance camera, and finally in an unconstrained, outdoor environment with challenging conditions. In each sequence the subject is recorded at three levels of zoom. This dataset is being constructed specifically to support research on techniques that bridge the gap between 2D, appearance-based recognition techniques, and fully 3D approaches. It is designed to simulate, in a controlled fashion, realistic surveillance conditions and to probe the efficacy of exploiting 3D models in real scenarios.
An object-oriented framework for static and dynamic object recognition

A framework based on the object-oriented paradigm is presented. In the framework, object form and motion are treated in a uniform way and integrated in the reasoning scheme for the object recognition tasks. A modified object model is proposed to support the conceptual focus in the recognition task and to allow efficient information retrieval. Both spatial data and alphanumeric information are integrated in a consistent organization in order to facilitate object recognition. As each model is an individual entity which can convey its ability to match a spatial entity which was extracted from the image data, several of them could be active in the same time.<<ETX>>
Visual image retrieval by elastic deformation of object sketches

We present a method for image retrieval based on sketches of object shapes. In our method, the shape is drawn by the user, and instantiated as an elastic template. The template is then deformed to match as well as possible the objects in the images. The degree of match achieved, and the elastic deformation energy spent to achieve such a match are used as a measure of the similarity between the template and the image object. Images can be ranked according to the matching of their objects with the sketch, and the top ranking ones can be presented to the user for browsing. The elastic matching is integrated with arrangements to provide for scale invariance, to take into account rotations and spatial relationships between objects for multiple-object queries.<<ETX>>
Indexing Ensembles of Exemplar-SVMs with rejecting taxonomies

Ensembles of Exemplar-SVMs have been used for a wide variety of tasks, such as object detection, segmentation, label transfer and mid-level feature learning. In order to make this technique effective though a large collection of classifiers is needed, which often makes the evaluation phase prohibitive. To overcome this issue we exploit the joint distribution of exemplar classifier scores to build a taxonomy capable of indexing each Exemplar-SVM and enabling a fast evaluation of the whole ensemble. We experiment with the Pascal 2007 benchmark on the task of object detection and on a simple segmentation task, in order to verify the robustness of our indexing data structure with reference to the standard Ensemble. We also introduce a rejection strategy to discard not relevant image patches for a more efficient access to the data.
Image retrieval by positive and negative examples

Systems for content based image retrieval typically support access to database images through the query-by-example paradigm. This includes query-by-image and query-by-sketch. Since query-by-sketch can be difficult in some cases-lack of sketching abilities, difficulty to detect distinguishing image features-generally querying is performed through the query-by-image paradigm. A limiting factor of this paradigm is that a single sample image rarely includes all and only the characterizing elements the user is looking for. In this paper a system is presented that supports query-by-image using multiple image examples. Examples can be positive and negative and can be edited in order to disregard irrelevant image features.
Shape reconstruction from a single photograph for 3D object retrieval and visualization

We describe a geometric approach for reconstructing 3D textured graphical models of surfaces of revolution (SOR) from a single uncalibrated view. Metric reconstruction of 3D shape is complemented with the extraction of flattened 2D texture, so as to support visual retrieval from 2D/3D cues and to generate realistic 3D visualization models. The approach developed is quite simple, yet accurate and robust; its applications range from the preservation, analysis and classification of cultural heritage, to advanced graphics and multimedia.
Extracting semantic information from news and sport video

Development of systems supporting effective retrieval by content of videos requires the performance of a wide spectrum of operations on video streams, including temporal segmentation, analysis of the audio and video tracks, identification and recognition of text. Low level features are then processed to provide some higher level description of video content, as most of the user queries are typically related to higher level syntax and semantics, rather on the lower, lexical level. Moreover, the specificity of different application domains entails that different solutions be adopted in different contexts. This may affect both the choice of low level features to be extracted as well as the modeling of specific domain knowledge required to address the issue of higher level semantics. In this paper, we report on our experience in the application contexts of news and sports videos. We will show solutions adopted to cope with specific requirements of different application domains.
Using weighted spatial relationships in retrieval by visual contents

An original modeling technique is presented which enables quantitative representation and comparison of the mutual positioning of a pair of extended regions, and which is able to account for the overall distribution of relationships among the individual pixels belonging to the two regions. Properties of the representation are expounded to develop an efficient computation technique and to motivate and assess a metric of similarity for quantitative comparison of spatial relationships between region pairs. Representation and comparison of binary relationships between regions is then embedded within a graph-theoretical framework supporting representation and comparison of the spatial arrangements of images.
Real-time people counting from depth imagery of crowded environments

In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications.
Interprocess Communication Dependency on Network Load

Results of an analysis of the communication performance as perceived by the application layer in the presence of background load are presented. The analysis was carried out on two distinct protocol suites, TCP/IP and XNS, on a single Ethernet LAN with personal computer workstations. Service times for a reliable transfer and an unreliable transmission as perceived by the application layer were measured. An appropriate model of the reliable transfer process was used to derive service time analytical estimates. Owing to the close relation between measured and estimated values, the model was used to identify the major sources of delay and to evaluate the effects of possible improvements. The most important causes affecting the transport layer performance as background load increases are identified, and different strategies are examined in order to reduce their impact. The improvement margin offered by the implementation of a lightweight transport protocol specifically tailored to the requirements of a bulk data transfer over an Ethernet is addressed.
A visual language for color-based painting retrieval

The availability of large image databases is emphasizing the relevance of filters, which permit one to focus on a small subset of data. Visual specification of such filters provides a natural way to express content-oriented queries. To support a high power of expression, an original visual language is proposed for the symbolic representation of the semantics induced by the color quality and arrangement over a painting. The proposed language is based on the theory of color semantics introduced by artists in the 20th Century, and is developed to support a visual querying paradigm. This paper formalizes the grammar of the language in its visual form and describes it implementation in a prototype system of painting retrieval by color content.
3-D Head Tracking via Invariant Keypoint Learning

Keypoint matching is a standard tool to solve the correspondence problem in vision applications. However, in 3-D face tracking, this approach is often deficient because the human face complexities, together with its rich viewpoint, nonrigid expression, and lighting variations in typical applications, can cause many variations impossible to handle by existing keypoint detectors and descriptors. In this paper, we propose a new approach to tailor keypoint matching to track the 3-D pose of the user head in a video stream. The core idea is to learn keypoints that are explicitly invariant to these challenging transformations. First, we select keypoints that are stable under randomly drawn small viewpoints, nonrigid deformations, and illumination changes. Then, we treat keypoint descriptor learning at different large angles as an incremental scheme to learn discriminative descriptors. At matching time, to reduce the ratio of outlier correspondences, we use second-order color information to prune keypoints unlikely to lie on the face. Moreover, we integrate optical flow correspondences in an adaptive way to remove motion jitter efficiently. Extensive experiments show that the proposed approach can lead to fast, robust, and accurate 3-D head tracking results even under very challenging scenarios.
Joint estimation of head pose and visual focus of attention

Head pose is an important indicator of a person's visual focus of attention (VFoA). A traditional way to recognize VFoA is to consider accurate head pose or gaze estimations. However, these estimations usually degrade drastically in middle or low resolution video data. In this paper, a joint estimation of head pose and VFoA is proposed to address this issue; both head pose and VFoA are iteratively refined until convergence. This approach is evaluated in a specific scenario involving children around a table playing together with toys. Datasets are acquired and annotated by psychologists in Peking university. The experimental results demonstrate the usefulness of the join estimation process to recognize visual focus of attention in middle resolution video sequences.
Performance of an ICA video watermarking scheme using informed techniques

Independent component analysis (ICA) techniques have been recently used in different watermarking schemes. However, performance of an ICA video watermarking scheme in comparison with those using classical domains, such as the discrete Fourier transform (DCT) domain, is still not clear. In this paper, we attempt to fill this gap. Therefore, we propose a video watermarking scheme, using an informed trellis, applied in two transformed domains obtained by using respectively the DCT transform and an ICA coding technique. We show that, for both domains, the scheme offers a good robustness against MPEG-2 compression, as well as an important capacity level. We consider in this paper data hiding in digital TV channels where data are compressed using MPEG-2.
Pairwise LIDAR calibration using multi-type 3D geometric features in natural scene

It has become a well-known technology that 3D measurement of a large environment could be achieved by using a number of 2D LIDARs on a mobile platform. In such a system, calibration is essential for making collaborative use of different LIDAR data, while existing methods usually require modifications to the environments, such as putting calibration targets, or rely on special facilities, which is labor intensive and put many restrictions to potential applications. This research aims at developing a calibration method for multiple 2D LIDAR sensing systems, which could be conducted in a general outdoor environment using the features of a nature scene. Special focus is cast on solving the noisy sensing in a complex environment and the occlusions caused by largely different sensor viewpoints. A multi-type geometric feature based calibration algorithm is proposed, which extracts the features such as points, lines, planes and quadrics from the 3D points of each LIDAR sensing. Transformation parameters from each sensor to the frame on a moving platform is estimated by matching the multi-type features. Experiments are conducted using the data sets of an intelligent vehicle platform (POSS-V) through a driving in the campus of Peking University. Results of calibrating two LIDAR sensors with largely different viewpoints are presented, and the accuracy and robustness concerning noisy feature extractions are examined intensively.
Head pose based intention prediction using Discrete Dynamic Bayesian Network

Intention prediction is an active research topic, however, few work make use of the head pose. This research investigates the usefulness of head pose and pose change features into a Discrete Dynamic Bayesian Network to predict pedestrian's intention. In particular, we first focus on scenarios within a shopping center (using the standard CAVIAR database), to predict whether a pedestrian will enter a shop or not. We assume that different head pose patterns reflect different intentions. Thus, a hierarchical clustering method is used to process the histogram of head pose and the change of head pose. Then the clustering results are used as features feeding into a dynamic bayesian network. Experiments have been conducted to demonstrate the importance of using head pose in intention prediction and it shows a fairly good result which use only the information derived from pedestrian themselves regardless of the complex environment. The proposed approach is simple to use and makes it easy to integrate other features.
Appearance factorization based facial expression recognition and synthesis

Facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. We address the issues of facial expression recognition and synthesis and compare the proposed bilinear factorization based representations with previously investigated methods such as linear discriminant analysis and linear regression. We conclude that bilinear factorization outperforms these techniques in terms of correct recognition rates and synthesis photorealism especially when the number of training samples is restrained.
Dual-space linear discriminant analysis for face recognition

Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they are often unstable and have to discard some discriminative information. In this paper, a dual-space LDA approach for face recognition is proposed to take full advantage of the discriminative information in the face space. Based on a probabilistic visual model, the eigenvalue spectrum in the null space of within-class scatter matrix is estimated, and discriminant analysis is simultaneously applied in the principal and null subspaces of the within-class scatter matrix. The two sets of discriminative features are then combined for recognition. It outperforms existing LDA approaches.
Simultaneous tracking and facial expression recognition using multiperson and multiclass autoregressive models

The dynamical recognition of facial gestures and expressions in image sequences is an important and challenging problem. Most of the existing methods adopt the following paradigm. First, facial feature movements are retrieved from the images, then the facial expression is recognized based on these retrieved movements. In contrast to this mainstream approach, this paper introduces a new approach allowing the simultaneous retrieval of facial feature movements and expression using a particle filter adopting multi-class and multi-person dynamics. The proposed fast scheme is either as robust as, or more robust than existing ones in a number of respects. We provide evaluations of performance to show the feasibility and robustness of the proposed approach.
A linear estimation method for 3D pose and facial animation tracking

This paper presents an approach that incorporates Canonical Correlation Analysis (CCA) for monocular 3D face pose and facial animation estimation. The CCA is used to find the dependency between texture residuals and 3D face pose and facial gesture. The texture residuals are obtained from observed raw brightness shape-free 2D image patches that we build by means of a parameterized 3D geometric face model. This method is used to correctly estimate the pose of the face and the model's animation parameters controlling the lip, eyebrow and eye movements (encoded in 15 parameters). Extensive experiments on tracking faces in long real video sequences show the effectiveness of the proposed method and the value of using CCA in the tracking context.
Adaptive Delaunay triangulation for attractor image coding

The principle of attractor image coding presented in this paper is based on the theory of IFS (iterated function systems). The algorithm exploits piece-wise similarities between block of different sizes. To improve the algorithms based on regular and square blocks, we propose an adaptive Delaunay triangulation of the image support. The originality of the method is to map the triangles on specific parts of the image in order to have lots of similarities between them.