nils-herrmann/clean_scirepeval huggingface dataset:
Symmetry integrated region-based image segmentation

Symmetry is an important cue for machine perception that involves high-level knowledge of image components. Unlike most of the previous research that only computes symmetry in an image, this paper integrates symmetry with image segmentation to improve the segmentation performance. The symmetry integration is used to optimize both the segmentation and the symmetry of regions simultaneously. Interesting points are initially extracted from an image and they are further refined for detecting symmetry axis. A symmetry affinity matrix is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of segmented regions. Experimental results and comparisons from a wide domain of images indicate a promising improvement by symmetry integrated image segmentation compared to other image segmentation methods that do not exploit symmetry.
Utility-Based Camera Assignment in a Video Network: A Game Theoretic Framework

In this paper, an approach for camera assignment and handoff in a video network based on a set of user-supplied criteria is proposed. The approach is based on game theory, where bargaining mechanisms are considered for collaborations as well as for resolving conflicts among the available cameras. Camera utilities and person utilities are computed based on a set of user-supplied criteria, which are used in the process of developing the bargaining mechanisms. Different criteria and their combination are compared with each other to understand their effect on camera assignment. Experiments for multicamera multiperson cases are provided to corroborate the proposed approach. Intuitive evaluation measures are used to evaluate the performance of the system in real-world scenarios. The proposed approach is also compared with two recent approaches based on different principles. The experimental results show that the proposed approach is computationally more efficient, more robust and more flexible in dealing with the user-supplied criteria.
Adaptive object detection based on modified Hebbian learning

This paper focuses on the issue of developing self-adapting automatic object detection systems for improving their performance. Two general methodologies for performance improvement are first introduced. They are based on parameter optimizing and input adapting. Different modified Hebbian learning rules are developed to build adaptive, feature extractors which transform the input data into a desired form for a given algorithm. To show its feasibility, an input adaptor for object detection is designed as an example and tested using multisensor data (optical, SAR, and FLIR). Test results are presented and discussed in the paper.
Integrated binocular and motion stereo in a inertial navigation sensor-based mobile vehicle

Range measurements to real-world objects from mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. The authors present an approach that consists of a synergistic combination of two types of passive ranging methods: binocular stereo and motion stereo. They describe a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system and derive the appropriate Kalman filter for refining the estimates from these two stereo ranging techniques. Results for laboratory images are presented, showing that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular or motion stereo. By incorporating a blending filter, the approach has the potential of providing accurate, dense range measurements for all the pixels in the field of view.<<ETX>>
Super-resolution of deformed facial images in video

Super-resolution (SR) of facial images from video suffers from facial expression changes. Most of the existing SR algorithms for facial images make an unrealistic assumption that the "perfect" registration has been done prior to the SR process. However, the registration is a challenging task for SR with expression changes. This paper proposes a new method for enhancing the resolution of low-resolution (LR) facial image by handling the facial image in a non-rigid manner. It consists of global tracking, local alignment for precise registration and SR algorithms. A B-spline based Resolution Aware Incremental Free Form Deformation (RAIFFD) model is used to recover a dense local non-rigid flow field. In this scheme, low-resolution image model is explicitly embedded in the optimization function formulation to simulate the formation of low resolution image. The results achieved by the proposed approach are significantly better as compared to the SR approaches applied on the whole face image without considering local deformations.
Evolutionary feature synthesis for object recognition

Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key to the performance of object recognition. In this paper, we propose a coevolutionary genetic programming (CGP) approach to learn composite features for object recognition. The knowledge about the problem domain is incorporated in primitive features that are used in the synthesis of composite features by CGP using domain-independent primitive operators. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. CGP, on the other hand, can try a very large number of unconventional combinations and these unconventional combinations yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can discover good composite features to distinguish objects from clutter and to distinguish among objects belonging to several classes. The comparison with other classical classification algorithms is favorable to the CGP-based approach proposed in this paper.
On the accuracy and robustness of deep triplet embedding for fingerprint liveness detection

Liveness detection is an anti-spoofing technique for dealing with presentation attacks on biometrics authentication systems. Since biometrics are usually visible to everyone, they can be easily captured by a malignant user and replicated to steal someone's identity. In particular, fingerprints can be easily reproduced by using gummy materials and attached to the impostor's fingertips, making the attack go unnoticed by security personnel and camera networks. In this paper, the classical binary classification formulation (live/fake) is substituted by a deep metric learning framework that can generate a representation of real and artificial fingerprints and explicitly models the underlying factors that explain their inter-and intra-class variations. The framework is based on a deep triplet network architecture and consists of a variation of the original triplet loss function. Experiments show that the approach can perform liveness detection in real-time outperforming the state-of-the-art on several benchmark datasets.
Concept learning and transplantation for dynamic image databases

The task of a content-based image retrieval (CBIR) system is to cater to users who expect to get relevant images with high precision and efficiency in response to query images. This paper presents a concept learning approach that integrates a mixture model of the data, relevance feedback and long-term continuous learning. The concepts are incrementally refined with increased retrieval experiences. The concept knowledge can be immediately transplanted to deal with the dynamic database situations such as insertion of new images, removal of existing images and query images, which are outside the database. Experimental results on Corel database show the efficacy of our approach.
Automated identification and retrieval of moth images with semantically related visual attributes on the wings

A new automated identification and retrieval system is proposed that aims to provide entomologists, who manage insect specimen images, with fast computer-based processing and analyzing techniques. Several relevant image attributes were designed, such as the so-called semantically-related visual (SRV) attributes detected from the insect wings and the co-occurrence patterns of the SRV attributes which are uncovered from manually labeled training samples. A joint probabilistic model is used as SRV attribute detector working on image visual contents. The identification and retrieval of moth species are conducted by comparing the similarity of SRV attributes and their co-occurrence patterns. The prototype system used moth images while it can be generalized to any insect species with wing structures. The system performed with good stability and the accuracy reached 85% for species identification and 71% for content-based image retrieval on a entomology database.
Composite phase and phase-based Gabor element aggregation

This paper describes how the phase, obtained by Gabor filtering an image, can be used to aggregate related Gabor elements (simple features identified by peaks in the Gabor magnitude). This phase-based feature grouping simplifies the perennial problem of target/background segmentation because we need to only determine if the aggregate feature is target or background, rather than determine the status of each feature independently. Since the phase from a single quadrature Gabor output cannot tolerate large changes in orientation, a new local measure, referred to as the composite phase, has been developed. It is a combination of the filter responses from multiple orientations which allows the phase to follow contours with large changes in orientation. A constant composite phase contour is used to connect related Gabor elements that would otherwise appear separated within the magnitude response.
Jitter camera: high resolution video from a low resolution detector

Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. Moving the camera and applying super-resolution reconstruction algorithms can computationally enhance the resolution of videos. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super resolution. The conclusion is, that in order to achieve the highest resolution, motion blur should be avoided. Sampling the space-time volume of the video in a specific manner can minimize motion blur. We have developed a novel camera, called the "jitter camera" that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.
Predicting an upper bound on SAR ATR performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. In such an approach, each model target is represented by a set of SAR views, and both model and data views are represented by locations of scattering centers. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. Firstly, we calculate a measure of the similarity between a given model view and each view in the model set, as a function of the relative transformation between them. Secondly we select a subset of possible erroneous hypotheses that correspond to peaks in similarity functions obtained in the first step. Thirdly, we determine an upper bound on the probability of correct recognition by computing the probability that every selected hypothesis gets less votes than those for the model view under consideration. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations.
Predicting Performance of Object Recognition

We present a method for predicting fundamental performance of object recognition. We assume that both scene data and model objects are represented by 2D point features and a data/model match is evaluated using a vote-based criterion. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, in addition to model similarity. This is unlike previous approaches, which consider only a subset of these factors. Performance is predicted in two stages. In the first stage, the similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. In the second stage, the similarity information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using real synthetic aperture radar (SAR) data.
Improving action units recognition using dense flow-based face registration in video

Aligning faces with non-rigid muscle motion in the real-world streaming video is a challenging problem. We propose a novel automatic video-based face registration architecture for facial expression recognition. The registration process is formulated as a dense SIFT-flow- and optical-flow-based affine warping problem. We start off by estimating the transformation of an arbitrary face to a generic reference face with canonical pose. This initialization in our framework establishes a head pose and person independent face model. The affine transformation computed from the initialization is then propagated by affine transformation estimated from the dense optical flow to guarantee the temporal smoothness of the nonrigid facial appearance. We call this method SIFT and optical flow affine image transform (SOFAIT). This real-time algorithm is designed for realistic streaming data, allowing us to analyze the facial muscle dynamics in a meaningful manner. Visual and quantitative results demonstrate that the proposed automatic video-based face registration technique captures the appearance changes in spontaneous expressions and outperforms the state-of-the-art technique.
Concept Learning with Co-occurrence Network for Image Retrieval

This paper addresses the problem of concept learning for semantic image retrieval. Two types of semantic concepts are introduced in our system: the individual concept and the scene concept. The individual concepts are explicitly provided in a vocabulary of semantic words, which are the labels or annotations in an image database. Scene concepts are higher level concepts which are defined as potential patterns of co occurrence of individual concepts. Scene concepts exist since some of the individual concepts co-occur frequently across different images. This is similar to human learning where understanding of simpler ideas is generally useful prior to developing more sophisticated ones. Scene concepts can have more discriminative power compared to individual concepts but methods are needed to find them. A novel method for deriving scene concepts is presented. It is based on a weighted concept co-occurrence network (graph) with detected community structure property. An image similarity comparison and retrieval framework is described with the proposed individual and scene concept signature as the image semantic descriptors. Extensive experiments are conducted on a publicly available dataset to demonstrate the effectiveness of our concept learning and semantic image retrieval framework.
Foreword - Biometrics in the Wild 2015

The first International Workshop on Biometrics in the Wild (B-Wild 2015) was held on May 8th, 2015 in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition (IEEE FG-2015) in Ljubljana, Slovenia. The goal of the workshop was to present the most advanced work related to biometric recognition in the wild and to bring recent advances from this field to the attention of the broader FG community.
Boosting Face Recognition in Real-World Surveillance Videos

Face recognition becomes a challenging problem in real-world surveillance videos where the low-resolution probe frames exhibit variations in pose, lighting condition, and facial expressions. This is in contrast with the gallery images which are generally frontal view faces acquired under controlled environments. A direct matching of probe images with gallery data often leads to poor recognition accuracy due to the significant discrepancy between the two kinds of data. In addition, the artifacts such as low resolution, blurriness and noise further enlarge this discrepancy. In this paper, we propose a video based face recognition framework using a novel image representation called warped average face (WAF). The WAFs are generated in two stages: in-sequence warping and frontal view warping. The WAFs can be easily used with various feature descriptors or classifiers. As compared to the original probe data, the image quality of the WAFs is significantly better and the appearance difference between the WAFs and the gallery data is suppressed. Given a probe sequence, only a few WAFs need to be generated for the recognition purpose. We test the proposed method on the ChokePoint dataset and our in-house dataset of surveillance quality. Experiments show that with the new image representation, the recognition accuracy can be boosted significantly.
CAD-Based 3D Object Representation for Robot Vision

This article explains that most existing vision systems rely on models generated in an ad hoc manner and have no explicit relation to the CAD/CAM system originally used to design and manufacture these objects. The authors desire a more unified system that allows vision models to be automatically generated from an existing CAD database. A CAD system contains an interactive design interface, graphic display utilities, model analysis tools, automatic manufacturing interfaces, etc. Although it is a suitable environment for design purposes, its representations and the models it generates do not contain all the features that are important in robot vision applications. In this article, the authors propose a CAD-based approach for building representations and models that can be used in diverse applications involving 3D object recognition and manipulation. There are two main steps in using this approach. First, they design the object's geometry using a CAD system, or extract its CAD model from the existing database if it has already been modeled. Second, they develop representations from the CAD model and construct features possibly by combining multiple representations that are crucial in 3D object recognition and manipulation.
Local reinforcement learning for object recognition

Current computer vision systems, whose basic methodology is open-loop or filter type, typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using local reinforcement learning to induce a highly adaptive mapping from input images to segmentation strategies. This is accomplished by using the confidence level of model matching as reinforcement to drive learning. The system is verified through experiments on a large set of real images.
Multistrategy fusion using mixture model for moving object detection

In a video surveillance domain, mixture models are used in conjunction with a variety of features and filters to detect and track moving objects. However, these systems do not provide clear performance results at the pixel detection level. In this paper, we apply the mixture model to provide several fusion strategies based on the competitive and cooperative principles of integration which we call OR and AND strategies. In addition, we apply the Dempster-Shafer method to mixture models for object detection. Using two video databases, we show the performance of each fusion strategy using receiver operating characteristic curves.
Landmark recognition for autonomous mobile robots

A novel approach for landmark recognition based on the perception, reasoning, action, and expectation (PREACTE) paradigm is presented for the navigation of autonomous mobile robots. PREACTE uses expectations to predict the appearance and disappearance of objects, thereby reducing computational complexity and locational uncertainty. It uses an innovative concept called dynamic model matching (DMM), which is based on the automatic generation of landmark description at different ranges and aspect angles and uses explicit knowledge about maps and landmarks. Map information is used to generate an expected site model (ESM) for search delimitation, given the location and velocity of the mobile robot. The landmark recognition vision system generates 2-D and 3-D scene models from the observed scene. The ESM hypotheses are verified by matching them to the image model. Experimental results that verify the performance of the PREACTE and DMM algorithms for real imagery are also presented.<<ETX>>
Task-oriented camera assignment in a video network

Camera assignment and hand-off are some of the key image processing problems in a video network. In this paper, we propose a new approach for camera assignment and hand-off in a video network. The camera assignment problem is modeled as a weakly acyclic game which allows the design of utility functions based on different user-supplied criteria. A theoretical and experimental comparison of the proposed approach with the two recently proposed approaches based on potential game theory and constraint satisfaction problem is provided. This comparison shows that the proposed approach is theoretically more general and computationally more efficient than the other approaches.
On the Performance Prediction and Validation for Multisensor Fusion

Multiple sensors are commonly fused to improve the detection and recognition performance of computer vision and pattern recognition systems. The traditional approach to determine the optimal sensor combination is to try all possible sensor combinations by performing exhaustive experiments. In this paper, we present a theoretical approach that predicts the performance of sensor fusion that allows us to select the optimal combination. We start with the characteristics of each sensor by computing the match score and non-match score distributions of objects to be recognized. These distributions are modeled as a mixture of Gaussians. Then, we use an explicit Phi transformation that maps a receiver operating characteristic (ROC) curve to a straight line in 2-D space whose axes are related to the false alarm rate (FAR) and the Hit rate (Hit). Finally, using this representation, we derive a set of metrics to evaluate the sensor fusion performance and find the optimal sensor combination. We verify our prediction approach on the publicly available XM2VTS database as well as other databases.