nils-herrmann/clean_scirepeval huggingface dataset:
VideoWeb: Design of a wireless camera network for real-time monitoring of activities

Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and non-imaging sensors such as acoustics, seismic, vibration, temperature, humidity, etc. The development of emerging video sensor networks poses its own set of unique challenges, including high bandwidth and low latency requirements for real-time processing and control. This paper presents a systematic approach for the design, implementation, and evaluation of a large-scale, software-reconfigurable, wireless camera network, suitable for a variety of practical real-time applications. We take into consideration issues related to the hardware, software, control, architecture, network connectivity, performance evaluation, and data processing strate-gies for the network. We perform multi-objective optimization on settings such as video resolution and compression quality to provide insight into the performance trade-offs when configuring such a network.
Active concept learning for image retrieval in dynamic databases

Concept learning in content-based image retrieval (CBIR) systems is a challenging task. We present an active concept learning approach based on mixture model to deal with the two basic aspects of a database system: changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation vs. exploration in the search space helps to find optimal model efficiently. Experimental results on Corel database show the efficacy of our approach.
Learning semantic visual concepts from video

Increasing amounts of digital video data have become available with the rapid growth in video technology. As a result, there is a great need for automatic extraction of concepts or events of interest from video. In this paper, we present an approach for learning concepts from video. The approach consists of three steps. In the first step, video shot boundaries are detected, and from these shots keyframes are extracted, which are representatives of the shots. In the second step, key frames are segmented and a variety of features are computed In the third step, a classification by feature partitioning method is employed for learning different semantic concepts. The results are presented for successfully learning semantic concepts such as ocean, mountain, people, and building from a variety of digital videos.
A biologically inspired approach for fusing facial expression and appearance for emotion recognition

Facial emotion recognition from video is an exemplar case where both humans and computers underperform. In recent emotion recognition competitions, top approaches were using either geometric relationships that best captured facial dynamics or an accurate registration technique to develop appearance features. These two methods capture two different types of facial information similarly to how the human visual system divides information when perceiving faces. In this paper, we propose a biologically-inspired fusion approach that emulates this process. The efficacy of the approach is tested with the Audio/Visual Emotion Challenge 2011 data set, a non-trivial data set where state-of-the-art approaches perform under chance. The proposed approach increases classification rates by 18.5% on publicly available data.
Learned templates for feature extraction in fingerprint images

Most current techniques for minutiae extraction in fingerprint images utilize complex preprocessing and postprocessing. In this paper, we propose a new technique, based on the use of learned templates, which statistically characterize the minutiae. Templates are teamed from examples by optimizing a criterion function using Lagrange's method. To detect the presence of minutiae in test images, templates are applied with appropriate orientations to the binary image only at selected potential minutia locations. Several performance measures, which evaluate the quality and quantity of extracted features and their impact on identification, are used to evaluate the significance of learned templates. The performance of the proposed approach is evaluated on two sets of fingerprint images: one is collected by an optical scanner and the other one is chosen from NIST special fingerprint database 4. The experimental results show that learned templates can improve both the features and the performance of the identification system.
Iris Liveness Detection by Relative Distance Comparisons

The focus of this paper is on presentation attack detection for the iris biometrics, which measures the pattern within the colored concentric circle of the subjects' eyes, to authenticate an individual to a generic user verification system. Unlike previous deep learning methods that use single convolutional neural network architectures, this paper develops a framework built upon triplet convolutional networks that takes as input two real iris patches and a fake patch or two fake patches and a genuine patch. The aim is to increase the number of training samples and to generate a representation that separates the real from the fake iris patches. The smaller architecture provides a way to do early stopping based on the liveness of single patches rather than the whole image. The matching is performed by computing the distance with respect to a reference set of real and fake examples. The proposed approach allows for real-time processing using a smaller network and provides equal or better than state-of-the-art performance on three benchmark datasets of photo-based and contact lens presentation attacks.
Learning feature relevance and similarity metrics in image databases

Most of the current image retrieval systems use "one-shot" queries to a database to retrieve similar images. Typically a K-NN (nearest neighbor) kind of algorithm is used where the weights of the features that are used to represent images remain fixed (or manually tweaked by the user) in the computation of a given similarity metric. However, neither all of the features are equally important for a given query nor a similarity metric is optimal for all kinds of images in a database. The manual adjustment of these weights and the selection of similarity metric are exhausting. Moreover, they require a very sophisticated user. The authors present a novel image retrieval system that continuously learns the weights of features and selects an appropriate similarity metric based on the user's feedback given as positive or negative image examples. Experimental results are presented that provide the objective evaluation of learning behavior of the system for image retrieval.
Active concept learning in image databases

Concept learning in content-based image retrieval systems is a challenging task. This paper presents an active concept learning approach based on the mixture model to deal with the two basic aspects of a database system: the changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we a) propose a new user directed semi-supervised expectation-maximization algorithm for mixture parameter estimation, and b) develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation versus exploration in the search space helps to find the optimal model efficiently. Our concept knowledge transduction approach is able to deal with the cases of image insertion and query images being outside the database. The system handles the situation where users may mislabel images during relevance feedback. Experimental results on Corel database show the efficacy of our active concept learning approach and the improvement in retrieval performance by concept transduction.
Evaluating the quality of super-resolved images for face recognition

The widespread use of super-resolution methods, in a variety of applications such as surveillance has led to an increasing need for or quality assessment measures. The current quality measures aim to compare different fusion methods by assessing the quality of the fused images. They consider the information transferred between the super-resolved image and input images only. In this paper, we propose an objective quality evaluation algorithm for super-resolved images, which focuses on evaluating the quality of super-resolved images that are constructed from different conditions of input images. The proposed quality evaluation method combines both the relationship between the super-resolved image and the input images, and the relationship between the input images. Using the proposed measure, the quality of the super-resolved face images constructed from videos are evaluated under different conditions, including the variation of pose, lighting, facial expressions and the number of input images.
Learning models for predicting recognition performance

This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of small gallery size that can give good error estimates and their confidences on large probe sets and populations. We use a generalized two-dimensional prediction model that integrates a hypergeometric probability distribution model with a binomial model explicitly and considers the distortion problem in large populations. We incorporate learning in the prediction process in order to find the optimal small gallery size and to improve its performance. The Chernoff and Chebychev inequalities are used as a guide to obtain the small gallery size. During the prediction we use the expectation-maximum (EM) algorithm to learn the match score and the non-match score distributions (the number of components, their weights, means and covariances) that are represented as Gaussian mixtures. By learning we find the optimal size of small gallery and at the same time provide the upper bound and the lower bound for the prediction on large populations. Results are shown using real-world databases
Soft Biometrics Integrated Multi-target Tracking

In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Track lets, the short-term tracking results, are generated by linking detections in consecutive frames based on conservative constraints. Our goal is to "re-stitching" the adjacent track lets that contain the same target so that robust long-term tracking results can be achieved. As the appearance of the same target may change greatly due to heavy occlusion, pose variations and changing lighting conditions, a discriminative appearance model is crucial for association-based tracking. Unlike most previous methods which simply use the similarity of color histograms or other low level features to construct the appearance model, we propose to use the fusion of soft biometrics generated from sub-track lets to learn a discriminative appearance model in an online manner. Compared to low level features, soft biometrics are robust against appearance variation. The experimental results demonstrate that our method is robust and greatly improves the tracking performance over the state-of-the-art method.
Stochastic models for recognition of articulated objects

We present a hidden Markov modeling (HMM) based approach for recognition of articulated objects in synthetic aperture radar (SAR) images. We develop multiple models for a given SAR image of an object and integrate these models synergistically using their probabilistic estimates for recognition and estimates of invariance of features as a result of articulation. The models are based on sequentialization of scattering centers extracted from SAR images. Experimental results are presented using 1440 training images and 2520 testing images for 4 classes.
Performance Prediction for Multimodal Biometrics

Sensor fusion is commonly used to improve the detection and recognition performance of a pattern recognition system. In this paper we propose a prediction model to predict the performance of a sensor fusion system. In particular, we answer two questions associated with the performance prediction in a sensor fusion system: (a) given the characteristics of the individual sensors how can we predict the performance of the fusion system? (b) How good the prediction is? We provide the Cramer-Rao bounds for the prediction model. We carry out experiments on the publicly available database XM2VTS that has speech and face data
EDeN: Ensemble of Deep Networks for Vehicle Classification

Traffic surveillance has always been a challenging task to automate. The main difficulties arise from the high variation of the vehicles appertaining to the same category, low resolution, changes in illumination and occlusions. Due to the lack of large labeled datasets, deep learning techniques still have not shown their full potential. In this paper, we train an Ensemble of Deep Networks (EDeN) to successfully classify surveillance images into eleven different classes of vehicles. The MIO-TCD dataset consists of 786,702 images with high diversity and resembles a real-world environment. Extensive evaluation was performed using individual networks and different combinations of ensembles. Experimental results show that ensemble of networks gives better performance compared to individual networks and it is robust to noise. The ensemble of networks achieves an accuracy of 97.80%, mean precision of 94.39%, mean recall of 91.90% and Cohen kappa of 96.58%.
Feature Fusion of Face and Gait for Human Recognition at a Distance in Video

A new video based recognition method is presented to recognize non-cooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated at feature level. For face, a high-resolution side face image is constructed from multiple video frames. For gait, gait energy image (GEI), a spatio-temporal compact representation of gait in video, is used to characterize human walking properties. Face features and gait features are obtained separately using principal component analysis (PCA) and multiple discriminant analysis (MDA) combined method from the high-resolution side face image and gait energy image (GEI), respectively. The system is tested on a database of video sequences corresponding to 46 people. The results showed that the integrated face and gait features carry the most discriminating power compared to any individual biometric
Symmetry-integrated injury detection for brain MRI

This paper presents a new brain injury detection approach in images acquired by magnetic resonance imaging (MRI). The proposed approach is based on the fact that the anatomical structure of a 2D brain is highly symmetric, while most of the injury in the brain generally indicates asymmetry. The approach starts from symmetry integrated region growing segmentation of the brain images using the symmetry affinity matrix, and candidate asymmetric regions are initially extracted using kurtosis and skewness of symmetry affinity matrix. An Expectation Maximum classifier with Gaussian mixture model is used explicitly to classify asymmetric regions into injury and non-injury. Experimental results are carried out to demonstrate the efficacy of the approach for injury detection.
Contextual and visual modeling for detection of mild traumatic brain injury in MRI

Mild traumatic brain injury (mTBI) is difficult to detect as the current tools are qualitative, which can lead to poor diagnosis and treatment. The low contrast appearance of mTBI abnormalities on magnetic resonance (MR) images makes quantification problematic for image processing and analysis techniques. To overcome these difficulties, an algorithm is proposed that takes advantage of subject information and texture information from MR images. A contextual model is developed to simulate the progression of the disease using multiple inputs, such as the time post-injury and the location of injury. Textural features are used along with feature selection for a single MR modality. Results from a probabilistic support vector machine using textural features are fused with the contextual model to obtain a robust estimation of abnormal tissue. A novel rat temporal dataset demonstrates the ability of our approach to outperform other state of the art approaches.
Age Classification Base on Gait Using HMM

In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM). A gait database including young people and elderly people is built. To extract appropriate gait features, we consider a contour related method in terms of shape variations during human walking. Then the image feature is transformed to a lower-dimensional space by using the Frame to Exemplar (FED) distance. A HMM is trained on the FED vector sequences. Thus, the framework provides flexibility in the selection of gait feature representation. In addition, the framework is robust for classification due to the statistical nature of HMM. The experimental results show that video-based automatic age classification from human gait is feasible and reliable.
Improving large-scale face image retrieval using multi-level features

In recent years, extensive efforts have been made for face recognition and retrieval systems. However, there remain several challenging tasks for face image retrieval in unconstrained databases where the face images were captured with varying poses, lighting conditions, etc. In addition, the databases are often large-scale, which demand efficient retrieval algorithms that have the merit of scalability. To improve the retrieval accuracy of the face images with different poses and imaging characteristics, we introduce a novel feature extraction method to bag-of-words (BoW) based face image retrieval system. It employs various scales of features simultaneously to encode different texture information and emphasizes image patches that are more discriminative as parts of the face. Moreover, the overlapping image patches at different scales compensate for the pose variation and face misalignment. Experiments conducted on a large-scale public face database demonstrate the superior performance of the proposed approach compared to the state-of-the-art method.
A system for obstacle detection during rotorcraft low altitude flight

An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.
Learning based interactive image segmentation

In this paper we present an approach to image segmentation in which user selected sets of examples and counter-examples supply information about the specific segmentation problem. Image segmentation is guided by a genetic algorithm which learns the appropriate subset and spatial combination of a collection of discriminating functions, associated with image features. The genetic algorithm encodes discriminating functions into a functional template representation, which can be applied to the input image to produce a candidate segmentation. The quality of each segmentation is evaluated within the genetic algorithm, by a comparison of two physics-based techniques for region growing and edge detection. Experimental results on real SAR imagery demonstrate that evolved segmentations are consistently better than segmentations derived from the Bayesian best single feature.
Modeling clutter and context for target detection in infrared images

In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not only for man-made targets bat also of natural background clutters. Because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. If available, contextual information that characterizes each training example can be used to further improve the learned clutter model. In this paper, we present such a clutter model aided target detection system. Emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.
Delayed reinforcement learning for adaptive image segmentation and feature extraction

Object recognition is a multilevel process requiring a sequence of algorithms at low, intermediate, and high levels. Generally, such systems are open loop with no feedback between levels and assuring their robustness is a key challenge in computer vision and pattern recognition research. A robust closed-loop system based on "delayed" reinforcement learning is introduced. The parameters of a multilevel system employed for model-based object recognition are learned. The method improves recognition results over time by using the output at the highest level as feedback for the learning system. It has been experimentally validated by learning the parameters of image segmentation and feature extraction and thereby recognizing 2D objects. The approach systematically controls feedback in a multilevel vision system and shows promise in approaching a long-standing problem in the field of computer vision and pattern recognition.
Reflection Symmetry-Integrated Image Segmentation

This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.