nils-herrmann/clean_scirepeval huggingface dataset:
Synergism of binocular and motion stereo for passive ranging

Range measurements to objects in the world relative to mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. An approach is presented that consists of a synergistic combination of two types of passive ranging method: binocular stereo and motion stereo. We show a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system (INS) and derive the appropriate Kalman filter to refine the estimates from these two stereo ranging techniques. We present results using laboratory images that show that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular and 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 (FOV).
Efficient alignment for vehicle make and model recognition

This paper presents a make and model recognition system for passenger vehicles. We propose a two-step efficient alignment mechanism to account for view point changes. The 2D alignment problem is solved as two separate one dimensional shortest path problems. To avoid the alignment of the query with the entire database, reference views are used. These views are generated iteratively from the database. To improve the alignment performance further, use of two references is proposed: a universal view and type specific showcase views. The query is aligned with universal view first and compared with the database to find the type of the query. Then the query is aligned with type specific showcase view and compared with the database to achieve the final make and model recognition. We report results on database of 1500 vehicles with more than 250 makes and models.
Selective experience replay in reinforcement learning for reidentification

Person reidentification is a problem of recognizing a person across non-overlapping camera views. Pose variations, illumination conditions, low resolution images, and occlusion are the main challenges encountered in reidentification. Due to the uncontrolled environment in which the videos are captured, people could appear in different poses and due to which the appearance of a person could vary significantly. The walking direction of a person can provide a good estimation of their pose. Therefore, in this paper, we propose a reidentification system which adaptively selects an appropriate distance metric based on context of walking direction using reinforcement learning. Though experiments, we show that such a dynamic strategy outperforms static strategy learned or designed offline.
Adaptive image segmentation using a genetic algorithm

Image segmentation is an old and difficult problem. One of the fundamental weaknesses of current computer vision systems to be used in practical applications is their inability to adapt the segmentation process as real-world changes occur in the image. We present the first closed loop image segmentation system which incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem is formulated as an optimization problem and the genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. The goals of our adaptive image segmentation system are to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. We present experimental results which demonstrate learning and the ability to adapt the segmentation performance in outdoor color imagery.
Multiperson Tracking by Online Learned Grouping Model With Nonlinear Motion Context

An online approach to learn elementary groups containing only two targets, i.e., pedestrians, for inferring high-level context is introduced to improve multiperson tracking. In most existing data association-based tracking approaches, only low-level information (e.g., time, appearance, and motion) is used to build the affinity model, and each target is considered as an independent agent. Unlike those previous methods, in this paper, an online learned social grouping behavior model is used to provide more robust tracklet affinities. A disjoint grouping graph is used to encode social grouping behavior of pairwise targets, where each node represents an elementary group of two targets, and two nodes are connected if they share a common target. Probabilities of the uncertain target in two connected nodes being the same person are inferred from each edge of the grouping graph. Relationships between elementary groups are discovered by group tracking, and a nonlinear motion map is used for explaining nonlinear motion pattern between elementary groups. The proposed method is efficient, able to handle group split and merge, and can be easily integrated into any basic affinity model. The approach is evaluated on four data sets, and it shows significant improvements compared with state-of-the-art methods.
Incremental Vehicle 3-D Modeling from Video

In this paper, we present a new model-based approach for building 3D models of vehicles from color video provided by a traffic surveillance camera. We incrementally build 3D models using a clustering technique. Geometrical relations based on 3D generic vehicle model map 2D features to 3D. The 3D features are then adaptively clustered over the frame sequence to incrementally generate the 3D model of the vehicle. Results are shown for both simulated and real traffic video. They are evaluated by a new structural performance measure underscoring usefulness of incremental learning
Dynamic bi-modal fusion of images for the segmentation of pollen tubes in video

Biologists study pollen tube growth to understand how internal cell dynamics affect observable structural characteristics like cell diameter, length, and growth rate. Fluorescence microscopy is used to study the dynamics of internal proteins and ions, but this often produces images with missing parts of the pollen tube. Brightfield microscopy provides a low-cost way of obtaining structural information about the pollen tube, but the images are crowded with false edges. We propose a dynamic segmentation fusion scheme that uses both Bright-field and Fluorescence images of growing pollen tubes to get a unified segmentation. Knowledge of the image formation process is used to create an initial estimate of the location of the cell boundary. Fusing this estimate with an edge indicator function amplifies desired edges and attenuates undesired edges. The cell boundary is obtained using Level Set evolution on the fused edge indicator function. Experimental testing shows that this fusion produces significantly better results than those obtained without it.
Statistical feature fusion for gait-based human recognition

This paper presents a novel approach for human recognition by combining statistical gait features from real and synthetic templates. Real templates are directly computed from training silhouette sequences, while synthetic templates are generated from training sequences by simulating silhouette distortion. A statistical feature extraction approach is used for learning effective features from real and synthetic templates. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance. We apply the proposed approach to USF HumanID Database. Experimental results demonstrate that the proposed fusion approach not only achieves better performance than individual approaches, but also provides large improvement in performance with respect to the baseline algorithm.
Representation and Shape Matching of 3-D Objects

A three-dimensional scene analysis system for the shape matching of real world 3-D objects is presented. Various issues related to representation and modeling of 3-D objects are addressed. A new method for the approximation of 3-D objects by a set of planar faces is discussed. The major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3-D scene analysis. The method is a sequential region growing algorithm. It is not applied to range images, but rather to a set of 3-D points. The 3-D model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object, applying the necessary transformations and then approximating the surface by polygons. A stochastic labeling technique is used to do the shape matching of 3-D objects. The technique matches the faces of an unknown view against the faces of the model. It explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. It is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. The objective here is to match the individual views of the object taken from any vantage point. Details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting.
Error bound for multi-stage synthesis of narrow bandwidth Gabor filters

This paper develops an error bound for narrow bandwidth Gabor filters synthesized using multiple stages. It is shown that the error introduced by approximating narrow bandwidth Gabor (1946) kernels by a weighted sum of spatially offset, separable kernels is a function of the frequency offset and the reduction in bandwidth of the desired kernel compared to the basis values, as well as the spatial subsampling rate between filter stages. This error bound should prove useful in the design of a general basis filter set for multi-stage filtering because the maximum frequency offset is largely determined by the spacing of the basis filters.
A Psychological Adaptive Model For Video Analysis

Extracting key-frames is the first step for efficient content-based indexing, browsing and retrieval of the video data in commercial movies. Most of the existing research deals with "how to extract representative frames?" However the unaddressed question is "how many key-frames are required to represent a video shot properly?" Generally, the user defines this number a priori or some heuristic methods are used. In this paper, we propose a psychological model, which computes this number adaptively and online, from variation of visual features in a video-shot. We incorporate it with an iterative key-frame selection method to automatically select the key-frames. We compare the results of this method with two other well-known approaches, based on a novel effectiveness measure that scores each approach based on its representational power. Movie-clips of varying complexity are used to underscore the success of the proposed model in real-time
Coevolutionary Computation for Synthesis of Recognition Systems

This paper introduces a novel visual learning method that involves cooperative coevolution and linear genetic programming. Given exclusively training images, the evolutionary learning algorithm induces a set of sophisticated feature extraction agents represented in a procedural way. The proposed method incorporates only general vision-related background knowledge and does not require any task-specific information. The paper describes the learning algorithm, provides a firm rationale for its design, and proves its competitiveness with the human-designed recognition systems in an extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery.
Real-Time Pedestrian Tracking with Bacterial Foraging Optimization

In this paper, we present swarm intelligence algorithms for pedestrian tracking. In particular, we present a modified Bacterial Foraging Optimization (BFO) algorithm and show that it outperforms PSO in a number of important metrics for pedestrian tracking. In our experiments, we show that BFO's search strategy is inherently more efficient than PSO under a range of variables with regard to the number of fitness evaluations which need to be performed when tracking. We also compare the proposed BFO approach with other commonly-used trackers and present experimental results on the CAVIAR dataset as well as on the difficult PETS2010 S2.L3 crowd video.
Temporal dynamics of tip fluorescence predict cell growth behavior in pollen tubes

In the sexual reproductive life cycle of flowering plants, the growth of the pollen tube plays a vital role. The pollen tube grows towards the ovary of the flower where it delivers male reproductive material. This growth often involves twists and turns as the pollen tube navigates towards the ovary. Current growth models are a collection of mathematical equations to explain observable linear growth behavior in pollen tubes. However, there are few studies on the relationship between the fluorescence signal at the tip of the cell and the growth behavior (straight vs. turning). In this paper, we propose a method of extracting features from the tip fluorescence signal which will be used to distinguishing between straight vs. turning growth behavior. The tip signal is obtained as a ratio of the average membrane-to-cytoplasm fluorescence values over time. A two-stage scheme is used to automatically detect individual growth intervals/cycles from the tip signal and split the experimental video into growth segments. In each growth segment, we extract relevant features. An initial classification uses structure-based features to distinguish between straight vs. turning growth cycles. The signal-based features are then used to train a Naive Bayes classifier to refine the miss-classifications of the initial classification. Our results show that this two-stage process yields good classification results.
A geometric constraint method for estimating 3-D camera motion

We investigate the problem of estimating the camera motion parameters from a 2D image sequence that has been obtained under combined 3D camera translation and rotation. Geometrical constraints in 2D are used to iteratively narrow down regions of possible values in the space of 3D motion parameters. The approach is based on a new concept called "FOE-feasibility" of an image region, for which an efficient algorithm has been implemented. Results are shown on real images.<<ETX>>
A system for aircraft recognition in perspective aerial images

Recognition of aircraft in complex, perspective aerial imagery has to be accomplished in presence of clutter, occlusion, shadow, and various forms of image degradation. This paper presents a system for aircraft recognition under real-world conditions that is based on the use of a hierarchical database of object models. The particular approach involves three key processes: (a) The qualitative object recognition process performs model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process refines the extracted features for more specific classification with input from (a); and (c) The primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.<<ETX>>
Adaptive target recognition

Target recognition is a multi-level 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 performance at the given probability of correct identification (PCI) and probability of false alarm (Pf) is a key challenge in computer vision and pattern recognition research. In this paper a robust closed-loop system for recognition of SAR images based on reinforcement learning is presented. The parameters in the model-based SAR target recognition are learned. The method meets performance specifications by using PCI and Pf as feedback for the learning system. It has been experimentally validated by learning the parameters of the recognition system for SAR imagery, successfully recognizing articulated targets, targets of different configuration and targets of different depression angles.
Automatic cell region detection by k-means with weighted entropy

In this paper, we propose an automatic method to detect human embryonic stem cell regions. The proposed method utilizes the K-means algorithm with weighted entropy. As in phase contrast images the cell regions have high intensity variation, they usually yield higher entropy values than the substrate regions which have less intensity variation. Thus, the entropy can be used as an important feature for the detection of stem cells. However, homogeneity in intensity within some of the cell bodies and halos surrounding the cell bodies also gives low entropy values. Therefore, we introduce a weighted entropy formulation which fuses entropy and image intensity information to detect the entire cell regions.
A reference-based framework for pose invariant face recognition

While face recognition technology has made significant progress in recent years, practical pose invariant face recognition remains a challenge. This paper describes a reference-based framework for solving this problem. The similarity between a face image and a set of reference individuals defines the reference-based descriptor for a face image. Recognition is performed using the reference-based descriptors of probe and gallery images. The dimensionality of the face descriptor generated by the accompanying face recognition algorithm is reduced to the number of individuals in the reference set. The proposed framework is a generalization of previous recognition methods that use indirect similarity and reference-based descriptors. Results are shown on a combination of seven publicly available face databases (LFW, FEI, RaFD, FERET, FacePix, CMU-PIE, and Multi-PIE). The proposed approach achieves good accuracy as compared to popular state-of-the-art algorithms, and it is computationally efficient.
Dynamic-Scene and Motion Analysis Using Passive Sensors - Part 1: A Qualitative Approach

Some dynamic-scene and motion analysis techniques developed to support the DARPA Strategic Computing program's autonomous land vehicle effort are described. The approach, called dynamic reasoning from integrated visual evidence (DRIVE), addresses the key problems of estimating robot motion from visual cues, detecting and tracking moving objects, and constructing and maintaining a global dynamic reference model. With reliable computation of displacement fields, this qualitative technique for understanding dynamic scenes reasons accurately on hundreds of image frames. The technique can also be extended to cases where the features are lines, regions, or contours.
Contour Matching for 3D Ear Recognition

Ear is a new class of relatively stable biometric that is invariant from childhood to early old age (8 to 70). It is not affected with facial expressions, cosmetics and eye glasses. In this paper, we introduce a two-step ICP (Iterative Closest Point) algorithm for matching 3D ears. In the first step, the helix of the ear in 3D images is detected. The ICP algorithm is run to find the initial rigid transformation to align a model ear helix with the test ear helix. In the second step, the initial transformation is applied to selected locations of model ears and the ICP algorithm iteratively refines the transformation to bring model ears and test ear into best alignment. The root mean square (RMS) registration error is used as the matching error criterion. The model ear with the minimum RMS error is declared as the recognized ear. Experimental results on a dataset of 30 subjects with 3D ear images are presented to demonstrate the effectiveness of the approach.
Recognition of Articulated and Occluded Objects

A model-based automatic target recognition system is developed to recognize articulated and occluded objects in synthetic aperture radar (SAR) images, based on invariant features of the objects. Characteristics of SAR target image scattering centers, azimuth variation, and articulation invariants are presented. The basic elements of the new recognition system are described and performance results are given for articulated, occluded and occluded articulated objects, and they are related to the target articulation invariance and percent unoccluded.
Structural Signatures for Passenger Vehicle Classification in Video

This paper focuses on a challenging pattern recognition problem of significant industrial impact, i.e., classifying vehicles from their rear videos as observed by a camera mounted on top of a highway with vehicles traveling at high speed. To solve this problem, this paper presents a novel feature called structural signature. From a rear-view video, a structural signature recovers the vehicle side profile information, which is crucial in its classification. As a vehicle moves away from a camera, its surfaces deform differently based on their relative orientation to the camera. This information is used to extract the structure of a vehicle, which captures the relative orientation of vehicle surfaces and the road surface. This paper presents a complete system that computes structural signatures and uses them for classification of passenger vehicles into sedans, pickups, and minivans/sport utility vehicles in highway videos. It analyzes the performance of the proposed system on a large video data set.
Vehicle logo super-resolution by canonical correlation analysis

Recognition of a vehicle make is of interest in the fields of law enforcement and surveillance. In this paper, we develop a canonical correlation analysis (CCA) based method for vehicle logo super-resolution to facilitate the recognition of the vehicle make. From a limited number of high-resolution logos, we populate the training dataset for each make using gamma transformations. Given a vehicle logo from low-resolution source (i.e., surveillance or traffic camera recordings), the learned models yield super-resolved results. By matching the low-resolution image and the generated high-resolution images, we select the final output that is closest to the low-resolution image in the histogram of oriented gradients (HOG) feature space. Experimental results show that our approach outperforms the state-of-the-art super-resolution methods in qualitative and quantitative measures. Furthermore, the super-resolved logos help to improve the accuracy in the subsequent recognition tasks significantly.
Improving retrieval performance by long-term relevance information

Relevance feedback (RF) is an iterative process which improves the retrieval performance by utilizing the user's feedback on retrieved results. Traditional RF techniques use solely the short-term experience and are short of knowledge of cross-session agreement. In this paper, we propose a novel RF framework which facilitates the combination of short-term and long-term experiences by integrating the traditional methods and a new technique called the virtual feature. The feedback history of all the users is digested by the system and is represented as a virtual feature of the images. As such, the dissimilarity measure can be adapted dynamically depending on the estimate of the relevance probability derived from the virtual features. The results manifest that the proposed framework outperforms the one that adopts a single traditional RF technique.