waashk/dblp huggingface dataset:
using specularities to recover multiple light sources in the presence of texture. recovering multiple point light sources from a sparse set of photographs in which objects of unknown texture can move is challenging. this is because both diffuse and specular reflections appear to slide across surfaces. what is seldom demonstrated, however, is that it can be taken advantage of to address the light-source recovery problem. in this paper, we therefore show that, if 3d models of the moving objects are available or can be computed from the images, we can solve the problem without any a priori constraints on the number of sources, on their color, or on the surface albedos. our approach involves finding local maxima in individual images, checking them for consistency across images, retaining the apparently specular ones, and having them vote in a hough-like scheme for potential light source directions. the precise directions of the sources and their relative power are then obtained by optimizing a standard lighting model. as a byproduct we also obtain an estimate of various material parameters such as the unlighted texture and specular properties. we show that the resulting algorithm can operate in presence of arbitrary textures and an unknown number of light sources of possibly different unknown colors. we also estimate its accuracy using ground-truth data.
a shape-preserving non-parametric symmetry transform. recently, a non-parametric image measure, symmetry, for local binary patterns has been introduced. in this paper, we propose a generalised symmetry transform for arbitrary neighbourhoods and describe its theoretical properties. the symmetry of the local binary patterns is shown to be a special case of a more general non-parametric concept. applications of the symmetry include searching of interest points and correspondence.
successive-least-squares error algorithm on minimum description length neural networks for time series prediction. a successive least-squares approach is proposed to find an optimal model of a flat neural network in a short period of time. it is based on a minimum description length (mdl) neural network that uses the mdl principle as the stopping criterion. different from conventional algorithms on flat neural networks that apply least-squares technique on weights between hidden layer and output layer only, it extends the least-squares technique to weights between the input layer and the hidden layer. we apply this algorithm to the chaotic mackey-glass time series and chaotic laser time series. the results show that it provides satisfactory prediction within a small amount of time.
using extended em to segment planar structures in 3d. the proposed algorithm segments planar structures out of data gained from 3d laser range scanners, typically used in robotics. the approach first fits planar patches to the dataset, using a new, extended expectation maximization (em) algorithm. this algorithm solves the classical em problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge framework. determining the fitting quality of the gained patches, the approach then allows for segmentation of planar surfaces out of the 3d environment. the result is a set of 2d objects, which can be used as input for classical computer vision applications, in particular for object recognition. our approach makes it possible to apply classical tools of 2d image processing to solve problems of 3d robot mapping, e.g. landmark recognition.
registration and fusion of retinal images: a comparative study. we present a new method to register high and low resolution color images of the retina as well as high resolution angiographies. the registration method is based on global point mapping with blood vessel bifurcations as control points. we also present results of various image fusion algorithms to determine the most appropriate one. registration and fusion quality assessment is also discussed.
human identification by using the motion and static characteristic of gait. in this paper, we propose a gait recognition algorithm that fuses motion and static spatio-temporal templates of sequences of silhouette images, the motion silhouette contour templates (mscts) and static silhouette templates (ssts). the performance of the proposed algorithm is evaluated experimentally using the soton dataset and the usf dataset. the proposed algorithm has a recognition rate of around 80% in intrinsic difference group (probe a-c) of usf dataset which is 14% higher than the baseline algorithm.
improved clustering algorithm based on calculus of variation. a major problem in data clustering is the degradation in performance due to outliers. we have developed a robust method to solve this problem using the l2m-fcm algorithm. however, this method has to solve a non-linear equation and can converge to a local optimum. in this paper, we introduce a regularized version of the l2m-fcm algorithm. the essential idea is to constrain the descent direction in the optimization procedure. we employ a novel method to correct the direction using the calculus of variations. experimental results show that the proposed method has a better performance than seven other clustering algorithms for both synthetic and real world data sets.
background subtraction using competing models in the block-dct domain. many image analysis applications rely on background subtraction as a pre-processing step. hence it should be efficient and robust. we present a background subtraction algorithm that uses multiple competing hidden-markov-models (hmms) over small neighbourhoods to maintain a locally valid background model in all situations. we use the dct coefficients of jpeg encoded images directly to minimize computation and to use local information in a principled way. region level processing is reduced to the minimum so that the extracted information that goes to higher level processing is unbiased.
atlas-based 3d-shape reconstruction from x-ray images. in many cases x-ray images are the only basis for surgery planning. nevertheless it is desirable to draw conclusions about the 3d-anatomy of the patient from such data. this work presents a method to reconstruct 3d shapes from few digital x-ray images on the basis of 3d-statistical shape models. at the core of this method lies an algorithm which optimizes a similarity measure assessing the difference between projections of the shape model and the x-ray images. based on theoretical and experimental observations we propose to measure the distance between the silhouettes of the object in the projections. the method is tested on 23 synthetically generated x-rays from ct data sets of the the geometrically as well as topologically complex shape of the pelvic bone.
machine learning for video compression: macroblock mode decision. video compression currently is dominated by engineering and fine-tuned heuristic methods. in this paper, we propose to instead apply the well-developed machinery of machine learning in order to support the optimization of existing video encoders and the creation of new ones. exemplarily, we show how by machine learning we can improve one encoding step that is crucial for the performance of all current video standards: macroblock mode decision. by formulating the problem in a bayesian setup, we show that macroblock mode decision can be reduced to a classification problem with a cost function for misclassification that is sample dependent. we demonstrate how to apply different machine learning techniques to obtain suitable classifiers and we show in detailed experiments that all of these perform better than the state-of-the-art heuristic method.
multi-level anchorperson detection using multimodal association. in contemporary tv news programs, multi-level anchorpersons are often used which indicate the inherent hierarchical structure of news program. however, these diverse anchorperson patterns make the conventional anchorperson detection algorithms failed. in this paper, we propose a robust approach to anchorperson detection by integrating visual modality, auditory modality and human appearance modality into multimodal associated clustering. based on the structure of clustered multi-level anchorpersons, the toc (table-of-content) of news video can be effectively generated. the effectiveness and robustness of the proposed approach are demonstrated by the experiments on five hours news programs fromz different tv channels.
precision-recall operating characteristic (p-roc) curves in imprecise environments. traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. in this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (roc). we argue that while roc analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (anova). the increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.
tree-like data structures for effective recognition of 2-d solids. an approach to extracting and recognizing the patterns given by the solids in two-level images is developed.the applied data processing and analyzing procedures are based on using the tree-structured representations of both the whole image and the individual patterns.these representations provide a significant reduction of calculations at the stages of pattern segmentation and recognition.the estimations of the fidelity and computational complexity performances have been obtained for the recognition procedure based on source coding and sequential decoding schemes.
active feature models. in this paper active feature models are proposed. they utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. they are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. the approach has advantages with complex image data like anatomical structures that exhibit high texture variation with limited relevance for the recognition of the object location. experimental results and the comparison to aams on different data sets indicate that active feature models can improve search speed and result accuracy, considerably.
building and registering parameterized 3d models of vessel trees for visualization during intervention. in this paper we address the problem of multimodal registration of coronary vessels by developing a 3d parametrical model of vessel trees from computer tomography data and registering it to angiography images during intervention. thus, the interventionist takes profit from 3d data otherwise only available before the intervention. this facilitates orientation in ambiguous radiographs, interactive visualization of all vessel structures to estimate their mutual position and navigation within the vessel system and ultimately reduces the radiation the patient and the physicians are exposed to. the model is build by exploring the branching vessel tree starting from a single position and successively expanding through the vessels guided by a local deformable surface. the result is a tree of cylindrical segments each adapted to the vessel walls that is registered to angiography images in a fast and robust way. validation on 8 patients confirms the robustness of our method.
gender recognition in non controlled environments. in most of the automatic face classification applications, images should be captured in natural environments, where partial occlusions or high local changes in the illumination are frequent. for this reason, face classification tasks in uncontrolled environment are still nowadays unsolved problems, given that the loss of information caused by these artifacts can easily mislead any classifier. we present in this paper a system to extract robust face features that can be applied to encode information from any zone of the face and that can be used for different face classification problems. to test this method we include the results obtained in different gender classification experiments, considering controlled and uncontrolled environments and extracting face features from internal and external face zones. the obtained rates show, on the one hand, that we can obtain significant information applying the presented feature extraction scheme and, on the other hand, that the external face zone can contribute useful information for classification purposes.
a fast discriminant approach to active object recognition and pose estimation. this paper presents a new criterion for viewpoint selection in the context of active bayesian object recognition and pose estimation. recognition is performed by probabilistically fusing successive observations with the current belief state of the system. based on the current belief state, the next viewpoint is chosen to maximize the expected discriminability of the current competing hypotheses. experiments on a difficult database of aircraft models show that this approach achieves comparable recognition performance to the widely used information theoretic approaches at a much lower computational cost.
velocity adaptation of space-time interest points. the notion of local features in space-time has recently been proposed to capture and describe local events in video. when computing space-time descriptors, however, the result may strongly depend on the relative motion between the object and the camera. to compensate for this variation, we present a method that automatically adapts the features to the local velocity of the image pattern and, hence, results in a video representation that is stable with respect to different amounts of camera motion. experimentally we show that the use of velocity adaptation substantially increases the repeatability of interest points as well as the stability of their associated descriptors. moreover, for an application to human action recognition we demonstrate how velocity-adapted features enable recognition of human actions in situations with unknown camera motion and complex, non-stationary backgrounds.
object tracking with dynamic template update and occlusion detec. the objective of this paper is to track moving objects using dynamic template initializations and updates, and to identify tracking events in videos such as occlusions and merging of motion regions. the proposed tracking method is based on spatiotemporal texture motion regions, image alignment, and minimum cost estimation based template selection. the dynamic template update is based on the detection of events in videos. the proposed method has been experimentally evaluated on color and thermal infrared videos.
stroke extraction and stroke sequence estimation on signatures. this article addresses the problem in strokes extraction and stroke sequence estimation from a static signature. a new algorithm is proposed based on a simple but effective writer model. the results are encouraging.
shape-based contour interpolation and extrapolation using distance mapping. we propose an approach to the problem of reconstruction of 3d surfaces from magnetic resonance (mr) images. we use shape-based contour interpolation and extrapolation algorithms implemented based on the active contour technique; our idea is to use distance mapping of feature points in order to model the active contour dynamics. we establish differential equations describing the active contour dynamics and set up an iterative procedure for numerical solution. the efficiency of approach is demonstrated on contours of liver and liver tumors in mr images.
local behaviours labelling for content based video copy detection. this paper presents an approach for indexing a large set of videos by considering the dynamic behaviour of local visual features along the sequences. the proposed concept is based on the extraction and the local description of interest points and further on the estimation of their trajectories along the video sequence. analysing the low-level description obtained allows to highlight trends of behaviour and then to assign a label. such an indexing approach of the video content has several interesting properties: the lowlevel descriptors provide a rich and compact description, while labels of behaviour provide a generic semantic description of the video content, relevant for video content retrieval. we demonstrate the effectiveness of this approach for content-based copy detection (cbcd) on large collections of videos (several hundred hours of videos).
learning integrated perception-based speed control. advances in the area of autonomous mobile robotics have allowed robots to explore vast and often unknown terrains. this paper presents a particular form of autonomy that allows a robot to autonomously control its speed, based on perception, while traveling on unknown terrain. the robot is equipped with an onboard camera and a 3-axis accelerometer. the method begins by classifying a query image of the terrain immediately before the robot. classification is based on the gabor wavelet features. in learning the speed, a genetic algorithm is used to map the gabor texture features to approximate speed that minimizes changes in accelerations along the three axes from their nominal values. learning is performed continuously. experiments are done in real time.
svm training time reduction using vector quantization. in this paper, we describe a new method for training svm on large data sets. vector quantization is applied to reduce a large data set by replacing examples by prototypes. training time for choosing optimal parameters is greatly reduced. some experimental results yields to demonstrate that this method can reduce training time by a factor of 100, while preserving classification rate. moreover this method allows to find a decision function with a low complexity when the training data set includes noisy or error examples.
3d hand reconstruction from a monocular view. in this paper, we describe an approach for human hand motion detection and its reconstruction using an articulated model with hand kinematics constraints. each finger can be modeled as planar robot arm with 3 joints and 3 links. we assume that first joints connecting palm and each finger configure a rigid form. by this simplification, we have several hand constraints which reduce the complexity of the estimation process and allow to infer the 3d hand motion and pose in ambiguous situations. the main advantage of the proposed approach is its ability to capture general articulated hand motion with self self-occlusion and rotation of the palm. the proposed method is illustrated on a set of examples of a hand motion captured from a monocular image sequence.
a robust fingerprint matching algorithm using local alignment. this paper describes a minutiae-based fingerprint matchig algorithm. geerally, a figerprit image is non-linearly deformed by torsion and traction when a finger is pressed on the sensor. this nonlinear deformation changes both position and orientation of minutiae and decreases thereliability o f minutiae. therefore, in matching algorithm using one referece minutiae pair, the reliability of a minutia decreases as the distance from the minutia to the minutia used for alignment increases. the proposed algorithm over-comes this problem by normalizing the distance between minutiae and using local alignment. experimental results show that the performace of the proposed algorithm is superior to that of using one referece minutiae pair.
extraction and recognition of license plates of motorcycles and vehicles on highways. in this paper, a recognition system is proposed to extract and recognize license plates of motorcycles and vehicles on highways. in the first stage, a block-difference method is used to detect moving objects. according to the variance and the similarity of the mxn blocks defined on two diagonal lines, the blocks are categorized as three kinds: low-contrast, stationary and moving blocks. in the second stage, a screening method based on the projection of edge magnitudes is used to find two peaks in the projection histograms to bound license plates. the scanning lines with low counts can be removed. in the third stage, character images are segmented and recognized. in our experiments, we tested 180 pairs of images. the block-difference method has a 98% success rate and can remove 88% of pixels from an image on average. the screening method has a 94.4% success rate and the character recognition method has a 95.7% precision rate.
nonlinear shape and appearance models for facial expression analysis and synthesis. facial expression passes through nonlinear shape and appearance deformations with variations in different people and expressions. we present nonlinear shape and appearance models for facial expression analysis and synthesis using nonlinear generative models for different facial expressions in different people. to achieve accurate shape normalized appearance models, we utilize nonlinear warping using thin plate spline (tps). a novel nonlinear generative model using conceptual manifold embedding and empirical kernel maps for facial expressions provides facial shape and appearance samples according to the configuration, personal style, and expression parameters. we can recognize facial expressions based on estimated facial expression parameters after iterative estimations of facial expression and style. in addition, the model provides accurate synthesis of facial expression sequences even with high nonlinear deformations of shape and appearance during facial expressions.
simultaneous inference of view and body pose using torus manifolds. inferring 3d body pose as well as viewpoint from a single silhouette image is a challenging problem. we present a new generative model to represent shape deformations according to view and body configuration changes on a two dimensional manifold. we model the two continuous states by a product space (different configurations × different views) embedded on a conceptual two dimensional torus manifold. we learn a nonlinear mapping between torus manifold embedding and visual input (silhouettes) using empirical kernel mapping. since every view and body pose has a corresponding embedding point on the torus manifold, inferring view and body pose from a given image becomes estimating the embedding point from a given input. as the shape varies in different people even in the same view and body pose, we extend our model to be adaptive to different people by decomposing person dependent style factors. experimental results with real data as well as synthetic data show simultaneous estimation of view and body configuration from given silhouettes from unknown people.