waashk/dblp huggingface dataset:
detecting rare events in video using semantic primitives with hmm. we present a new approach for recognizing rare events in aerial video. we use the framework of hidden markov models (hmms) to represent the spatio-temporal relations between objects and uncertainty in observations, where the data observables are semantic spatial primitives encoded based on prior knowledge about the events of interest. events are observed as a sequence of binarized distance relations among the objects participating in the event. this avoids directly modeling the temporal trajectories of continuous observables, which is difficult when training data is scarce. the approach enables better generalization to other scenes for which little or no training data may be available. we demonstrate the effectiveness of our approach using real aerial video and simulated data.
event recognition with fragmented object tracks. complete and accurate video tracking is very difficult to achieve in practice due to long occlusions, traffic clutter, shadows and appearance changes. in this paper, we study the feasibility of event recognition when object tracks are fragmented. by changing the lock score threshold controlling track termination, different levels of track fragmentation are generated. the effect on event recognition is revealed by examining the event model match score as a function of lock score threshold. using a dynamic bayesian network to model events, it is shown that event recognition actually improves with greater track fragmentation, assuming fragmented tracks for the same object are linked together. the improvement continues up to a point when it is more likely to be offset by other errors such as those caused by frequent object reinitialization. the study is conducted on busy scenes of airplane servicing activities where long tracking gaps occur intermittently.
multiplierless fast dct algorithms with minimal approximation errors. the approximation method proposed in this paper converts each constant in a dct algorithm into a minimum number of signed digits. then any multiplication in the transform can be turned into a number of add-and-shift operations on an integer value. with this technique, any fast dct algorithm can be made multiplierless. to reduce the algorithm complexity and the word length requirement, we developed an effective algorithm for converting any constant into a signed digits string with minimum number of non-zero signed digits and a reduced word length. the approximation errors of different constants could affect differently the mse of the approximated algorithm. the simple strategy is to assign more digits to those constants whose errors are more sensitive to the mse of the algorithm whose complexity will however increase when the total number of digits assigned to the constants becomes larger. this paper devised an efficient algorithm to find an optimized signed digits configuration for minimizing the mse of the algorithm with a specified complexity. experiment results show that lee's fast dct algorithm approximated by the proposed method can be used to reconstruct images with high visual quality in terms of psnr.
extending the depth of field in a compound-eye imaging system with super-resolution reconstruction. optical device miniaturization is highly desirable in many applications. direct down-scaling of traditional imaging system is one approach, but the extent to which it can be minimized is limited by the effect of diffraction. compound-eye imaging system, which utilizes multiple microlenses in image capture is a promising alternative. in this paper, we explore the possibility of an incorporation of phase masks in such a system to extend the depth of field. simulation experiments are conducted to verify the feasibility of the system.
pose estimation for multiple camera systems. pose estimation of a multiple camera system (mcs) is usually achieved by either solving the pnp problem or finding the least-squared-error rigid transformation between two 3d point sets. these methods employ partial information of an mcs, in which only a small number of features in one or two cameras can be utilized. to overcome this limitation, we propose a new pose estimation method for an mcs that uses complete information of an mcs. in our method, we treat the mcs as a single generalized camera [a general imaging model and a method for finding its parameters][using many cameras as one] and formulate this problem in a least-squared manner. an iterative algorithm is proposed for solving the least-squared problem. from the experimental results, it shows that the proposed method is accurate for pose estimation of mcs.
discriminative descriptor-based observation model for visual tracking. varying illumination and partial occlusion are two main difficulties in visual tracking. existing methods based on appearance information cannot solve these problems effectively since appearance is sensitive to lighting and the appearances under occlusions are quite different. in this paper, we propose a descriptor-based dynamic tracking approach that can track objects under partial occlusions and varying illumination. instead of global appearance, an object is represented by a set of invariant feature descriptors that are generated from local regions around some salient points. by integrating the local descriptor information into the observation model, our method is effective under varying illumination and partial occlusions.
an intelligent bulletin board system with real-time vision-based interaction using head pose estimation. a bulletin board is a place where people can read announcements, posters or leave public messages. however, traditional bulletin board certainly have several disadvantages such like without real-time interaction with users and monotonous. this paper presents an intelligent bulletin board system (ibbs), which allows a user to real-time interact with it without any additional auxiliaries. at first, a real-time front-view face detection using harr-like features is used to decide when ibbs should wake up and become interactive with the user. after system initialization, we keep on finding some feature points within the detected face area. then we estimate the orientation of user's head via pyramidal lucas-kanade optical flow tracking. compared to the traditional bulletin board system, our system has more flexibility. with the comparison to other non-vision-based input devices such like gloves or markers, our system offers a simple, useful and economical solution for the real-time interaction between the user and computer.
physics-based fusion of multispectral data for improved face recognition. a novel physics-based fusion of multispectral images within the visual spectra is proposed for the purpose of improving face recognition under constant or varying illumination. spectral images are fused according to the physics properties of the imaging system, including illumination, spectral response of the camera, and spectral reflectance of skin. the fused image is given as a probe to the recognition software faceit® which compares it to a gallery of images. the identification performance of our physics-based fusion method is compared to the performance of principle component analysis and average fusion methods. the results show that the proposed fusion yields a higher identification rate. a method of illumination adjustment is proposed when the probe and gallery images are acquired under different illumination conditions. the results show that the identification rate is higher than that of unadjusted gray-level images.
detecting deformable objects with flexible shape priors. we address the problem of detecting objcts/shapes with large deformation and articulation in cluttered images. the approach requires a shape prior that describes the approximated outline and articulation property of a given model. while dynamic programming is often used in solving shape detection, our focus is on formulating a more effective energy function to evaluate the optimality of a matching between the shape prior and image features. for efficiency, the detection via optimization is carried out over a non-uniform elastic grid based on referencing the edge information. experimental results are included to illustrate our method.
applying a hybrid method to handwritten character recognition. in this paper, we propose a new prototype learning/matching method that can be combined with support vector machines (svm) in pattern recognition. this hybrid method has the following merits. one, the learning algorithm for constructing prototypes determines both the number and the location of prototypes. this algorithm terminates within a finite number of iterations and assures that each training sample matches in class types with the nearest prototype. two, svm can be used to process top-rank candidates obtained by the prototype learning/matching method so as to save time in both training and testing processes. we apply our method to recognizing handwritten numerals and handwritten chinese/hiragana characters.experiment results show that the hybrid method saves great amount of training and testing time in large-scale tasks and achieves comparable accuracy rates to those achieved by using svm solely. our results also show that the hybrid method performs better than the nearest neighbour method.
an iterative bayesian approach for digital matting. this paper proposes a new iterative approach for digital image matting. it combines pre-segmentation and matting into an unified approach and extracts good matte iteratively within a well-defined bayesian framework based on a few user strokes on foreground and background regions. this method does not need a well specified trimap, which refers to a pre-segmented image with definitely foreground, definitely background and unknown regions, and can therefore efficiently handle the images, in which the trimap is very hard to create even manually. experimental results show that, compared with previous approaches, our method is more convenient and robust especially for images with large amount of holes or with foreground objects containing large portion of semi-transparent parts.
non stationary bayesian image restoration. in this paper we propose a new iterative bayesian non stationary image restoration algorithm. the main novelty of this approach is the introduction of a hierarchical non stationary image prior. based on this prior and the generative graphical model for the observations, bayesian inference is performed integrating out the hidden variables. an interesting byproduct of this approach is the justification, using a bayesian framework, of previous non stationary image restoration formulations that were based on heuristic arguments. numerical experiments are provided that demonstrate the advantages of the proposed non stationary approach as compared with stationary approaches.
a fibre bundle model of surfaces and its generalization. a fibre bundle model of shapes is proposed to describe a surface as a local direct product of a base curve and a fibre curve. with fibre curves as 1-parameter groups, this model is efficient in both synthesis and recognition. in fact, the 1-parameter groups can be uniquely determined by finite, e.g. six invariants of their lie algebras. besides the surfaces can be fastly generated by elementary function without numerical integration error. this model is then extended to fibres defined by high order ode.
defect detection in low-contrast glass substrates using anisotropic diffusion. in this research, we propose an anisotropic diffusion scheme to detect defects in low-contrast surface images and, especially, aim at glass substrates used in tft-lcds (thin film transistor-liquid crystal displays). in a sensed glass substrate, the gray levels of defects and background are hardly distinguishable and result in a low-contrast image. therefore, thresholding and edge detection techniques cannot be applied to detect subtle defects in the glass substrates surface. the proposed diffusion method in this paper can simultaneously carry out the smoothing and sharpening operations. it adaptively triggers the smoothing process in faultless areas to make the background uniform, and performs the sharpening process in defective areas to enhance anomalies. experimental results from a number of glass substrate samples including backlight panels and lcd glass substrates have shown the efficacy of the proposed diffusion scheme in low-contrast surface inspection.
a kernel-based discrimination framework for solving hypothesis testing problems with application to speaker verification. real-word applications often involve a binary hypothesis testing problem with one of the two hypotheses ill-defined and hard to be characterized precisely by a single measure. in this paper, we develop a framework that integrates multiple hypothesis testing measures into a unified decision basis, and apply kernel-based classification techniques, namely, kernel fisher discriminant (kfd) and support vector machine (svm), to optimize the integration. experiments conducted on speaker verification demonstrate the superiority of our approaches over the predominant approaches.
a robust region-based multiscale image fusion scheme for mis-registration problem of thermal and visible images. in this paper, a region based multiscale image fusion based on discrete wavelet frame (dwf) is proposed to combine information from thermal and visible images. because of the fusion result of thermal and visible image may then allow both detection and imperceptible localization of the target in thermal image with respect to the discernible background provided by visible image. additionally, the objective is to decrease the misregistration problem of these images which is actually exists in practice. the concept of discrete wavelet frame, which leads to translation invariant, can solve this problem. in our experiments, the preprocessing procedure of these source images is approached. it includes enhancement and registration based on object matching. experimental results show that the proposed fusion scheme provides more robust when mis-registration distortion exists for the source images.
learning and extracting edges from images by a modified hopfield neural network. this paper introduced a modified unsupetvised hopfield network that can learn the underlying process in an edge detection task from grey level images. after the learning phase, the network peiformance is tested to ensure that it can detect only the significant edges andthat it can generalise other images.
real time tracking with occlusion and illumination variations. the authors present a robust real time object tracking by image processing. a planar model defined by a center and few interest points of the object is used. this model choice allows to be robust to light variations and partial occlusions and a particle filter based tracker allows to recover from total occlusion of the object during a few seconds. this algorithm works at 15hz on a personal computer under linux and is robust concerning both geometrical aspects as planar rotation and scale modification.real experimentations prove the validity of the method for outdoor applications like vehicule tracking for adaptive cruise control.
a two-stage outlier rejection strategy for numerical field extraction in handwritten documents. in this article, we propose a segmentation-driven recognition system which aims at extracting numerical fields from handwritten documents. we show that a crucial point of the system is the rejection ability of the handwritten numeral classifier. therefore, we propose a simple two-stage outlier rejection strategy, and we show the benefit of this strategy on the numerical field extraction results.
local representation of 3d free-form contours for pose estimation. in this paper we present a new representation for 3d free-form contours in the conformal geometric algebra g4,1. this new representation allows to extract local geometrical feature information which is used to solve the correspondence problem for pose estimation applications. under perspective projection, local features are extracted from a projected contour segment and compared with image features obtained from the monogenic signal. we tested our approach using synthetical and real data for several pose estimation algorithms.
automatic sleep apnoea detection using measures of amplitude and heart rate variability from the electrocardiogram. a method for the automatic processing of the electrocardiogram (ecg) for the detection of disordered breathing associated with obstructive sleep apnoea is presented. the method provides a minute-by-minute analysis of night-time single lead ecg recordings. anindependently validated database of 35 ecg recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. a wide variety of features based on heart beat intervals and an electrocardiogram derived respiratory signal were considered. classifiers based on linear and quadratic discriminants were used. results show that a 90% success rate in correctly identifying one-minute segments containing disordered breathing is achievable.
media content and type selection from always-on wearable video. a system is described for summarizing head-mounted or hand-carried "always-on" video. the example used is a tourist walking around a historic city with friends and family. the summary consists of a mixture of stills, panoramas and video clips. the system identifies both the scenes to appear in the summary and the media type used to represent them. as there are few shot boundaries in this class of video, the decisions are based on the system's classification of the user's behaviour demonstrated by the motion of the camera, and motion in the scene.
m-estimator based robust kernels for support vector machines. in this paper, we propose m-estimator based robust kernels for support vector machine. the main motivation for our proposed kernels is that the sum of squared difference in the widely used gaussian radial basis function kernels is not robust to outlier or noise. in addition, inspired by using a robust loss function in support vector machine regression to control training error [learning with kernels - support vector machines, regularization, optimzation and be-yond] and the idea of robust template matching with m-estimator [fast algorithm for robust template matching with m-estimators], we apply m-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for support vector machines. we test our proposed kernels in several classification benchmark datasets and experimental results show that svm with proposed kernels are better than svm with gaussian radial basis function kernels.
an application of robust template matching to user location on wireless infrastructure. instead of using conventional template matching with the sum of squared distance (ssd) for signal strength matching to provide user location on wireless infrastructure[radar: an in-building rf based user location and tracking system], we propose the use of robust template matching methods [fast algorithm for robust template matching with m-estimators] for providing robust user location. our method expects and handles the noisy data common within a wireless setting, and provides the user with relevant information as to the reliability of their location within the system. in our experiments, we compare our method with conventional ssd based and sum of absolute difference (sad) based template matching and a state of the art method, support vector machines [the nature of statistical learning theory]. our experimental results show that some of robust template matching methods can outperform compared methods very much.
noise variance adaptive sea for motion estimation: a two-stage schema. in a practical video encoder, a video sequence obtained from a ccd camera inevitably conveys noise, which degrades not only image quality but also coding efficiency. based on the statistic analysis of noise signal,a noise variance adaptive two-stage successive elimination algorithm(nva-sea) for block motion estimation is presented. simulation results demonstrate that the proposed algorithm can get close performance to the full search, while the computation time has been significantly reduced.
3d free-form object recognition in range images using local surface patches. this paper introduces an integrated local surface descriptor for surface representation and 3d object recognition. a local surface descriptor is characterized by its centroid, its local surface type and a 2d histogram. the 2d histogram shows the frequency of occurrence of shape index values vs. the angles between the normal of reference feature point and that of its neighbors. instead of calculating local surface descriptors for all the 3d surface points, they are calculated only for feature points that are in areas with large shape variation. in order to speed up the retrieval of surface descriptors and to deal with a large set of objects, the local surface patches of models are indexed into a hash table. given a set of test local surface patches, votes are cast for models containing similar surface descriptors. based on potential corresponding local surface patches candidate models are hypothesized. verification is performed by running the iterative closest point (icp) algorithm to align models with the test data for the most likely models occurring in a scene. experimental results with real range data are presented to demonstrate and compare the effectiveness and efficiency of the proposed approach with the spin image and the spherical spin image representations.
human ear detection from side face range images. ear detection is an important part of an ear recognition system. in this paper we address human ear detection from side face range images. we introduce a simple and effective method to detect ears, which has two stages: offline model template building and on-line detection. the model template is represented by an averaged histogram of shape index. the on-line detection is a four-step process: step edge detection and thresholding, image dilation, connect-component labeling and template matching. experiment results with real ear images are presented to demonstrate the effectiveness of our approach.
invariant texture classification using ridgelet packets. in this paper, we propose a novel rotation invariant texture classification technique by using ridgelet packets. ridgelet packets provide many orthonormal bases that can effectively capture directional features present in textures. the fourier transform is good at eliminating the texture orientation differences. by combining these two tools, a very efficient rotation invariant texture classification technique is created. experimental results show that the proposed method achieves very high classification rates and it outperforms two state-of-the-art methods for rotation invariant texture classification under both noise-free and noisy environments.
function dot product kernels for support vector machine. a new family of kernels for support vector machine is proposed by taking the dot product of two function vectors. these kernels are proved to be admissible support vector kernels, and the dot product function in the kernels can be selected as the polynomial, the gaussian radial basis function, the exponential radial basis function, the wavelet function, the autocorrelation wavelet function, the probability function, etc. experiments show the feasibility of the proposed kernels for pattern recognition. the dual-tree complex wavelet is used to extract invariant features for recognizing similar handwritten numerals, and the recognition rate is about 99.50% for a training data set of 800 samples and a testing data set of 400 samples. it is also possible to apply the proposed kernels to function regression.