waashk/dblp huggingface dataset:
generalizing inverse compositional image alignment. the inverse compositional (ic) approach to image alignment uses characteristics of the alignment problem to improve optimization speed. while a number of authors have noted its usefulness, to date it has only been explored for least-squares type image difference measures using gauss- newton optimization schemes. we extend the ic approach to general difference measures, and a wider class of optimization approaches, with specific development for normalized correlation and mutual information using the bfgs optimizer. we present alignment experiments on image pairs of several different classes that demonstrate performance improvements for the general case.
efficient calculation of the complete optimal classification set. feature and structure selection is an important part of many classification problems. in previous papers, an approach called basis pursuit classification has been proposed which poses feature selection as a regularization problem using a 1-norm to measure parameter complexity. in addition, a complete optimal parameter set, here called the locus, can be calculated which contains every optimal collection of sparse features as a function of the regularization parameter. this paper considers how to iteratively calculate the parameter locus using a set of rank-1 inverse matrix updates. the algorithm is tested on both artificial and real data and it is shown that the computational cost is reduced from a cubed to a squared problem in the number of features.
image analysis through local information measures. the properties of local image statistics are analyzed in a classic information theoretic setting. local spatiochromatic image elements are projected into a space in which constituent components are independent by way of independent component analysis, allowing a fast and tractable means of considering the joint likelihood of such statistics. observation of this likelihood allows inferences to be made regarding the informativeness of a particular set of statistics. this operation is shown to illuminate a number of perceptually important image properties, allowing figure-ground segmentation, removal of common or expected image elements, and prediction of regions of interest.
image segmentation by shape particle filtering. statistical appearance models are valuable tools in medical image segmentation. current methods elegantly incorporate global shape and appearance, but can not cope with local appearance variations and rely on an assumption of gaussian gray value distribution. furthermore, initialization near the optimal solution is required. we propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. optimization by stochastic sampling removes the need for accurate initialization. the method is demonstrated on vertebra segmentation in spine radiographs. segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.
scratch detection via underdamped harmonic motion. this paper presents sdho (scratch detection via harmonic oscillator), a generalized model to detect line scratches on digital images. the line scratch profile is now a solution of a second order homogeneous differential equation, depending exclusively on the width and the brightness of the scratch. this method performs noticeably better than the other existing techniques with a lower computing time.
fast removal of line scratches in old movies. in this paper a fast algorithmfor removing line scratches in old movies is presented. it is strongly based on exploiting the defect visibility in the image. to this aim the weber's law can be applied to coefficients of an over-complete wavelet representation of the degraded image. the intensity of the defect, which is represented as a light diffraction effect, is then attenuated in the vertical and approximation sub-bands till the minimum threshold of visibility is reached. the experimental results are very satisfying: the image is completely recovered without local artefacts or annoying smoothing effects.
video structuring, indexing and retrieval based on global motion wavelet coefficients. this paper describes an approach for video structuring and indexing. it relies on motion wavelet coefficients directly estimated from image sequence. these coefficients provide a multiscale characterization of optical flow . they allow to define dominant and local motion descriptors, respectively related to camera and object displacements. we use dominant motion descriptors to perform a temporal segmentation of the sequence. shots extracted are characterized in term of dominant motion properties and indexed by using descriptors related to local motion content. these operations allow to retrieve shots, by example queries, according to only dynamic content of the scene and not camera displacements.
interactive road extraction with pixel force fields. pixel force field (pff) is a novel image representation where at each pixel a two-dimensional vector is defined for representing interaction of pixels. the vector is oriented to the center of the region composed of pixels having the same qualitative property, such as color and gray-scale level. using the pixel force field and improved live-wire segmentation technique the task of interactive road extraction from remote sensing images is solved.
application of non-negative and local non negative matrix factorization to facial expression recognition. in this paper two image representation approaches called non-negative matrix factorization (nmf) and local non-negative matrix factorization (lnmf) have been applied to two facial databases for recognizing six basic facial expressions. a principal component analysis (pca) approach was performed as well for facial expression recognition for comparison purposes. we found that, for the first database, lnmf outperforms both pca and nmf, while nmf produces the poorest recognition performance. results are approximately the same for the second database, with slightly performance improvement on behalf of nmf.
performance driven facial animation using illumination independent appearance-based tracking. we introduce a procedure to estimate human face high level animation parameters from a marker-less image sequence in presence of strong illumination changes. we use an efficient appearance-based tracker to stabilise face images and estimate illumination variation. this is achieved by using an appearance model composed by two independent linear subspaces modelling face deformation and illumination changes respectively. the system is very simple to train and is able to re-animate a 3d face model in real-time.
view-based dynamic object recognition based on human perception. psychophysical studies have shown that humans actively exploit temporal information such as contiguity of images in object recognition. we have recently developed a recognition system which uses temporal contiguity to learn extensible representations of objects on-line. the system performs well both on real-world and synthetic data and shows robustness under illumination changes. in this paper, we present results which compare the proposed representation against simple image-based representations of the same complexity using minkowski minimum distance classifiers and support vector machine classifiers. recognition results for all classifiers show large improvements with incorporated temporal information.
robot navigation by panoramic vision and attention guided fetaures. in visual-based robot navigation, panoramic vision emerges as a very attractive candidate for solving the localization task. unfortunately, current systems rely on specific feature selection processes that do not cover the requirements of general purpose robots. in order to fulfill new requirements of robot versatility and robustness to environmental changes, we propose in this paper to perform the feature selection of a panoramic vision system by means of the saliency-based model of visual attention, a model known for its universality. the first part of the paper describes a localization system combining panoramic vision and visual attention. the second part presents a series of indoor localization experiments using panoramic vision and attention guided feature detection. the results show the feasibility of the approach and illustrate some of its capabilities.
scene classification from dense disparity maps in indoor environments. we present our approach for scene classification in dense disparity maps from a binocular stereo system. the classification result is used for tracking and navigation purposes. the presented system is capable of foreground-background separation classifying room structures. the 3d model of the scene is derived directly from the disparity image. this approach is used for initial target selection and scene classification in mobile navigation. it is used on our mobile system for target tracking, but can also be used for localization as described in this paper.we describe the basic principles of our object detection and classification using disparity information from a binocular stereo system. the theoretical derivation is supported by results from the binocular stereo sensor system on our mobile robot.
extraction and clustering of motion trajectories in video. a system is described that tracks moving objects in a video dataset so as to extract a representation of the objects' 3d trajectories. the system then finds hierarchical clusters of similar trajectories in the video dataset. objects' motion trajectories are extracted via an ekf formulation that provides each object's 3d trajectory up to a constant factor. to increase accuracy when occlusions occur, multiple tracking hypotheses are followed. for trajectory-based clustering and retrieval, a modified version of edit distance, called longest common subsequence is employed. similarities are computed between projections of trajectories on coordinate axes. trajectories are grouped based, using an agglomerative clustering algorithm. to check the validity of the approach, experiments using real data were performed.
omnidirectional stereo vision with a hiperbolic double lobed mirror. this paper presents a compact panoramic stereo vision system based on a double lobed mirror with hyperbolic profile. as hyperbolic mirrors ensure a single viewpoint the incident light rays are easily found from the points of the image. the geometry of the double lobed mirror naturally ensures matched epipolar lines in the two images of the scene. these two properties make the double lobed mirror especially suitable for panoramic stereo vision because range estimation becomes simple and fast. this is a great advantage for real-time applications. the proposed mirror is useful for mobile robot navigation, surveillance, and machine vision where fast and real-time calculations are needed.
a unified formulation of invariant point pattern matching. we present a unified framework for modeling and solving invariant point pattern matching problems. invariant features are encoded as potentials in a probabilistic graphical model. by using a specific kind of graph topology, different types of invariant matching models can be implemented via tree-width selection. models with tree-widths 1, 2, 3 and 4 implement translation, similarity, affine and projective invariant point matching, respectively. the optimal match is then found by exploiting the markov structure of the graph through the generalized distributive law in a dynamic programming setting. in the absence of noise in the point coordinates, the solutions found are optimal. our early experiments suggest the approach is robust to outliers and moderate noise.
a comparison of junction tree and relaxation algorithms for point matching using different distance metrics. we have developed a polynomial time optimal method for a class of attributed graph matching problems using the junction tree algorithm from graphical models.in this paper we compare this method with standard probabilistic relaxation labelling using different forms of point metrics and under different levels of additive noise.results show that, no matter which of the metrics is applied, our technique is more effective than probabilistic relaxation labeling for large graph sizes.for small graph sizes, our technique is still preferable for two of the metrics, while for the third one both techniques perform similarly.
context enhancement of nighttime surveillance by image fusion. in this paper, we propose a novel method of automatically combining images of a scene at different time intervals by image fusion. all the important information of the original low quality nighttime images is combined with the context from a high quality image of the daytime at the same viewpoint. the fused image contains a comprehensive description of the scene which is more useful for human visual and machine perception. experimental results show that the proposed method is robust and effective.
an efficient radical-based algorithm for stroke-order-free online kanji character recognition. this paper investigates improvements of an online handwriting stroke-order analysis algorithm - cube search, based on cube graph stroke-order generation model and dynamic programming (dp). by dividing character into radicals, the model is decomposed into intra-radical graphs and an inter-radical graph. this decomposition considerably reduces the time complexity of stroke-order search dp. experimental results showed an significant improvements in operational speed. additionally, recognition accuracy was also improved by prohibiting unnatural stroke-order.
offline cursive character challenge: a new benchmark for machine learning and pattern recognition algorithms.. cursive character recognition is a challenging task due to high variability and intrinsic ambiguity of cursive letters. this paper presents c-cube (cursive character challenge), a new public-domain cursive character database. ccube contains 57293 cursive characters manually extracted from cursive handwritten words, including both upper and lower case versions of each letter. the database can be downoloaded from the web and it provides predefined experimental protocols in order to compare rigorously the results obtained by different researchers.
recognition of free-form objects in dense range data using local features. this article describes a system for recognizing free-form 3d objects in dense range data employing local features and object-centered geometric models. local features are extracted from range images and object models using curvature analysis, and variability in feature size is accommodated by decomposition of features into sub-features. shape indices and other attributes provide a basis for correspondence between compatible image and model features and subfeatures, as well as pruning of invalid correcpondences. a verification step provides a final ranking of object identity and pose hypotheses. the evaluation system contained 10 free-form objects and was tested using 10 range images with two objects from the database in each image. comments address strengths of the proposed technique as well as areas for future improvement.
experiments in transform-based range image compression. range images (depth maps) are seeing increased usage in a variety of application areas including entertainment, industrial automation, inspection, remote sensing, and military tactical planning. as the corpus of range imagery increases in size and the need to communicate such images over fixed-bandwidth channels increases, the compression of range data deservesinvestigation. since the geometry encoded by range sensors is inherently "low-bandwidth", transform-based techniques seem appropriate for investigation in this context. this paper reports on experiments with a popular zerotree-based image codec (the spiht algorithm developed by said and pearlman) and its application to the compression of range imagery. experiments suggest that compression rates of 1 bit/pixel and below are achievable with minimal impact on fidelity.
reliable and fast eye finding in close-up images. this paper describes a method for quickly and robustly localizing the iris and pupil boundaries of a human eye in close-up images. such an algorithm can be critical for iris identification, or for applications that must determine the subject's gaze direction, e.g., human-computer interaction or driver attentiveness determination. a multiresolution coarse-to-fine search approach is used, seeking to maximize gradient strengths and uniformities measured across rays radiating from a candidate iris or pupil's central point. an empirical evaluation of 670 eye images, both with and without glasses, resulted in a 98% localization accuracy. the algorithm has also shown robustness to weak illumination and most specular reflections (e.g., at eyewear and cornea), simplifying system component requirements. rapid execution is achieved on a 750 mhz desktop processor.
challenges for data mining in distributed sensor networks. the way of collecting sensor data will face a revolution when the newly developing technology of distributed sensor networks becomes fully functional and widely available. smart sensors will acquire full interconnection capabilities with similar devices, so that run-time data aggregation, parallel computing, and distributed hypothesis formation will become reality with off-the-sheif components and sensor boards. this revolution started around ten years ago, and now hardware and network are converging on the jirst convincing solutions. exploring and exploiting this paradigm are a renovated challenge for the pattern recognition and data mining community. this paper attempts a survey on state-of-the-art of wireless sensor technology, with an eye on data-related problems and technological limits. although the possibilities seem promising, the today limited computational resources of individual nodes hamper the elaboration of data with recent, computationallyintensive algorithms. new software paradigms must be developed, both creating new techniques or adapting, for network computing, old algorithms of earlier ages of computing.
most minutiae-based matching algorithms confront. most minutiae-based matching algorithms confront with the challenge of missing and fake minutiae, and especially the non-linear distortion. many efforts have been made to cope with these problems while relatively slow improvements achieved. in this paper, we proposed a novel minutiae-based matching scheme which introduced a concept of compatibility to the minutiae triangle structures. and based on the compatibility, we further adopted a relaxation process to adjust the similarity matrix of the minutiae triangle cells between the query and template images. to reduce the effect of non-linear distortion evidently, an extended searching step independent of any linear models was proposed. results obtained on the fvc2004 b1_a show that the proposed algorithm overcomes the influence of missing and fake minutiae, and meanwhile decreases the time cost of matching saliently.
simple calibration without metric information using an isoceles trapezoid. this paper addresses the problem of calibrating a pin-hole camera from images of an isoceles trapezoid. assuming a unit aspect ratio and zero skew, we introduce a novel and simple camera calibration approach. the key features of the proposed technique are its simplicity and the lack of need for 3d coordinate information about the calibrating object - i.e. the isosceles trapezoid. by ultilizing the symmetry of such trapezoid, we show that one can obtain both the internal and the external camera parameters. to demonstrate the effectiveness of the algorithm, we present the processing results on synthetic and real images, and compare our results to zhang's flexible calibration method.
automatic morphological detection of otolith nucleus. this paper deals with the analysis of otolith images for fish ageing issues. we present a new and well-founded approach for the automatic detection of the nucleus within otolith images. this topic is of key interest since the knowledge of the location of the otolith nucleus is required to achieve further processing (2d ring segmentation, age and growth estimation,...). using morphological features within an a contrario framework, we develop a robust parameterless scheme. its efficiency is demonstrated by an evaluation carried out for a set of several hundred of plaice otoliths.
synthesizing reflections of inserted objects. the aim of reflection synthesis of inserted objects is to generate reflections which would be seen by the same camera capturing the target scene and be reflected by the true reflective media in the target scene. this problem is inherently difficult because we are typically given only several, sometimes even just one, views of the target scene. in this paper, we explore the geometric constraints to synthesize geometrically correct reflections. we also demonstrate how to constraint the synthesized reflections to be photometrically consistent with those in the original target views. the proposed method, therefore, advances the current imagebased compositing techniques one step further toward the scenes with variations in both lighting conditions and viewpoints. we demonstrate our approach for real scenes.
nonlinear manifold clustering by dimensionality. because of variable dependence, high dimensional data typically have much lower intrinsic dimensionality than the number of its variables. hence high dimensional data can be expected to lie in (nonlinear) lower dimensional manifold. in this paper, we describe a nonlinear manifold clustering algorithm. by connecting data vectors with their neighbors in feature space, we construct a neighborhood graph from given set data vectors. furthermore, geometrical invariance, namely dimensionality, are extracted from the neighborhood of vectors, and used to facilitate the clustering procedure. in addition, we discuss a latent model for data cluster descriptions and an em algorithm to find such descriptions. preliminary experiments illustrate that this new algorithm can be used to explore the nonlinear structure of data.
online structure based chinese character pre-classification. in this paper, an online structural recognition (olsr) system for chinese characters is proposed. a pre-classification scheme based on this olsr is applied to improve the performance of a pure statistical pattern matching (spm) based chinese character recognition engine. the experimental result shows that the pre-classifier not only reduces the overall recognition time but also improves the online recognition accuracy
new rht-based ellipsoid recovery method. a new method that enables randomized hough transform (rht)-based recovery of ellipsoid parameters from a collection of 3d points is presented. the approach is attractive since it can alleviate the traditional hough transform's disadvantages of large computation time and memory usagein particular for the ellipsoid detection's highdimensional parameter space. the new method uses two rht-based stages, first exploiting shape to enable recovery of position and then exploiting eigen analysis to recover the remaining parameters (of shape and orientation). experimental results on synthetic and real data are also presented.