waashk/dblp huggingface dataset:
region segmentation and matching in stereo images. in this paper, we propose a new method to simultaneously achieve segmentation and dense matching in a pair of stereo images. in constrast to conventional methods that are based on similarity or correlation techniques, this method is based on geometry and uses correlations only on a limited number of key points. stemming from the observation that our environment is abundant in planes, this method focuses on segmentation and matching of planes in an observed scene. neither prior knowledge about the scene nor camera calibration are needed. using two uncalibrated images as inputs, the method starts with a rough identification of a potential plane, defined by three points only. based on these three points, a plane homography is then calculated and, used for validation. starting from a seed region defined by the original three points, the method grows the current region by successive move/confirmation steps until occlusions and/or surface discontinuity occur. in this case, the homography-based mapping of points between the two images will fail. this failure is detected by the correlation, used in the confirmation process. in particular, this method grows a region even across different colors as long as the region is planar. experiments on real images validated our method and showed its capability and performance.
gaussian energy functions for registration without correspondences. a new criterion based on gaussian fields is introduced and applied to the task of automatic rigid registration of point-sets. the method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. we show that the size of the region of convergence can be extended so that no close initialization is needed, thus overcoming local convergence problems of iterative closest point algorithms. furthermore, the gaussian energy function can be evaluated with linear complexity using the fast gauss transform, which permits efficient implementation of the registration algorithm. analysis through several experimental results on real world datasets shows the practicality and points out the limits of the approach.
a k-means-based algorithm for projective clustering. in this paper, a new algorithm for projective clustering is proposed. the algorithm consists of two phases. the first phase performs attribute relevance analysis by detecting dense regions in each attribute, thereby allowing irrelevant attributes and outliers to be captured and eliminated. starting from the results of the first phase, the second phase aims to uncover clusters in different subspaces. the clustering process is based on the k-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense.
a powreful finite mixture model based on the generalized dirichlet distribution: unsupervised learning and applications. this paper presents a new finite mixture model based on a generalization of the dirichlet distribution. for the estimation of the parameters of this mixture we use a gem (generalized expectation maximization) algorithm based on a newton-raphson step. the experimental results involve the comparison of the performance of gaussian and generalized dirichlet mixtures in the classification of several pattern-recognition data sets.
application of rigid motion geometry to film restoration. film restoration involves locating the position of artifacts and replacing the "missing" portion of the film (obscured by the artifact) with pixels that had been lost. computer vision research has recently developed many techniques for constraining and predicting parts of a scene based upon the assumption of rigid motion. in this paper, we show how the constraints can help identify artifacts as well as how the prediction can be used to replace the artifact with natural looking portions of the scene. these techniques can be superior, when the rigid motion assumption is valid, to other techniques for film restoration.
content based image retrieval using gradient color fields. this paper presents a content based system retrieval that uses gradient color fields as features. these features take into account both contour curvature and colors found in adjacent regions. this approach is dual from color region based methods. it allows a simple description of images using a coarse understandable sketch, which preserves rich information with a small amount of storage. this representation allows different type of queries such as: statistical or structural, global or partial and contour based. these features have been tested, using different modes on a very large image database from broadcast television. results obtained from our system is presented and discussed.
coarse visual registration from closed-contour neighborhood descriptor. this article introduces an innovative visual coarseregistration process suitable for textureless objects. because our framework is industrial, the process is designed for metallic, complex objects containing multiple bores and repetitive patterns. this technique is based on a local shape descriptor, invariant under affine transform, which characterizes the neighborhood of a closed contour. the affine invariance is exploited in the learning stage to produce a lightweight model: for an automobile cylinder head, a learning viewsphere with twelve viewpoints is sufficient. moreover, during the learning stage, this descriptor is combined to a 2d/3d pattern, concept likewise presented in this article. once associated, the 2d/3d information wealth of this descriptor allows a pose estimation from a single match between two descriptors. this ability is exploited to obtain efficiently a great number of coarse pose hypothesis. a pose hypothesis classification method is proposed to select the best-ones. an evaluation on a cylinder head and a binding beam confirms both the robustness and the precision of the process.
face verification system architecture using smart cards. a smart card based face verification system is proposed in which the feature extraction and decision making is performed on the card. such an architecture has many privacy and security benefits. as smart cards are limited computational platforms, the face verification algorithms have to be adapted to limit the facial image representations. this minimises the information needed to be sent to the card and lessens the computational load of the template matching. studies performed on the banca and xm2vts databases demonstrate that by limiting these representations the verification performance of the system is not degraded and that the proposed architecture is a viable one.
incorporating temporal context with content for classifying image collections. semantic scene classification is an open problem in image understanding, especially when information purely from image content (i.e., pixels) is employed. however, in applications involving image collections, surrounding images give each image a temporal context. we present a probabilistic approach to scene classification, capable of integrating both image content and temporal context. elapsed time between images can be derived from the timestamps recorded by digital cameras. our temporal context model is trained to exploit the stronger dependence between images captured within a short period of time, indicated by the elapsed time. we demonstrate the efficacy of our approach by applying it to the problem of indoor-outdoor scene classification and achieving significant gains in accuracy. the probabilistic temporal context model can be applied to other scene classification problems.
photo classification by integrating image content and camera metadata. despite years of research, semantic classification of unconstrained photos is still an open problem. existing systems have only used features derived from the image content. however, exif metadata recorded by the camera provides cues independent of the scene content that can be exploited to improve classification accuracy. using the problem of indoor-outdoor classification as an example, analysis of metadata statistics for each class revealed that exposure time, flash use, and subject distance are salient cues. we use a bayesian network to integrate heterogeneous (content-based and metadata) cues in a robust fashion. based on extensive experimental results, we make two observations: (1) adding metadata to content-based cues gives highest accuracies; and (2) metadata cues alone can outperform content-based cues alone for certain applications, leading to a system with high performance, yet requiring very little computational overhead. the benefit of incorporating metadata cues can be expected to generalize to other scene classification problems.
fuzzy point of view combination for contextual shape recognition: application to on-line graphic gesture recognition. in this paper, we focus on the explicit use of the spatial context of strokes in the recognition process of graphic gestures. we dispose of two sources of knowledge on the samples: their shape which is a classical one and their spatial context. the proposed method is based on three different points of view to exploit optimally these two sources of knowledge to perform the recognition. the first point of view uses the spatial context to filter the possible classes and then uses the shape to discriminate the remaining classes. the second one reverses the roles of the sources of knowledge. the third one uses them jointly. the underlying idea is that each point of view suits to one of the three case of source reliability. the challenge is to automatically compose the points of view and to combine them to build a system performing contextual shape recognition without any prior information on the targeted domain.
a survey of approaches to three-dimensional face recognition. the vast majority of face recognition research has focused on the use of two-dimensional intensity images, and is covered in existing survey papers. this survey focuses on face recognition using three-dimensional data, either alone or in combination with two-dimensional intensity images. challenges involved in developing more accurate three-dimensional face recognition are identified.
perceptual organization in range data: robust detection of low order surfaces in heavy clutter. we consider the problem of detecting manmade objects in range data in the presence of extensive clutter. such situations arise in, for example, the detection of small structures or vehicles beneath a leaf canopy in range data collected from an airborne platform. this problem calls for an extremely robust detection scheme, and it should be fast. since most manufactured objects comprise large low-order piecewise smooth surfaces (often planes), we focus on detecting locally planar surfaces. we propose a novel technique we call distribution weighted histograms (dwh), which exploits the inherent geometric distributions of man-made objects versus the random occlusions such as those due to an overhanging leaf canopy. the dwh algorithm performs well under heavy occlusion while being computationally inexpensive (linear complexity). we present extensive experimental results.
multiscale surface organization and description for free form bject recognition. we introduce an efficient, robust means to obtain reliable surface descriptions, suitable for free form object recognition, at multiple scales from range data. mean and gaussian curvatures are use to segment the surface into four saliency classes of based on curvature consistency as evaluated in a robust multivoting scheme. contiguous regions consistent in both mean and gaussian curvature are identified as the most homogeneous segments, followed by those consistent in mean curvature but not gaussian curvature, followed by those consistent in gaussian curvature only. segments at each level of the hierarchy are extracted in the order of size, large to small, such that the most salient features of the surface are recovered first. this has potential for efficient object recognition by stopping once a just sufficient description is extracted.
sample size estimation using the receiver operating characteristic curve. in this paper we describe two related approaches to estimating the sample sizes required to statistically compare the performance of two classifiers: acceptable failure rates (afr) and the area under the receiver operating characteristic (roc) curve (auc). in particular, we consider rare event detection problems, where the prior class probabilities are highly skewed, and measure performance at a specific operating point and for the whole roc curve. it is shown that the use of auc as a performance measure is preferable to afr as it requires a smaller data set to demonstrate superiority of one classifier over another.
color image coding based on embedded wavelet zerotree and scalar quantization. with the continuous expansion of multimedia applications and the blossoming demand for efficiently storing and transmitting visual information, the needs and requirements of image compression have became increasingly vital. to address this need in the specific area of still image encoding, a new standard jpeg2000 is being designed. our paper deals with color image coding using discrete wavelets transform, color embedded zerotree wavelet (cezw) and combining scalar quantification and adaptive huffman coding. the results are compared to jpeg2000 performances using the java implementation.
comparing normalization and adaptation techniques for on-line handwriting recognition. in this paper a writer-independent on-line handwriting recognition system is described comparing the influence of handwriting normalization and adaptation techniques on the recognition performance. our hidden markov model (hmm) - based recognition system for unconstrained german script can be adapted to the writing style of a new writer using different adaptation techniques whereas the impact of preprocessing to normalize the pen-trajectory is examined. the performance of the resulting writer-dependent system increases significantly, even if only a few words are available for adaptation. so this approach is also applicable for on-line systems in hand-held computers such as pdas. in addition, the developed normalization techniques are helpful to improve completely writer independent systems. this paper presents the performance comparison of three different adaptation techniques either in a supervised or an unsupervised mode, in combination with appropriate normalization methods, with the availability of different amounts of adaptation dta ranging from only 6 words up to 100 words per writer.
cast shadow removing in foreground segmentation. in this paper, we focus on the problem of foreground segmentation in outdoor environment with a static tv camera. our application context is the visual surveillance of archeological sites. in this context the main aim is to detect the presence of people and to recognize their gestures in order to individuate the illegal actions. in this paper we concentrate solely on the primary step of moving object detection. in particular, the system should be able of recovering the true shape of the moving objects in order to allow to a classifier to discriminate a people from any other moving object as car and animals. moreover, the system should not be sensitive to changes in lighting, weather, number of people, etc., and it is required to work autonomously for long periods of time. a main problem in analyzing real outdoor daylight scenes is to deal with shadows cast by moving objects such as vehicles or pedestrians. cast shadows are often detected as a part of the moving objects since they move in the same way. when the detected objects contain shadows, large errors may occur with respect their recognition. in this paper a new approach for cast shadow removing is proposed. our idea is to detect shadow points as points that are static for a short temporal sequence and that are characterized by a photometric gain, with respect the reference background image, that is lower than unit and that we estimate for each new image through an optimization approach.
robust factorisation with uncertainty ana. this paper proposes how the classic factorisation algorithm for affine reconstruction can be extended to be robust to outliers and proposes how the uncertainty analysis can be performed in this case. the robust estimation approach elaborated here is based on the iteratively reweighted least-squares but the use of other robust methods is also discussed. moreover, the uncertainty analysis presented in this paper could be similarly used in a ransac or lmeds extension of the factorisation algorithm. the experiments verify that the proposed approach is reliable and able to give to consistent estimates and uncertainty measure for affine structure and motion.
robust alignment of transmission electron microscope tilt series. in this paper, we propose a novel method for automatic, feature-based alignment of transmission electron microscope images that is needed for computing 3d reconstructions in electron tomography. the proposed method, termed as trifocal alignment, is more accurate than the previous markerless methods. the key components of this work are (1) a reliable multiresolution algorithm for matching feature points between images, (2) a robust, maximumlikelihood- based estimator for determining the trifocal constraint, needed for validating the correctness of the matches, (3) a robust, large scale optimisation framework to compute the alignment parameters from hundreds of thousands of feature point measurements from a couple of hundred images. the experiments show for the first time that by the proposed feature-based alignment approach the accuracy level of the fiducial marker alignment can be achieved.
recognition of gestures in the context of speech. the scope of this paper is the interpretation of a user's intention via a video camera and a speech recognizer. in comparison to previous work which only takes into account gesture recognition, we demonstrate that by including speech, system comprehension increases. for the gesture recognition, the user must wear a colored glove, then we extract the velocity of the center of gravity of the hand. a hidden markov model (hmm) is learned for each gesturethat we want to recognize. in a dynamic action, to know if a gesture has been performed or not, we implement a threshold model below which the gesture is not detected. the off line tests for gesture recognition have a success rate exceeding 85% for each gesture. the combination of speech and gestures is realized using bayesian theory.
automatic acquisition of context models and its application to video surveillance. this paper addresses the problem of automatically acquiring context models from data. context and human behavior are represented using a state model, called situation model. this model consists of different layers referring to entities, filters, roles, relations, situation and situation relationship. we propose a framework for the automatic acquisition of these different layers. in particular, this paper proposes a novel generic situation acquisition algorithm. the algorithm is also successfully applied to a video surveillance task and is evaluated by the public caviar video database. the results are encouraging.
gmm-based svm for face recognition. a new face recognition algorithm is presented. it supposes that a video sequence of a person is available both at enrollment and test time. during enrollment, a client gaussian mixture model (gmm) is adapted from a world gmm using eigenface features extracted from each frame of the video. then, a support vector machine (svm) is used to find a decision border between the client gmm and pseudoimpostors gmms. at test time, a gmm is adapted from the test video and a decision is taken using the previously learned client svm. this algorithm brings a 3.5% equal error rate (eer) improvement over the biosecure reference system on the pooled protocol of the banca database.
unsupervised clustering of text entities in heterogeneous grey level documents. this paper presents a new method of functional classification of text blocks on a document. it is based on texture analysis and unsupervised classification. texture is used here to define different classes of text blocks in the document and to direct a possible way of exploration from the most eye-catching data to the less significant text block. the typographicai properties of blocks are characterized by two main discriminating primitives: the complexity of the text draw ing and the structural relief of the block. this analysis is the starting point of ahree-classes categorization into functional families (main headings, sub-headings and text paragraphs). each block of text is described and classified through a labeling process based on a 3d-feature space using the two previous features (complexity and structural relief) and athird one among pattern primitives, blocks size and location in the document. this method allows a first approach to a global context-free classification of documents.
recognition of building roof facets by merging aerial images and 3d lidar data in a hierarchical segmentation framework. we investigate in this paper an original methodology for detecting roof facets through the fusion of aerial images and lidar data (3d point cloud). based on a hierarchical segmentation of the image, we define a cost function that manages the merging order of regions. it depends on both radiometric similarities of two neighbouring regions as well as on extracted information from lidar data. considering that lidar data have been filtered into points belonging either to ground or non-ground classes, we define semantic and geometric rules in the binary merging process. building roof facets are finally detected by selecting a level of generallity for representing roof building components. some remarks are given concerning the reliability of the integration of lidar and image data. reconstructed roof facets are finally shown onto complex buildings.
paper to pda. a system is described for the automatic analysis of a document image into atomic fragments (e.g. word images) that can be reconstructed or "reflowed" onto a display device of arbitrary size, depth, and aspect ratio. the main intent is to allow scans and other page-image documents to be viewed effectively on a limited-resolution hand-held computing device, without any errors and losses due to ocr and retype-setting. the methods of image analysis and representation are described.
fusion of frequency and spatial domain information for motion analysis. this paper presents an approach to the analysis of multiple motions in video, which combines frequency and spatial domain information in a new manner. the tasks of interest are finding the number of moving objects, velocity estimation, object tracking, and motion segmentation. we propose a novel, hybrid approach, which avoids problems of spatial domain methods, like sensitivity to illumination changes or great computational cost, but also uses spatial information for precise object localization. experiments with synthetic and real sequences, show both the possibilities and limitations of this approach.
spatial and fourier error minimization for motion estimation and segmentation. we present a new approach to motion estimation by minimizing the squared error in both the spatial and frequency domains and we show that the spatially global nature of ft leads to a motion estimation error that is much lower than that obtained via spatial motion estimation. on the other hand, spatial analysis is useful for accurate segmentation. we describe a novel, hybrid approach combining the above two estimates of motion and segmentation. we examine the robustness of minimizing the error terms in both domains, both theoretically and experimentally. experiments with real and synthetic sequences demonstrate the capabilities of the proposed algorithm.