waashk/dblp huggingface dataset:
shape metamorphism using p-laplacian equation. we present a new approach for shape metamorphism, which is a process of gradually changing a source shape (known) through intermediate shapes (unknown) into a target shape (known).the problem, when represented with implicit scalar function, is under-constrained, and regularization is needed.using the p-laplacian equation (ple), we generalize a series of regularization terms based on the gradient of the implicit function, and we show that the present methods lack additional constraints for a more stable solution. the novelty of our approach is in the deployment of a new regularization term when p ¿ ¿ which leads to the infinite laplacian equation (ile).we show that ile minimizes the supremum of the gradient and prove that it is optimal for metamorphism since intermediate solutions are equally distributed along their normal direction.application of the proposed algorithm for 2d and 3d objects are demonstrated.
transformation invariance in hand shape recognition. in hand shape recognition, transformation invariance is key for successful recognition. we propose a system that is invariant to small scale, translation and shape variations. this is achieved by using a-priori knowledge to create a transformation subspace for each hand shape. transformation subspaces are created by performing principal component analysis (pca) on images produced using computer animation. a method to increase the efficiency of the system is outlined. this is achieved using a technique of grouping subspaces based on their origin and then organising them into a hierarchical decision tree. we compare the accuracy of this technique with that of the tangent distance technique and display the results.
a novel two-layer pca/mda scheme for hand posture recognition. principle component analysis (pca) and multiple discriminant analysis (mda) have long been used for the appearance-based hand posture recognition. in this paper, we propose a novel pca/mda scheme for hand posture recognition. unlike other pca/mda schemes, the pca layer acts as a crude classification. since posture alone cannot provide sufficient discriminating information, each input pattern will be given a likelihood of being in the nodes of pca layers, instead of a strict division. based on the expectation-maximization (em) algorithm, we introduce three methods to estimate the parameters for this crude classification during training. the experiments on a 110-sign vocabulary show a significant improvement compared with the global pca/mda.
a new method to compute the distortion vector field from two images. in this paper, we present a new method to compute the distortion vector field from two images. the aim of this study is to characterize the distortion between to images. several methods are based on optical flow techniques. however they are sensitive to image noise and grey-levelvariations between the two images. our method is based on distance transforms. images must be passed over twice before computing the distance transform and the distortion vector field simultaneously. pixels are characterized by the displacement from the nearest feature element (surface for grey-level images). 3x3x3 chamfer masks are used for grey-level images. the relative weights of spatial versus grey-level displacements are considered. experimental resultsare analyzed for both synthetic and natural images.
selecting canonical views for view-based 3-d object recognition. given a collection of sets of 2-d views of 3-d objects and a similarity measure between them, we present a method for summarizing the sets using a small subset called a bounded canonical set (bcs), whose members best represent the members of the original set. this means that members of the bcs are as dissimilar from each other as possible, while at the same time being as similar as possible to the non-bcs members. this paper will extend our earlier work on computing canonical sets [approximation of canonical sets and their application to 2d view simplification] in several ways: by omitting the need for a multi-objective optimization, by allowing the imposition of cardinality constraints, and by introducing a total similarity function. we evaluate the applicability of bcs to view selection in a view-based object recognition environment.
dissimilarity measures in color spaces. a problem of both theoretical and practical importance in image processing is to compare two color images, taking into account the eventual diversities due to translation, or color variations. the aim of this paper is to study the behavior of dissimilarity measures in different color spaces. five color spaces (rgb, hsi, hsv, cielab, cie-luv) are studied. properties of the dissimilarity measures are compared in terms of sensitivity to radiometric variations, spatial shifts and shape distortions. we compare the results with those obtained by subjective testing.
fast extraction of tubular and tree 3d surfaces with front propagation methods. we present a new fast approach for surface segmentation of thin structures, like vessels and vascular trees, based on fast marching and level sets methods. fast marching allows segmentation of tubular structures inflating a "long balloon" from a user given single point. however, when the tubular shape is rather long, the front propagation may blow up through the boundary of the desired shape close to the starting point. our contribution is focused on a way to go on front propagation only on the actually moving front and freezing other points. we demonstrate the ability to build a fast and accurate segmentation for those tubular and tree structures. we also develop a useful stopping criterion for the causal front propagation. we illustrate our algorithms by applications on the segmentation of vessels in 3d medical images.
homotopy-based estimation of depth cues in spatial domain. this paper presents a homotopy-based algorithm for a cooperative and simultaneous estimation of defocus blur and spatial shifts (2d motion, stereo disparities and/or zooming disparities) in the spatial domain. these cues are estimated from two images of the same scene acquired by a camera evolving in time and/or space and for which the intrinsic parameters are known. we show that these depth cues can be directly computed by resolving a system of equations is embedded in a family of systems using a homotopy method. the results confirm that the use of homotopies reduces approximation errors and thus leads to a denser and more accurate estimation of depth cues.
detecting rotational symmetry under affine projection. a new method is presented for detecting planar rotational symmetry under affine projection. the method can deal with partial occlusion and is able to detect multiple rotationally symmetric surfaces in complex backgrounds. the order of rotational symmetry can be estimated, and if it is greater than two, the tilt and orientation of the rotationally symmetric surface can be found and the symmetric region segmented. local features robust to local affine distortion are matched to obtain pairs of features. each feature pair hypothesises a set of centres of rotation for different tilts and orientations and the centres of rotation that are close to each other are grouped together to find the dominant rotational symmetries in the image. the method is posed independently of a specific feature detector and descriptor. results are presented on natural images.
classification error rate for quantitative evaluation of content-based image retrieval systems. a major problem in the field of content-based image retrieval is the lack of a common performance measure which allows the researcher to compare different image retrieval systems in a quantitative and objective manner. we analyze different proposed performance evaluation measures, select an appropriate one, and give quantitative results for four different, freely available image retrieval tasks using combinations of features. this work gives a concrete starting point for the comparison of content-based image retrieval systems. an appropriate performance measure and a set of databases are proposed and results for different retrieval methods are given.
automatic visual recognition of armed robbery. we propose a method by which to analyze silhouettes and recognize a classic holdup position of armed robbery. in such a situation, one actor levels his or her arm while another actor raises his or her arm(s) into the air. the core of this algorithm is skeleton analysis. we attempt recognition by fir st segmenting the skeleton of the silhouette into separate pieces of the body, then identifying the positions of the arms. we show that our algorithm correctly utilizes skeletons to identify parts of the human body and recognize these holdup positions.
analysis of abnormality in endoscopic images using combined hsi color space and watershed segmentation. in this paper, a method for detecting possible presence of abnormality in the endoscopic images is presented. the pre-processed endoscopic color images are segmented in the hsi color space. the pixels in the input color image corresponding to the segmented image are extracted for further processing. this image is smoothened using average filter and converted into grayscale image. its inverse transform is obtained for further processing and extended minima is imposed on the processed image using morphological reconstruction. then the morphological watershed segmentation is carried out on this image and the number of regions is counted and is compared with the threshold value. if the number of regions is more than the threshold value, then the output image is an indicative of possible presence of abnormality in the image.
on the performance of wavelets for handwritten numerals recognition. this paper validates a recognition system using bi-dimensional wavelet transforms as feature extractor and investigates the relevance of each subband image in the recognition process. an experiment to verify the efficiency of the wavelets was performed omitting the feature extraction step. results show that information about the relevant image features are evenly distributed in all subband images of wavelet coefficients and that wavelets are promising feature extractors. numerals from the nist database were used for evaluation of the systems tested.
skew detection in binary image documents based on image dilation and region labeling approach. in this paper we propose a new and efficient method for estimation of skew angle in a binary document image, based on image dilation and region labeling technique. the input document is dilated by using structuring element as line whose length is fixed experimentally and the region labeling technique is applied using depth first search. orientation angle is calculated for all the labeled regions, the average of all orientation angles is considered as the skew angle of the document. the experimental results show that better accuracy of estimation could be achieved using this approach since it is based on orientation angles of all the text lines of the underlying document and is the minimum variance unbiased estimator of the true skew angle. the novelty of the proposed method is that, it is robust for machine printed document of any size/font, multi-column layouts and documents containing graphics, pictures, charts, tables etc.
diagnosis and treatment of film tear in degraded archived media. a common form of degradation in archived film is film tear. this is caused by the physical ripping of the film. tear causes displacement of a region of the degraded frame and the loss of image data. as of yet no method of automatically treating film tear has been proposed. this paper outlines an algorithm to automatically detect and restore torn frames. tear detection is facilitated by the presence of a large edge feature, unlikely to be caused by other forms of degradation, in the torn frame. restoration is achieved by estimating the regional displacement and recovering missing image data.
script identification based on morphological reconstruction in document images. in this paper, the study of script identification based on morphological reconstruction for printed document images is carried out. the system is developed by using 609-scanned document images representing english, hindi, kannada, and urdu scripts. the system developed includes a feature extractor and a classifier. the feature extractor consists of two stages. in the first stage, the morphological erosion and opening by reconstruction is carried out on a document image in horizontal, vertical, right and left diagonal directions using the line structuring element. the length of the structuring element is fixed, based on the average height of all the connected components of an image. in the next stage, average pixel distribution is found in these resulting images. a nearest neighbor analysis is used to classify the new documents. accuracy of classification averaged 97% across the four scripts. the method shows robustness with respect to noise, font sizes and styles.
image segmentation through energy minimization based subspace fusion. in this paper we present an image segmentation technique that fuses contributions from multiple feature subspaces using an energy minimization approach. for each subspace, we compute a per-pixel quality measure and perform a partitioning through the standard normalized cut algorithm [normalized cuts and image segmentation]. to fuse the subspaces into a final segmentation, we compute a subspace label for every pixel. the labeling is computed through the graph-cut energy minimization framework proposed by [fast approximate energy minimization via graph cuts]. finally, we combine the initial subspace segmentation with the subspace labels obtained from the energy minimization to yield the final segmentation. we have implemented the algorithm and provide results for both synthetic and real images.
automatic adjustment of discriminant adaptive nearest neighbor. k-nearest neighbors relies on the definition of a global metric. in contrast, discriminant adaptive nearest neighbor (dann) computes a different metric at each query point based on a local linear discriminant analysis. in this paper, we propose a technique to automatically adjust the hyper-parameters in dann by the optimization of two quality criteria. the first one measures the quality of discrimination, while the second one maximizes the local class homogeneity. we use a bayesian formulation to prevent overfitting.
video motion estimation using prediction based hybrid approach. motion estimation is important for compression of video sequences. in this paper a new block motion estimation algorithm proposed. in the proposed method, the motion vector of previous frame are used to predict the initial motion vector and the search direction. the extent of search region is determined by predicted motion vector and motion vectors of four casual neighbours in the current frame. results shows significant speed-up compared to existing ones.
speech driven facial animation using a hidden markov coarticulation model. we present a hierarchical image based facial model which is driven from speech. it incorporates a novel modelling and synthesis algorithm for learning and producing coarticulated mouth animation we demonstrate using the hierarchical model how animation of the entire face may be created solely from animations of the mouth and how colour may be reincorporated and reproduced compactly without explicitly being modelled. we then consider and evaluate methods of merging animations from several different facial areas for delivery of output.
hierarchical, generic to specific multi-class object recognition. early vision systems could perform specific object recognition reasonably well but did not fare as well on identifying natural object classes. recent advances have led to systems that can learn a representation for different object classes and achieve good generic object class recognition. however, these systems are generally unable to perform the fine distinctions required for specific object identification. it seems that the two approaches are in contrast with each other. we propose a system that addresses the problems of generic class recognition as well as specific object recognition in the same framework. our system also possesses the property of graceful degradation, i.e., if it is unable to recognize an object as a dog, it recognizes it at least as a quadruped and so on. we automatically learn a hierarchy of the classes from the training data, progressing from the most generic class labels to the most specific object labels. this hierarchy is used during recognition. one important benefit of the hierarchical organization of the classes is that the number of comparisons performed for every input does not increase linearly with the number of classes added.
kernel procrustes. in this work we introduce a new methodology to build a kernel matrix from a collection of kernels. the key idea is to build an unique kernel that eliminates spurious differences between kernels. we propose a method based on the procrustes problems that uses the alternating projections method to minimize a certain error measure. the resulting kernel will be used for classification purposes using support vector machines (svms). the proposed method has been successfully evaluated against alternative kernel combination techniques.
orientation difference statistics for texture description. this paper presents a new approach for the description of directional textures based on the study of their orientation and coherence fields. we apply the feature-based interaction maps to the characterization of an orientation field. the features are extracted from orientation spatial difference histograms. the representation of a feature, as a function of the spacing vector used to compute the spatial statistics, yields a 2-d interaction map which can be used to assess the structural layout of texture. orientation-based interaction maps are exercised on original and distorted brodatz textures. the results show the relevance and the robustness of the approach for the description of directional textures.
onset detection through maximal redundancy detection. we propose a criterion, called maximal redundancy', for onset detection in time series. the concept redundancy is adopted from information theory and indicates how well a signal locally can be explained by an underlying model. it is shown that a local maximum in the redundancy is a good indicator for an onset. it is proven that 'maximal redundancy' detection is a statistical asymptotically optimal detector for ar processes. it also accounts for potentially non-gaussian time series and non- gaussian innovations in the ar processes. several applications are shown where the new criterion has been successfully applied.
spectral characterization of orientation data along curvilinear structures. this paper lies within the scope of texture analysis. we focus on anisotropic textures and propose an approach to measure the ripple of texture patterns. it assumes that the patterns are represented by some characteristic curves called generatrices (i.e. edges, level curves). two steps are involved. the first one deals with the estimation of the orientation along the generatrices. the second step consists in computing the power spectrum of the generatrix orientation. the global texture ripple is captured by the ripple spectrum which is computed by cumulating the orientation spectra of all individual generatrix. the approach is exercised on synthetic textures and on composite material images.
a 'no panacea theorem' for multiple classifier combination. we introduce the 'no panacea theorem' for classifier combination in the two-classifier, two-class case. it states that if the combination function is continuous and diverse, there exists a situation in which the combination algorithm will always give very bad performance. thus, there is no optimal algorithm, suitable in all situations. from this theorem, we see that the probability density functions (pdf's) play an important role in the performance of combination algorithms, so studying the pdf's becomes the first step in finding a good algorithm.
towards shape from shading under realistic photographic conditions. this paper describes a new modeling of the shape from shading problem taking perspective projection into account, and proposes a method of resolution for the new equation. an application is proposed, which consists in correcting the defects of photographs of skew i.e., non flat, documents.
a global solution to the sfs problem using b-spline surface and simulated annealing. this paper restates the shape from shading problem regarding both surface modeling and optimization. we combine the use of a b-spline as 3d model for the scene surface and the use of stochastic optimization, through simulated annealing. the proposed method overcomes successfully the main difficulties usually encountered in shape from shading. experiments are presented on both synthetic and real images.
did the great masters use optical projections while painting? perspective comparison of paintings and photographs of renaissance chandeliers. recently it has been claimed that as early as 1420 some european artists constructed their paintings by optically projecting images onto their supports (canvas, oak panel, etc.) and then tracing or painting over these projections. because projected images obey the laws of perspective, a powerful test of this claim centers on analyzing the geometric accuracy of key renaissance paintings. this paper investigates new techniques for analyzing the perspective accuracy of paintings. notably, we focus on a portion of a painting central to the debate of the theory: the chandelier in jan van eyck's "portrait of arnolfini and his wife." despite the high level of visual realism of the painting, the technique proposed here highlights large geometric inaccuracies that are very hard to explain as arising from the optical projection route. the contribution of this paper is two fold: i) we present a projective geometry-based technique for detecting and measuring geometric inaccuracies in paintings, and ii) we demonstrate that in the arnolfini portrait the source of those inaccuracies lies in the imaging process, as opposed to the manufacturing of the actual chandelier. the results presented in this paper cast serious doubts on the validity of the claim that optical tools were employed in painting the arnolfini portrait.
on-line adaptive background modelling for audio surveillance. in this paper, we investigate the problem of automatic audio surveillance. this aspect of the surveillance, which extends the more investigated area of video surveillance, can be very informative to solve many problems in real situations. similarly to video surveillance, also in this case it is necessary to build a background (bg) model, so that it is immediate to discover foreground (fg) events. to this end, we first introduce the concepts of audio bg and fg in an automated surveillance scenario. subsequently, we propose a novel audio bg system able to build in real time an adaptive model of the audio scene bg, and to promptly detect unexpected fg auditory events. the method is based on the probabilistic modelling of the audio data stream using separate sets of adaptive gaussian mixture models, working on the audio frequency spectrum. this approach is also characterized by the use of only one microphone and on-line functioning, so that it can be directly used in real situations, also to support a video surveillance system. preliminary results show the effectiveness of the approach to discover different fg audio situations.