waashk/dblp huggingface dataset:
recognition of handwritten phrases as applied to street name images. a method for recognition of street name phrases collected from mail pieces is presented in this paper. some of the challenges posed by the problem are: (i) patron errors, (ii) non-standardized way of abbreviating names, and (iii) variable number of words in a street name image. a neural network has been designed to segment words in a phrase, a street name in this case, using distances between components and style of writing. the network learns the type of spacing (including size) that one should expect between different pairs of characters in handwritten text. experiments show perfect word segmentation performance at about 85% of cases. unlike conventional methods, where lexicon entries are expanded to take care of all variations of prefixes and suffixes, substring matching is attempted only between the main body of a lexicon entry and the word segments of an image. efforts to reduce computational complexity are successfully made by the sharing of character segmentation results between the segmentation and recognition phases. 83% phrase recognition accuracy is achieved on a test set.
content-based trademark retrieval system using visually salient features. an ever increasing number of registered trademarks has created greater demand for an automatic trademark retrieval system. we present a method for such a system based on the image content using shape features. zernike or pseudo-zernike moments of the image are employed as a feature set. to retrieve similar shapes, we take into account visually salient features that dominantly affect the global shape of the trademarks and ignore their minor detail. experimental results on a database of 3,000 trademark images demonstrate that the proposed method retrieves visually similar trademarks which agree well with human perception.
images as embedding maps and minimal surfaces: movies, color, and volumetric medical images. a general geometrical framework for image processing is presented. we consider intensity images as surfaces in the (x, i) space. the image is thereby a two dimensional surface in three dimensional space for gray level images. the new formulation unifies many classical schemes, algorithms, and measures via choices of parameters in "master" geometrical measure. more important, it is a simple and efficient tool for the design of natural schemes for image enhancement, segmentation, and scale space. here we give the basic motivation and apply the scheme to enhance images. we present the concept of an image as a surface in dimensions higher than the three dimensional intuitive space. this will help us handle movies, color, and volumetric medical images.
facial expression recognition and its degree estimatio. the purpose of this study is not only to recognize some kind of facial expressions which is associated with human emotion but also to estimate its degree. our method is based on the idea that facial expression recognition can be achieved by extracting a variation from expressionless face with considering face area as a whole pattern. for the purpose of extracting subtle changes in the face such as the degree of expressions, it is necessary to eliminate the individuality appearing in the facial image. using a elastic net model, a variation of facial expression is represented as motion vectors of the deformed net from a facial edge image. then, applying k-l expansion, the change of facial expression represented as the motion vectors of nodes is mapped into low dimensional eigen space, and estimation is achieved by projecting input images on to the emotion space. in this paper we have constructed three kinds of expression models: happiness, anger, surprise, and experimental results are evaluated.
interaction with on-screen objects using visual gesture recognition. this paper will review the design of a working system that visually recognizes hand gestures for the control of a window based user interface. after an overview of the system, it will explore one aspect of gestural interaction in depth, hand tracking, and what is needed for the user to be able to interact comfortably with on-screen objects. we describe how the location of the hand is mapped to a location on the screen, and how it is both necessary and possible to smooth the camera input using a non-linear physical model of the cursor. the performance of the system is examined, especially with respect to object selection. we show how a standard hci model of object selection (fitts' law) can be extended to model the selection performance of free-hand pointing.
non-linear operators in image restoration. we firstly present a variational approach such that during image restoration, edges detected in the original image are being preserved, and then we compare in a second part, the mathematical foundation of this method with respect to some of the well known methods recently proposed in the literature within the class of pde based algorithms (anisotropic diffusion, mean curvature motion, min/max flow technique,... ). the performance of our approach is carefully examined and compared to the classical methods. experimental results on synthetic and real images will illustrate the capabilities of all the studied approaches.
panoramic image acquisition. this paper is concerned with acquiring panoramic focused images using a small field of view video camera. when scene points are distributed over a range of distances from the sensor, obtaining a focused composite image involves focus computations and mechanically changing some sensor parameters (translation of sensor plane, panning of camera etc.) which can be time intensive. in this paper we present methods to optimize the image acquisition strategy in order to reduce redundancy. we show that panning a camera about a point f (focal length) in front of the camera eliminates redundancy. the non-frontal imaging camera (nicam) with tilted sensor plane has been previously introduced as a sensor that can acquire focused panoramic images. in this paper we also describe strategies for optimal selection of panning angle increments and sensor plane tilt for nicam. experimental results are presented for panoramic image acquisition using a regular camera as well as using nicam.
eigenfeatures for planar pose measurement of partially occluded objects. planar pose measurement from images is an important problem for automated assembly and inspection. in addition to accuracy and robustness, ease of use is very important for real world applications. recently, murase and nayar have presented the "parametric eigenspace " for object recognition and pose measurement based on training images. although their system is easy to use, it has potential problems with background clutter and partial occlusions. we present an algorithm that is robust in these terms. it uses several small features on the object rather than a monolithic template. these "eigenfeatures" are matched using a median statistic, giving the system robustness in the face of background clutter and partial occlusions. we demonstrate our algorithm's pose measurement accuracy with a controlled test, and we demonstrate its detection robustness on cluttered images with the objects of interest partially occluded.
object detection with vector quantized binary features. this paper presents a new algorithm for detecting objects in images, one of the fundamental tasks of computer vision. the algorithm extends the representational efficiency of eigenimage methods to binary features, which are less sensitive to illumination changes than gray-level values normally used with eigenimages. binary features (square subtemplates) are automatically chosen on each training image. using features rather than whole templates makes the algorithm more robust to background clutter and partial occlusions. instead of representing the features with real-valued eigenvector principle components, we use binary vector quantization to avoid floating point computations. the object is detected in the image using a simple geometric hash table and hough transform. on a test of 1000 images, the algorithm works on 99.3%. we present a theoretical analysis of the algorithm in terms of the receiver operating characteristic, which consists of the probabilities of detection and false alarm. we verify this analysis with the results of our 1000-image test and we use the analysis as a principled way to select some of the algorithm's important operating parameters.
novel active-vision-based visual-threat-cue for autonomous navigation tasks. this paper presents a new visual motion cue, we call the visual threat cue (vtc) that provides some measure for a relative change in range as well as clearance between a 3d surface and a fixing observer in motion. the vtc corresponds to visual fields surrounding a moving observer. the fields are time-based imaginary 3-d surfaces that move with the observer. they are analogous to equi-potential fields of an electric dipole. a practical method to extract the vtc is presented. the approach is independent of the 3d surface texture and needs no optical flow information, 3d reconstruction, segmentation, feature tracking or pre-processing. this algorithm to extract the vtc was applied to several indoor as well as outdoor real images of textures, where we observed a similar behavior for most of the textures employed.
an image-based visual-motion-cue for autonomous navigatio. this paper presents a novel time-based visual motion cue called the hybrid visual threat cue (hvtc) that provides some measure for a change in relative range as well as absolute clearances, between a 3d surface and a moving observer. it is shown that the hvtc is a linear combination of time-to-contact (ttc), visual looming and the visual threat cue (vtc). the visual field associated with the hvtc can be used to demarcate the regions around a moving observer into safe and danger zones of varying degree, which may be suitable for autonomous navigation tasks. the hvtc is independent of the 3d environment and needs almost no a-priori information about it. it is rotation independent, and is measured in ~time/sup -1/\ units several approaches to extract the hvtc, are suggested. also a practical method to extract it from a sequence of images of a 3d textured surface obtained by a visually fixating, fixed-focus monocular camera in motion is presented. this approach of extracting the hvtc is independent of the type of 3d surface texture and needs no optical flow information, 3d reconstruction, segmentation, feature tracking.
shape from the light field boundary. ray-based representations of shape have received little attention in computer vision. in this paper we show that the problem of recovering shape from silhouettes becomes considerably simplified if it is formulated as a reconstruction problem in the space of oriented rays that intersect the object. the method can be used with both calibrated and uncalibrated cameras, does not rely on point correspondences to compute shape, and does not impose restrictions on object topology or smoothness.
what is a light source? traditional light source modelling is concerned with specific types of light sources, the two most common of which are point sources and daylight. little attempt has been made, however, to relate different types of sources to each other. for example, how may the lighting from an overcast sky be compared to that from a lamp? having a theoretical framework to compare different types of light sources is important for computer vision, in particular for understanding shading and shadow cues. a vision system needs to take account of the light source in order to interpret these cues. in this paper, we present a framework for comparing types of light sources which is based on a dimensional analysis of the set of light rays in a free space. specifically, we introduce a 4-d light source hypercube in which the different types of sources may be embedded and compared. we also present a novel definition for light sources which generalizes the standard definition of a source as an emitter.
recovering the viewing parameters of random, translated and noisy projections of asymmetric objects. a method is described for the determination of the viewing parameters of randomly acquired projections of asymmetric objects. it extends upon the common lines algorithm by determining the relative orientation of projections from the location of lines of intersection among the fourier transforms of the projections in three-dimensional fourier space. a new technique for finding the lines of intersection in the presence of translational displacement, and for subsequently finding the translational displacement, is presented. a new technique for dealing with noise is also presented. the complete algorithm is described and its efficacy is demonstrated using real data. this technique may be applied to the three-dimensional reconstruction of viruses, molecules, and cells from in vivo images. it also has many other applications including the reconstruction of underwater scenes, radioastronomy, geoseismic analysis, and portable radiography for medical diagnosis and industrial inspection.
new, faster, more controlled fitting of implicit polynomial 2d curves and 3d surfaces to data . denote a point in the plane by z=(z,y) and a polynomial of nth degree in z by f(z) /spl sigma//sub i,j//spl ges/o/sub 1/i+j/spl les/n(a/sub ij/x/sup i/y/sup j/). denote by z(f) the set of points for which f(z)=0. z(f) is the 2d curve represented by f(z). in this paper, we present a new approach to fitting 2d curves to data in the plane (or 3d surfaces to range data) which has significant advantages over presently known methods. it requires considerably less computation and the resulting curve can be forced to lie close to the data set at prescribed points provided that there is an nth degree polynomial that can reasonably approximate the data. linear programming is used to do the fitting. the approach can incorporate a variety of distance measures and global geometric constraints.
using differential constraints to reconstruct complex surfaces from stereo. stereo reconstruction algorithms often fail to properly deal with complex surfaces, because there is not enough image information. to overcome this problem, we propose to guide the reconstruction process using a priori information about the differential geometry of the object surfaces. we use both linear structures such as crest lines or scalar fields such as curvature values to generate a reconstruction of the surface which is consistent with the differential properties. this method improves the accuracy of the reconstruction around the discontinuities and increases the compactness of the surface representation.
dealing with occlusions in the eigenspace approach. the basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. in this paper we present a new approach which successfully solves these problems. the major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. instead of computing the coefficients by a projection of the data onto the eigenimages, we extract them by a hypothesize-and-test paradigm using subsets of image points. competing hypotheses are then subject to a selection procedure based on the minimum description length principle. the approach enables us not only to reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.
on perpendicular texture: why do we see more flowers in the distance? almost all work on texture in the computer vision and graphics communities has modeled the texture as tangential, i.e. lying in the tangent plane to the surface. this is equivalent to thinking of the texture as a pattern painted on the surface. three-dimensional textures, where the elements may point out of the surface, have largely been ignored. we study a special class of 3d textures, perpendicular textures where we can model the elements as being normal to the surface. the perspective projection of perpendicularly textured surfaces results in several interesting phenomena, which do not occur in the much-studied tangential texture case. these include occlusion, foreshortening and illumination.in this paper, we study the geometry of the problem, modeling the locations of the elements of the texture as being a realization of a spatial point process. relations between slant and tilt of the surface, density and height of elements and occlusions are derived. occlusions can now be used as a cue to infer shape, instead of being treated as a source of error.
bayesian image restoration and segmentation by constrained optimizatio. abstract: a constrained optimization method, called the lagrange-hopfield (lh) method, is presented for solving markov random field (mrf) based bayesian image estimation problems for restoration and segmentation. the method combines the augmented lagrangian multiplier technique with the hopfield network to solve a constrained optimization problem into which the original bayesian estimation problem is reformulated. the lh method effectively overcomes instabilities that are inherent in the penalty method (e.g. hopfield network) or the lagrange multiplier method in constrained optimization. an additional advantage of the lh method is its suitability for neural-like analog implementation. experimental results are presented which show that lh yields good quality solutions at reasonable computational costs.
geometric and photometric constraints for surface recovery. in this paper we present a novel approach to surface recovery from an image sequence of a rotating object. in this approach, the object is illuminated under a collinear light source (where the light source lies on or near the optical axis) and rotated on a controlled turntable. a wire-frame of 3d curves on the object surface is extracted by using shading and occluding contours in the image sequence. then the whole object surface is recovered by interpolating the surface between curves on the wire-frame. the interpolation can be done by using geometric or photometric methods. the photometric method uses shading information and is more powerful than geometric methods. the experimental results on real image sequence of matte and specular surfaces show that the technique is feasible and promising.
dynamic appearance-based recognition. we describe a hierarchical appearance-based method for learning, recognizing, and predicting arbitrary spatiotemporal sequences of images. the method, which implements a robust hierarchical form of the kalman filter derived from the minimum description length (mdl) principle, includes as a special case several well-known object encoding techniques including eigenspace methods for static recognition. successive levels of the hierarchical filter implement dynamic models operating over successively larger spatial and temporal scales. each hierarchical level predicts the recognition state at a lower level and modifies its own recognition state using the residual error between the prediction and the actual lower-level state. simultaneously, on a longer time scale, the filter learns an internal model of input dynamics by adapting its generative and state transition matrices at each level to minimize prediction errors. the resulting prediction/learning scheme thereby implements an on-line form of the well-known expectation-maximization (em) algorithm from statistics. we present experimental results demonstrating the method's efficacy in mediating robust spatiotemporal recognition in a variety of scenarios containing varying degrees of occlusions and clutter.
using chromaticity distributions and eigenspace analysis for pose, illumination, and specularity-invariant recognition of 3d objects. the distribution of object colors can be effectively utilized for recognition and indexing. difficulties arise in the recognition of object color distributions when there are variations in illumination color, changes in object pose with respect to illumination direction, and specular reflections. however, most of the recent approaches to color-based recognition focus mainly on illumination color invariance. we propose an approach that identifies object color distributions influenced by: (1) illumination pose, (2) illumination color and (3) specularity. we suggest the use of chromaticity distributions to achieve illumination pose invariance. to characterize changes in chromaticity distribution due to illumination color, a set of chromaticity histograms of each object is generated for a range of lighting colors based on linear models of illumination and reflectance, and the histograms are represented using a small number of eigen basis vectors constructed from principal components analysis. since specular reflections may alter the chromaticity distributions of test objects, a model-based specularity detection/rejection algorithm, called chromaticity differencing, is developed to reduce these effects.
texture features and learning similarity. this paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. a gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. these features compare favorably with other existing texture representations. a simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. with learning similarity, the performance of similar pattern retrieval improves significantly. an important aspect of this work is its application to real image data. texture feature extraction with similarity learning is used to search through large aerial photographs. feature clustering enables efficient search of the database as our experimental results indicate.
edge flow: a framework of boundary detection and image segmentation. a novel boundary detection scheme based on edgejow is proposed in this paper. this scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector: by iteratively propagating the edge flow, the boundaries can be detected at image locations which encounter two opposite directions offrow in the stable state. a user defined image scale is the only significant control parameter that is needed by the algorithm. the scheme facilitates integration of color and texture into a single framework for boundary detection.