waashk/dblp huggingface dataset:
histogram preserving image transformations. histograms are used to analyze and index images. they have been found experimentally to have low sensitivity to certain types of image morphisms, for example, viewpoint changes and object deformations. the precise effect of these image morphisms on the histogram, however, has not been studied. in this work we derive the complete class of local transformations that preserve or scale the magnitude of the histogram of all images. we also derive a more general class of local transformations that preserve the histogram relative to a particular image. to achieve this, the transformations are represented as solutions to families of vector fields acting on the image. the local effect of fixed points of the fields on the histograms is also analyzed. the analytical results are verified with several examples. we also discuss several applications and the significance of these transformations for histogram indexing.
projective registration with difference decomposition. current methods for registering image regions perform well for simple transformations or large image regions. the author presents a new method that is better able to handle small image regions as they deform with nonlinear transformations. he introduces difference decomposition, a novel approach to solving the registration problem. the method is a generalization of previous methods and can better handle nonlinear transforms. although the methods are general, he focuses on projective transformations and introduces piecewise-projective transformations for modeling the motions of non-planar objects. he concludes with examples from a prototype implementation.
real-time tracking of image regions with changes in geometry and illumination. historically, ssd or correlation-based visual tracking algorithms have been sensitive to changes in illumination and shading across the target region. this paper describes methods for implementing ssd tracking that is both insensitive to illumination variations and computationally efficient. we first describe a vector-space formulation of the tracking problem, showing how to recover geometric deformations. we then show that the same vector space formulation can be used to account for changes in illumination. we combine geometry and illumination into an algorithm that tracks large image regions on live video sequences using no more computation than would be required to trade with no accommodation for illumination changes. we present experimental results which compare the performance of ssd tracking with and without illumination compensation.
motion of disturbances: detection and tracking of multi-body non-rigid motion. we present a new approach to the tracking of very non rigid patterns of motion, such as water flowing down a stream. the algorithm is based on a "disturbance map," which is obtained by linearly subtracting the temporal average of the previous frames from the new frame. every local motion creates a disturbance having the form of a wave, with a "head" at the present position of the motion and a historical "tail" that indicates the previous locations of that motion. these disturbances serve as loci of attraction for "tracking particles" that are scattered throughout the image. the algorithm is very fast and can be performed in real time. we provide excellent tracking results on various complex sequences, using both stabilized and moving cameras, showing: a busy ant column, waterfalls, rapids and flowing streams, shoppers in a mall, and cars in a traffic intersection.
rectified catadioptric stereo sensors. it has been previously shown how mirrors can be used to capture stereo images with a single camera, an approach termed catadioptric stereo. in this paper, we present novel catadioptric sensors that use mirrors to produce rectified stereo images. the scan-line correspondence of these images benefits real-time stereo by avoiding the computational cost and image degradation due to resampling when rectification is performed after image capture. first, we develop a theory which determines the number of mirrors that must be used and the constraints on those mirrors that must be satisfied to obtain rectified stereo images with a single camera. then, we discuss in detail the use of both one and three mirrors. in addition, we show how the mirrors should be placed in order to minimize sensor size for a given baseline, an important design consideration. in order to understand the feasibility of building these sensors, we analyze rectification errors due to misplacement of the camera with respect to the mirrors.
/spl lambda//spl tau/-space representation of images and generalized edge detector. an image clad surface representation based on regularization theory is introduced in this paper. this representation is based on a hybrid model derived from the physical membrane and plate models. the representation, called the /spl lambda//spl tau/-representation, has two dimensions; one dimension represents smoothness or scale while the other represents the continuity of the image or surface. it contains images/surfaces sampled both in scale space and the weighted sobolev space of continuous functions. thus, this new representation can be viewed as an extension of the well-known scale space representation. we have experimentally shown that the proposed hybrid model results in improved results compared to the two extreme constituent model, i.e., the membrane and the plate models. based on this hybrid model, a generalized edge detector (ged) which encompasses most of the well-known edge detectors under a common framework is developed. the existing edge detectors can be obtained from the generalized edge detector by simply specifying the valves of two parameters, one of which controls the shape of the filter (/spl tau/) and the other controls the scale of the filter (/spl lambda/). by sweeping the valves of these two parameters continuously, one can generate an edge representation in the /spl lambda//spl tau/ space, which is very useful for developing a goal-directed edge detection scheme for a specific task. the proposed representation and the edge detector have been evaluated qualitatively and quantitatively on several different types of image data such as intensity, range and stereo images.
reconstruction of a scene with multiple linearly moving objects. in this paper we describe an algorithm to recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously given a monocular image sequence. the number of the moving objects is automatically detected without prior motion segmentation. assuming that the objects are moving linearly with constant speeds, we propose a unified geometrical representation of the static scene and the moving objects. this representation enables the embedding of the motion constraints into the scene structure, which leads to a factorization-based algorithm. we also discuss solutions to the degenerate cases which can be automatically detected by the algorithm. extension of the algorithm to weak perspective projections is presented as well. experimental results on synthetic and real images show that the algorithm is reliable under noise.
graph matching by graduated assignment. a new algorithm for graph matching, which uses graduated assignment is presented, along with experimental results demonstrating large improvements in speed and accuracy over previous techniques. the softassign, a novel constraint satisfaction technique, is applied to a new graph matching energy function that uses a robust, sparse distance measure between the links of the two graphs. the softassign, which has emerged out of the neural network/statistical physics framework enforces two-way (assignment) constraints without the use of penalty terms. the algorithm's low order computational complexity [0(lm), where l and m are the number of links in the two graphs] compares favorably with most competing approaches. the method, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. experiments on graphs generated from images and on randomly generated graphs, including benchmarks against a relation labeling algorithm and an algorithm employing potts glass dynamics are reported. over twenty-five thousand experiments were conducted. no comparable results have been reported by any other graph matching algorithm before in the research literature.
mirror and point symmetry under perspective skewing. over recent years, symmetry research has shifted from the detection of affinely to perspectively skewed mirror symmetry. also, links between invariance research and symmetry-specific geometric constraints have been established. the paper aims to contribute to both strands. several sets of symmetry specific invariants are derived, that can be used in different situations, depending on the a priori assumptions made. it is also argued that all the results directly apply to the case of perspectively skewed point symmetry.
lip reading from scale-space measurements. systems that attempt to recover the spoken word from image sequences usually require complicated models of the mouth and its motions. here we describe a new approach based on a fast mathematical morphology transform called the sieve. we form statistics of scale measurements in one and two dimensions and these are used as a feature vector for standard hidden markov models (hmms).
hierarchical recognition of articulated objects from single perspective views. this paper presents an approach to the recognition of articulated 3d objects in monocular video images. a hierarchical object representation models objects as a composition of rigid components which are explicitly connected by specific kinematic constraints, e.g., rotational and/or translational joints. the recognition task follows this tree-like structure by first estimating the 3d pose of the static component (root) and afterwards determining the relative 3d pose of the remaining components recursively. this method limits the search space for the actual correspondences between image and model features and copes with the problem of self-occlusion. experiments in the context of autonomous, mobile robots show the practicability of this approach.
computing optical flow with physical models of brightness variation. although most optical flow techniques presume brightness constancy, it is well-known that this constraint is often violated, producing poor estimates of image motion. this paper describes a generalized formulation of optical flow estimation based on models of brightness variations that are caused by time-dependent physical processes. these include changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. with these models, we simultaneously estimate the 2d image motion and the relevant physical parameters of the brightness change model. the estimation problem is formulated using total least squares (tls), with confidence bounds on the parameters. experiments in four domains, with both synthetic and natural inputs, show how this formulation produces superior estimates of the 2d image motion.
inference of segmented, volumetric shape from three intensity images. we present a method to infer segmented and full volumetric descriptions of objects from intensity images. we use three weakly calibrated images from closely spaced viewpoints as input. deriving full volumetric descriptions requires the development of robust inference rules. the inference rules are based on local properties of generalized cylinders (gcs). we first detect groups in each image based on proximity, parallelism and symmetry. the groups in the three images are matched and their contours are labelled as "true" and "limb" edges. we use the information about groups and the label associated with their contours to recover visible surfaces and their surface axes. to extract the complete volume in terms of a gc, we need to infer the gc axis, its cross section and the scaling function. the properties of straight and curved axis generalized cylinders are used locally on the visible surfaces to obtain the gc axis. the cross section is recovered if seen in the images, else it is inferred using the visible surfaces and gc properties. we consider groups with true edges, limb edges or a combination of both. the final descriptions are volumetric and in terms of parts. sometimes, when not enough information is present to make volumetric inferences, the descriptions remain at the surface level. we demonstrate results on real images of moderately complex objects with texture and shadows.
object-based video indexing for virtual studio productions. this paper introduces an object-based approach for temporal video partitioning and content-based indexing, where the basic indexing unit is ``lifespan of a video object," rather than a ``camera shot" or ``story unit." we propose a system to extract content-based features of video objects (vos), based on a compact 2d triangular mesh representation of them. an adaptive mesh-based video object tracking scheme is then employed to compute the motion trajectori of all node points. a set of ``key snapshots" which constitute a visual summary of the lifespan of the object are automatically selected using motion and shape information. the system provides direct access to the vos and gives the functionalities such as object-based search, manipulation, animation, and tracking.
using physics-based invariant representations for the recognition of regions in multispectral satellite images. we present a set of algorithms and a search strategy for the robust content- based retrieval of multispectral satellite images. since the property of interest in these images is usually the physical characteristics of ground cover, we use representations and methods that are invariant to illumination and atmospheric conditions. the representations and algorithms are derived for this application from a physical model for the formation of multispectral satellite images. the use of several representations and algorithms is necessary to interpret the diversity of physical and geometric structure in these images. algorithms are used that exploit multispectral distributions, multispectral spatial structure, and labeled classes. the performance of the system is demonstrated on a large set of multispectral satellite images taken over different areas of the united states under different illumination and atmospheric conditions.
comparison of edge detectors: a methodology and initial study. because of the difficulty of obtaining ground truth for real images, the traditional technique for comparing low-level vision algorithms is to present image results, side by side, and to let the reader subjectively judge the quality. this is not a scientifically satisfactory strategy. however, human rating experiments can be done in a more rigorous manner, to provide useful quantitative conclusions. we present a paradigm based on experimental psychology and statistics, in which humans rate the output of low level vision algorithms. we demonstrate the proposed experimental strategy by comparing four well known edge detectors: canny, nalwa-binford, sarkar-boyer, and sobel. we answer the following questions: is there a statistically significant difference in edge detector outputs as perceived by humans? do the edge detection results of an operator vary significantly with the choice of its parameters? for each detector, is it possible to choose a single set of optimal parameters for all the images without significantly affecting the edge output quality? does an edge detector produce edges of the same quality for all images, or does the edge quality vary with the image?
a four-step camera calibration procedure with implicit image correction. in geometrical camera calibration the objective is to determine a set of camera parameters that describe the mapping between 3-d reference coordinates and 2-d image coordinates. various methods for camera calibration can be found from the literature. however, surprisingly little attention has been paid to the whole calibration procedure, i.e., control point extraction from images, model fitting, image correction, and errors originating in these stages. the main interest has been in model fitting, although the other stages are also important. in this paper we present a four-step calibration procedure that is an extension to the two-step method. there is an additional step to compensate for distortion caused by circular features, and a step for correcting the distorted image coordinates. the image correction is performed with an empirical inverse model that accurately compensates for radial and tangential distortions. finally, a linear method for solving the parameters of the inverse model is presented.
tracking non-rigid, moving objects based on color cluster flow. in this contribution we present an algorithm for tracking non-rigid, moving objects in a sequence of colored images, which were recorded by a non-stationary camera. the application background is vision-based driving assistance in the inner city. in an initial step, object parts are determined by a divisive clustering algorithm, which is applied to all pixels in the first image of the sequence. the feature space is defined by the color and position of a pixel. for each new image the clusters of the previous image are adapted iteratively by a parallel k-means clustering algorithm. instead of tracking single points, edges, or areas over a sequence of images, only the centroids of the clusters are tracked. the proposed method remarkably simplifies the correspondence problem and also ensures a robust tracking behaviour.
canonical decomposition of steerable functions. this paper describes a general mathematical formulation for the problem of constructing steerable functions. the formulation is based on lie group theory and is thus applicable to transformations which are lie groups, such as, rotation, translation, scaling, and affine transformation. for one-parameter and abelian multi-parameter lie transformation groups, a canonical decomposition of all possible steerable functions, derived using the jordan decomposition of matrices, is developed. it is shown that any steerable function under lie transformation groups can be described using this decomposition. finally, a catalog of steerable functions for several common multi-parameter image transformation groups is also provided.
invariant histograms and deformable template matching for sar target recognition. recognizing a target in synthetic-aperture radar (sar) images is an important, yet challenging, application of the model-based vision technique. this paper describes a model-based sar recognition system based on invariant histograms and deformable template matching techniques. an invariant histogram is a histogram of invariant values defined by geometric features such as points and lines in sar images. although a few invariants are sufficient to recognize a target, we use a histogram of all invariant values given by all possible target feature pairs. this redundant histogram enables robust recognition under severe occlusions typical in sar recognition scenarios. multi-step deformable template matching examines the existence of an object by superimposing templates over potential energy field generated from images or primitive features. it determines the template configuration which has the minimum deformation and the best alignment of the template with features. the deformability of the template absorbs the instability of sar features. we have implemented the system and evaluated the system performance using hybrid sar images, generated from synthesized model signatures and real sar background signatures.
recognizing 3d objects by generating random actions. this paper presents a formal model of an active recognition system that can be programmed by learning. at each time step the system decides between producing an action to generate new data and stopping to issue the name of the object observed. the actions can be directed either towards the external environment or towards the internal perceptual system of the agent. the decision strategy is based on a quantitative evaluation of the system learning experience. the problem studied is the recognition of chess pieces using a moving camera and a multiscale feature detector. the recognition is difficult because the objects are complex -- neither polyhedral nor smooth -- and rather similar between classes, especially in certain view configurations. the system uses the information obtained by observing internal state transitions when the camera is moved or when the feature detector scale is changed. a simulation of the agent and the environment is used for experimental measures of the model performances.
real-time closed-world tracking. a real-time tracking algorithm that uses contextual information is described. the method is capable of simultaneously tracking multiple, non-rigid objects when erratic movement and object collisions are common. a closed-world assumption is used to adaptively select and weight image features used for correspondence. results of algorithm testing and the limitations of the method are discussed. the algorithm has been used to track children in an interactive, narrative playspace.
euclidean reconstruction from image sequences with varying and unknown focal length and principal point. in this paper the special case of reconstruction from image sequences taken by cameras with skew equal to 0 and aspect ratio equal to has been treated. these type of cameras, here called cameras with euclidean image planes, represent rigid projections where neither the principal point nor the focal length is known. it will be shown that it is possible to reconstruct an unknown object from images taken by a camera with euclidean image plane up to similarity transformations, i.e., euclidean transformations plus changes in the global scale. an algorithm, using bundle adjustment techniques, has been implemented. the performance of the algorithm is shown on simulated data.