OpenRaiser/Intern-Atlas huggingface dataset:
Rotation Invariant Neural Network-Based Face Detection
Authors: Henry A. Rowley, Shumeet Baluja, Takeo Kanade
Venue: CVPR
Year: 1998

In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; a "router" network first processes each input window to determine its orientation and then uses this information to prepare the window for one or more "detector" networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces rotated out of the image plane, such as profiles and semi-profiles.
Rotation Invariant Neural Network-Based Face Detection
Authors: Henry A. Rowley, Shumeet Baluja, Takeo Kanade
Venue: CVPR
Year: 1998

In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; a &quot;router&quot; network first processes each input window to determine its orientation and then uses this information to prepare the window for one or more &quot;detector&quot; networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces rotated out of the image plane, such as profiles and semi-profiles. 1. Introduction In our observations of face detector demonstrations, we have found that users expect faces to be detected at any angle, as shown in Figure 1. In this paper, we present a neural network-based algorithm to detect faces in gray-scale images. Unlike similar pre...
Moment Invariants and Quantization Effects
Authors: Gouda I. Salama, A. Lynn Abbott
Venue: CVPR
Year: 1998

This paper considers the effect of spatial quantization on several moment invariants. Of particular interest are the affine moment invariants, which have emerged in recent years as a useful tool for image reconstruction, image registration, and recognition of deformed objects. Traditional analysis assumes moments and moment invariants for images that are defined in the continuous domain. In practice, however, the digitization process introduces errors that violate the invariance assumption. This paper presents an analysis of quantization-induced error on (two-dimensional) Hu moment invariants and affine moment invariants, and on invariants derived from (one-dimensional) contour moments. Error bounds are given in several cases.
Learning to Form Large Groups of Salient Image Features
Authors: Sudeep Sarkar
Venue: CVPR
Year: 1998

We offer a novel strategy to adapt the perceptual organization process to an object and its contest in a scene. Given a set of training images of an object in context, a learning process decides on the relative importance of the basic Gestalt relationships such as proximity, parallelness, similarity, symmetry, closure, and common region towards segregating the object from the background. This learning is accomplished using a team of stochastic automata in a N-player cooperative game framework. The grouping process which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We demonstrate the robust performance of the growing system on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to perform figure-ground segmentation from a set of local relations, each defined over a small number of primitives.
Minimax Entropy and Learning by Diffusion
Authors: Jayant Shah
Venue: CVPR
Year: 1998

A system of coupled differential equations is formulated which learns priors for modelling "preattentive" textures. It is derived from an energy functional consisting of a linear combination of a large number of terms corresponding to the features that the system is capable of learning. The system learns the parameters associated with each feature by applying gradient ascent to the log-likelihood function. Update of each parameter is thus governed by the residual with respect to the corresponding feature. A feature residual is computed from its observed value and value generated by the system. The latter is calculated from a synthesized sample image, which is generated by means of a reaction-diffusion equation obtained by applying gradient descent to the energy functional.
Multiple Feature Integration for Robust Object Localization
Authors: Shishir Shah 0001, J. K. Aggarwal
Venue: CVPR
Year: 1998

This paper presents a methodology for localization of manmade objects in complex scenes by learning multiple feature models in images. The methodology is based on a modular structure consisting of multiple classi#ers, each of which solves the problem independently based on its input observations. Each classi- #er module is trained to detect manmade object regions and a higher order decision integrator collects evidencefrom each of the modules to delineate a #nal region of interest. The proposed framework is applied to the problem of Automatic Manmade Object Localization #Detection. Results obtained on the detection of vehicles in color visual and infrared imagery are presented in this paper. 1 Introduction This paper addresses the problem of object localization in complex scenes imaged by a single sensor or registered multiple sensors. The general topic of determining region of interest #ROI# and object detection is a critical step in all existing paradigms for object recognition. In...
An Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task
Authors: Min C. Shin, Dmitry B. Goldgof, Kevin W. Bowyer
Venue: CVPR
Year: 1998

This paper presents a task-oriented evaluation methodology for edge detectors. Performance is measured based on the task of structure from motion. Eighteen real image sequences from 2 different scenes varying in the complexity and scenery types are used. The task-level ground truth for each image sequence is manually specified in terms of the 3D motion and structure. An automated tool computes the accuracy of the motion and structure achieved using the set of edge maps. Parameter sensitivity and execution speed are also analyzed. Four edge detectors are compared. All implementations and data sets are publicly available.
Interactive Construction of 3D Models from Panoramic Mosaics
Authors: Heung-Yeung Shum, Mei Han, Richard Szeliski
Venue: CVPR
Year: 1998

This paper presents an interactive modeling system that constructs 3D models from a collection of panoramic image mosaics. A panoramic mosaic consists of a set of images taken around the same viewpoint, and a transformation matrix associated with each input image. Our system first recovers the camera pose for each mosaic from known line directions and points, and then constructs the 3D model using all available geometrical constraints. We partition constraints into soft and hard linear constraints so that the modeling process can be formulated as a linearly-constrained least-squares problem, which can be solved efficiently using QR factorization. The results of extracting wire frame and texture-mapped 3D models from single and multiple panoramas are presented.
What is the Spectral Dimensionality of Illumination Functions in Outdoor Scenes?
Authors: David Slater, Glenn Healey
Venue: CVPR
Year: 1998

The spectral properties of outdoor illumination functions can vary significantly due to atmospheric conditions and scene geometry. The authors show using a statistical analysis of a comprehensive physical model that the variation in outdoor illumination functions over both the visible range (0.33 /spl mu/m-0.7 /spl mu/m) and the visible/near-infrared range (0.4 /spl mu/m-2.5 /spl mu/m) can be represented accurately by use of seven-dimensional linear models. The physical model includes solar and scattered radiation as well as the effects of atmospheric gases and aerosols. The MODTRAN 3.5 code was employed for computing radiative transfer aspects of the model. The authors show that the new model has strong agreement over the visible wavelengths with the empirical study of Judd et al. (1964). The authors also demonstrate the accuracy of the model over the 0.4 /spl mu/m-2.5 /spl mu/m spectral range using measured outdoor illumination functions.
Optimal Structure from Motion: Local Ambiguities and Global Estimates
Authors: Stefano Soatto, Roger W. Brockett
Venue: CVPR
Year: 1998

We present an analysis of SFM from the point of view of noise. This analysis results in an algorithm that is provably convergent and provably optimal with respect to a chosen norm. In particular, we cast SFM as a nonlinear optimization problem and define a bilinear projection iteration that converges to fixed points of a certain cost-function. We then show that such fixed points are "fundamental", i.e. intrinsic to the problem of SFM and not an artifact introduced by our algorithm. We classify and characterize geometrically local extrema, and we argue that they correspond to phenomena observed in visual psychophysics. Finally, we show under what conditions it is possible-given convergence to a local extremum-to "jump" to the valley containing the optimum; this leads us to suggest a representation of the scene which is invariant with respect to such local extrema.
Interactive Sensor Planning
Authors: Ioannis Stamos, Peter K. Allen
Venue: CVPR
Year: 1998

This paper describes an interactive sensor planning system, that can be used to select viewpoints subject to camera visibility, field of view and task constraints. Application areas for this method include surveillance planning, safety monitoring, architectural site design planning, and automated site modeling. Given a description, of the sensor's characteristics, the objects in the 3-D scene, and the targets to be viewed, our algorithms compute the set of admissible view points that satisfy the constraints. The system first builds topologically correct solid models of the scene from a variety of data sources. Viewing targets are then selected, and visibility volumes and field of view cones are computed and intersected to create viewing volumes where cameras can be placed. The user can interactively manipulate the scene and select multiple target features to be viewed by a camera. The user can also select candidate viewpoints within this volume to synthesize views and verify the correctness of the planning system. We present experimental results for the planning system on an actual complex city model.
Direct Estimation of Motion and Extended Scene Structure from a Moving Stereo Rig
Authors: Gideon P. Stein, Amnon Shashua
Venue: CVPR
Year: 1998

We investigate the relationship between the kinematics (infinitesimal motion model) of a calibrated Stereo Rig and point and line image feature measurements seen at two time instances of the rig's motion (four images in all). In particular we are interested in the byproduct of this analysis providing a direct connection between the spatio-temporal derivatives of the images at two time instances and kinematics of the 3D motion of the Rig. We establish a fundamental result showing that 3 quadruples of point-line-line-line matches (i.e., point in the reference image and lines coincident with the corresponding points in the remaining three images) are sufficient for a unique linear solution for the kinematics of the rig. In other words, the projected instantaneous motion of "one and a half" 3D lines is sufficient for recovering the kinematics of the moving rig. In particular, spatio-temporal derivatives across 3 points are sufficient for a direct estimation of the rig's motion. Consequently, we describe a new direct estimation method for motion estimation and 3D reconstruction from stereo image sequences obtained by a stereo rig moving through a rigid world. Correspondences (optic flow) are not required as spatio-temporal derivative are used instead. One can then use the images from both pairs combined, to compute a dense depth map. Finally, since the basic equations are linear, we combine the contribution coming from all pixels in the image using a Least Squares approach.
Analyzing the Bidirectional Texture Function
Authors: Pei-hsiu Suen, Glenn Healey
Venue: CVPR
Year: 1998

The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis features that are required to represent the space of observed textures for a surface as a function of ranges of illumination and viewing angles. Basis textures are represented using multiband correlation functions. We study properties of the dimensionality surface for real materials using the Columbia Utrecht Reflectance and Texture (CUReT) database. The analysis shows that the dependence of the dimensionality surface on ranges of illumination and viewing angles is approximately linear with a slope dependent on the complexity of the sample.
Connected Vibrations: A Modal Analysis Approach for Non-Rigid Motion Tracking
Authors: Hai Tao, Thomas S. Huang
Venue: CVPR
Year: 1998

A new framework is presented in this paper for tracking non-rigid motion in image sequences. This method models non-rigid motions as the deformations of connected 2D membrane patches. Local smoothness constraints for each patch are the low-frequency vibration modes obtained from modal analysis. These constraints are incorporated with the template matching algorithm into a local motion estimator. To maintain the global topological structure, different patches are connected together by hinges, which introduce additional constraints in the tracking process. The resulted over-constrained linear system is solved efficiently using a least square estimator. To improve the robustness of the tracker, stochastic filtering technique is employed to take advantage of the temporal continuity of the deformations. Promising results on both natural and synthetic facial motion sequences are demonstrated in this paper.
A New Complex Basis for Implicit Polynomial Curves and its Simple Exploitation for Pose Estimation and Invariant Recognition
Authors: Jean-Philippe Tarel, David B. Cooper
Venue: CVPR
Year: 1998

New representations are developed for 2D IP (implicit polynomial) curves of arbitrary degree. These representations permit shape recognition and pose estimation with essentially single, rather than iterative, computation, and extract and use all the information in the polynomial coefficients. This is accomplished by decomposing polynomial coefficient space into a union of orthogonal subspaces for which rotations within two dimensional subspaces or identity transformations within one dimensional subspaces result from rotations in x, y measured-data space. These rotations in the two dimensional coefficient subspaces are related in simple ways to each other and to rotation in the x, y data space. By recasting this approach in terms of complex polynomials, i.e., z=x+iy and complex coefficients, further simplification occurs for rotations and some simplification occurs for translation.
Variants of Dynamic Link Architecture Based on Mathematical Morphology for Frontal Face Authentication
Authors: Anastasios Tefas, Constantine Kotropoulos, Ioannis Pitas
Venue: CVPR
Year: 1998

Two novel variants of dynamic link architecture that are based on mathematical morphology and incorporate coefficients which weigh the contribution of each node in elastic graph matching according to its discriminatory power are developed. They are the so called Morphological Dynamic Link Architecture and the Morphological Signal Decomposition-Dynamic Lint Architecture. The proposed variants are tested for face authentication in a cooperative scenario where the candidates claim an identity to be checked. Their performance is evaluated in terms of their receiver operating characteristic and the equal error rate achieved in M2VTS database. An equal error rate in the range 3.7-6.8% is reported.
Nonrigid Motion Analysis Based on Dynamic Refinement of Finite Element Models
Authors: Leonid V. Tsap, Dmitry B. Goldgof, Sudeep Sarkar
Venue: CVPR
Year: 1998

In this paper we propose new algorithms for accurate nonrigid motion tracking. Given only a set of sparse correspondences and incomplete or missing information about geometry or material properties, we recover dense motion vectors using nonlinear finite element models. The method is based on the iterative analysis of the differences between the actual and predicted behavior. Large differences indicate that an object's properties are not captured properly by the model. Feedback from the images during the motion allows the refinement of the model by minimizing the error between the expected and true position of the object's points. Unknown parameters are recovered using an iterative descent search for the best model that approximates nonrigid motion of the given object. Thus, during tracking the model is refined which, in turn, improves tracking quality. The method was applied successfully to man-made elastic materials and human skin to recover unknown elasticity, to complex 3-D objects to find details of their geometry, and to a hand motion analysis application.
Image Segmentation for Human Tracking Using Sequential-Image-Based Hierarchical Adaptation
Authors: Akira Utsumi, Jun Ohya
Venue: CVPR
Year: 1998

We propose a novel method of extracting a moving object region from each frame in a series of images regardless of complex, changing background using statistical knowledge about the target. In vision systems for 'real worlds' like a human motion tracer, a priori knowledge about the target and environment is often limited (e.g., only the approximate size of the target is known) and is insufficient for extracting the target motion directly. In our approach, information about both target object and environment is extracted with a small amount of given knowledge about the target object. Pixel value (color, intensity, etc.) distributions for both the target object and background region are adaptively estimated from the input image sequence based on the knowledge. Then, the probability of each pixel being associated with the target object is calculated. The target motion can be extracted from the calculated stochastic image. We confirmed the stability of this approach through experiments.
A Spatiotemporal Motion Model for Video Summarization
Authors: Nuno Vasconcelos, Andrew Lippman
Venue: CVPR
Year: 1998

The compact description of a video sequence through a single image map and a dominant motion has applications in several domains, including video browsing and retrieval, compression, mosaicing, and visual summarization. Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene, a task which has been, in the past, addressed through temporally localized motion estimates. In this paper, we show how the lack of temporal consistency associated with such estimates can undermine the validity of the dominant motion assumption, leading to oscillation between different scene interpretations and poor registration. To avoid this oscillation, we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations, leading to more meaningful content summarization.
A Bayesian Framework for Semantic Content Characterization
Authors: Nuno Vasconcelos, Andrew Lippman
Venue: CVPR
Year: 1998

Current systems for content filtering, browsing, and retrieval rely on low-level image descriptors which are unintuitive for most users. In this paper, we propose an alternative framework that exploits the structured nature of most content sources to achieve semantic content characterization, and lead to much more meaningful user interaction. Computationally, this framework is based on the principles of Bayesian inference and can be implemented efficiently with Bayesian networks. As an illustration of its potential we apply it to the domain of movie databases.
A Coupled PDE Model of Nonlinear Diffusion for Image Smoothing and Segmentation
Authors: Baba C. Vemuri, Li Wang, Yunmei Chen
Venue: CVPR
Year: 1998

Image denoising and segmentation are fundamental problems in the field of image processing and computer vision with numerous applications. We propose a partial differential equation (PDE) based smoothing and segmentation framework wherein the image data are smoothed via an evolution equation that is controlled by a vector field describing a viscous fluid flow. Image segmentation in this framework is defined by locations in the image where the fluid velocity is a local maximum. The nonlinear image smoothing is selectively achieved to preserve edges in the image. The novelty of this approach lies in the fact that the selective term is derived from a nonlinearly regularized image gradient field unlike most earlier techniques which either used a constant (with respect to time) selective term or a time varying nonlinearly smoothed scalar valued term. Implementation results on synthetic and real images are presented to depict the performance of the technique in comparison to methods recently reported in literature.
Boundary Finding with Correspondence Using Statistical Shape Models
Authors: Yongmei Wang, Lawrence H. Staib
Venue: CVPR
Year: 1998

We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information.
Nonlinear PHMMs for the Interpretation of Parameterized Gesture
Authors: Andrew D. Wilson, Aaron F. Bobick
Venue: CVPR
Year: 1998

Recently we modified the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the states of the HMM. Development of the parametric hidden Markov model (PHMM) was motivated by the task of simultaneously recognizing and interpreting gestures that exhibit meaningful variation. With standard HMMs, such global variation confounds the recognition process. The original PHMM approach assumes a linear dependence of output density means on the global parameter. In this paper we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. We show a generalized expectation-maximization (GEM) algorithm for training the PHMM and a GEM algorithm to simultaneously recognize the gesture and estimate the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural azimuth/elevation parameterization of pointing direction.