OpenRaiser/Intern-Atlas huggingface dataset:
Photometric Computation of the Sign of Gaussian Curvature Using a Curve-Orientation Invariant
Authors: Elli Angelopoulou, Lawrence B. Wolff
Venue: CVPR
Year: 1997

The authors compute the sign of Gaussian curvature using a purely geometric definition. Consider a point p on a smooth surface S and a closed curve /spl gamma/ on S which encloses p. The image of /spl gamma/ on the unit normal Gaussian sphere is a new curve /spl beta/. The Gaussian curvature at p is defined as the ratio of the area enclosed by /spl gamma/ over the area enclosed by /spl beta/ as /spl gamma/ contracts to p. The sign of Gaussian curvature at p is determined by the relative orientations of the closed curves /spl gamma/ and /spl beta/. They directly compare the relative orientation of two such curves from intensity data. They employ three unknown illumination conditions to create a photometric scatter plot. This plot is in one-to-one correspondence with the subset of the unit Gaussian sphere containing the mutually illuminated surface normals. This permits direct computation of the sign of Gaussian curvature without the recovery of surface normals. Their method is albedo invariant. They assume diffuse reflectance, but the nature of the diffuse reflectance can be general and unknown. Simulations, as well as empirical results, demonstrate the accuracy of the technique.
A Curvature Based Descriptor Invariant to Pose and Albedo Derived from Photometric Data
Authors: Elli Angelopoulou, James P. Williams 0002, Lawrence B. Wolff
Venue: CVPR
Year: 1997

Gaussian curvature is an invariant local descriptor of smooth surfaces. We present an object signature which is a condensed representation of the distribution of Gaussian curvature information at visible object points. An invariant related to Gaussian curvature at a point is derived from the covariance matrix of the photometric values in a neighborhood about that point. In addition, we introduce an albedo-normalization method that is capable of cancelling albedo on Lambertian surfaces. We use three illumination conditions, two of which are unknown. The three-tuple of intensity values at a point is related via a one-to-one mapping to the surface normal at that point. The determinant of the covariance matrix of the local three-tuples is invariant to albedo, rotation and translation. The collection of determinants over mutually illuminated object points is combined into a signature distribution which is albedo, rotation, translation, and scale invariant. An object recognition methodology using these signatures is proposed.
Independent 3D Motion Detection Based on Depth Elimination in Normal Flow Fields
Authors: Antonis A. Argyros, Stelios C. Orphanoudakis
Venue: CVPR
Year: 1997

This paper considers a specific problem of visual perception of motion, namely the problem of visual detection of independent 3D motion. Most of the existing techniques for solving this problem rely on restrictive assumptions about the environment, the observer's motion, or both. Moreover they are based on the computation of optical flow, which amounts to solving the ill-posed correspondence problem. In this work, independent motion detection is formulated as robust parameter estimation applied to the visual input acquired by a binocular rigidly moving observer. Depth and motion measurements are combined in a linear model. The parameters of this model are related to the parameters of self-motion (egomotion) and the parameters of the stereoscopic configuration of the observer. The robust estimation of this model leads to a segmentation of the scene based on 3D motion. The method avoids the correspondence problem by employing only normal flow fields. Experimental results demonstrate the effectiveness of this method in detecting independent motion in scenes with large depth variations, without any constraints imposed on observer motion.
Novel view synthesis in tensor space
Authors: Shai Avidan, Amnon Shashua
Venue: CVPR
Year: 1997

We present a new method for synthesizing novel views of a 3D scene from few model images in full correspondence. The core of this work is the derivation of a tensorial operator that describes the transformation from a given tensor of three views to a novel tensor of a new configuration of three views. By repeated application of the operator on a seed tensor with a sequence of desired virtual camera positions we obtain a chain of warping functions (tensors) from the set of model images to create the desired virtual views.
Optic flow calculation using robust statistics
Authors: Alireza Bab-Hadiashar, David Suter
Venue: CVPR
Year: 1997

A method for calculating optic flow, using robust statistics, is developed. The method generally out-performs all competing methods in terms of accuracy. One of the key features in the success of this method, is that we use least median of squares, which is known to be robust to outliers. The computational cost is kept very low by using an approximate solution to the least median of squares only in a first stage that detects outliers. The essential ingredients of our method should be applicable in a wide range of other computer vision problems.
The Bas-Relief Ambiguity
Authors: Peter N. Belhumeur, David J. Kriegman, Alan L. Yuille
Venue: CVPR
Year: 1997

Since antiquity, artisans have created flattened forms, often called "bas-reliefs,"-which give an exaggerated perception of depth when viewed from a particular vantage point. This paper presents an explanation of this phenomena, showing that the ambiguity in determining the relief of an object is not confined to bas-relief sculpture but is implicit in the determination of the structure of any object. Formally, if the object's true surface is denoted by z/sub true/=f(x, y), then we define the "generalized bas-relief transformation" as z=/spl lambda/f(x, y)+/spl mu/x+/spl nu/y, with a corresponding transformation of the albedo. For each image of a Lambertian surface f(x, y) produced by a point light source at infinity, there exists an identical image of a bas-relief produced by a transformed light source. This equality holds for both shaded and shadowed regions. Thus, the set of possible images (illumination cone) is invariant over generalized bas-relief transformations. When /spl mu/=/spl nu/=0 (e.g. a classical bas-relief sculpture), we show that the set of possible motion fields are also identical. Thus, neither small unknown motions nor changes of illumination can resolve the bas-relief ambiguity. Implications of this ambiguity on structure recovery and shape representation are discussed.
Representation Of Objects In A Volumetric Frequency Domain With Application To Face Recognition
Authors: Jezekiel Ben-Arie, Dibyendu Nandy
Venue: CVPR
Year: 1997

A novel method for representing 3-D objects that unifies viewer and model centered object representations is presented. A unified 3-D frequency-domain representation (called volumetric/iconic spectral signatures-V/ISS) encapsulates both the spatial structure of the object and a continuum of its views in the same data structure. The frequency-domain image of an object viewed from any direction can be directly extracted employing an extension of the projection slice theorem, where each Fourier-transformed view is a planar slice of the volumetric frequency representation. The V/ISS representation call be employed for pose-invariant recognition of complex objects such as faces. The recognition and pose estimation is based on an efficient matching algorithm in a four dimensional Fourier space. Experimental examples of pose estimation and recognition of faces are also presented.
Pictorial Recognition Using Affine-Invariant Spectral Signatures
Authors: Jezekiel Ben-Arie, Zhiqian Wang
Venue: CVPR
Year: 1997

This paper describes an efficient approach to pose invariant object recognition employing pictorial recognition of image patches. A complete affine invariance is achieved by a representation which is based on a new sampling configuration in the frequency domain. Employing Singular Value Decomposition (SVD), the affine transform is decomposed into slant, tilt, swing, scale and 2D translation. From this decomposition, we derive an affine invariant representation that allows to recognize image patches that correspond to object surfaces which are roughly planar-invariant to their pose in space. The representation is in the form of Spectral Signatures that are derived from a set of Cartesian logarithmic-logarithmic (log-log) sampling configuration in the frequency domain. Unlike previous log-polar representations which are not invariant to slant (i.e. foreshortening only in one direction), our new configuration yields complete affine invariance. The proposed log-log configuration can be employed both globally or locally by a Gabor or Fourier transforms. Local representation enables to recognize separately several objects in the same image. The actual signature recognition is performed by multidimensional indexing in a pictorial dataset represented in the frequency domain. The recognition also provides 3D pose information.
Deformable Multi Template Matching with Application to Portal Images
Authors: Martin Berger, Gaudenz Danuser
Venue: CVPR
Year: 1997

The exact positioning of patients during radiotherapy is essential for high precision treatment. The registration of portal image sequences can help to control the patient position. The particular problem of such megavoltage X-ray imagery is its extremely low contrast, rendering accurate feature extraction a difficult task. To circumvent the step of feature extraction, the algorithm presented in this paper relies on an area-based matching of the image signal using deformable templates. This strategy contrasts with most state of the art registration algorithms for portal imagery. The paper includes the mathematical formalism of the least squares template matching method, as well as the framework for automated quality control, together yielding a fast, robust and very accurate image matching procedure. Tests on 17 portal image series with more than 100 images in total have shown very satisfying results. Artificially rotated and shifted images demonstrate the performance of the method with respect to a ground truth.
Reconstruction of 3D-Curves from 2D-Images Using Affine Shape Methods for Curves
Authors: Rikard Berthilsson, Kalle Åström
Venue: CVPR
Year: 1997

In this paper, we propose an algorithm for doing reconstruction of general 3D-curves from a number of 2D-images taken by uncalibrated cameras. No point correspondences between the images are assumed. The curve and the view points are uniquely reconstructed, modulo common projective transformations and the point correspondence problem is solved. Furthermore, the algorithm is independent of the choice of coordinates, as it is based on orthogonal projections and aligning subspaces. The algorithm is based on an extension of affine shape of finite point configurations to more general objects.
Recursive Structure and Motion from Image Sequences using Shape and Depth Spaces
Authors: Rikard Berthilsson, Anders Heyden, Gunnar Sparr
Venue: CVPR
Year: 1997

A novel recursive method for estimating structure and motion from image sequences is presented. The novelty lies in the fact that the output of the algorithm is independent of the chosen coordinate systems in the images as well as the ordering of the points. It relies on subspace methods and is derived from both ordinary coordinate representations and camera matrices and from a so called depth and shape analysis. Furthermore, no initial phase is needed to start up the algorithm. It starts directly with the first two images and incorporates new images as soon as new corresponding points are obtained. The performance of the algorithm is shown on simulated data. Moreover, the two different approaches, one using camera matrices and the other using the concepts of affine shape and depth, are unified into a general theory of structure and motion from image sequences.
Motion estimation using ordinal measures
Authors: Dinkar N. Bhat, Shree K. Nayar, Alok Gupta
Venue: CVPR
Year: 1997

We present a method for motion estimation using ordinal measures. Ordinal measures are based on relative ordering of intensity values in an image region called rank permutation. While popular measures like the sum-of-squared-difference (SSD) and normalized correlation (NCC) rely on linearity between corresponding intensity values, ordinal measures only require them to be monotonically related so that rank permutations between corresponding regions are presented. This property turns out to be useful for motion estimation in tagged magnetic resonance images. We study the imaging equation involved in two methods of tagging and observe temporal monotonicity in intensity under certain conditions though the tags themselves fade. We compare our method to SSD and NCC in a rotating ring phantom image sequence. We present an experiment on a real heart image sequence which suggests the suitability of our method.
Controlling view-based algorithms using approximate world models and action information
Authors: Aaron F. Bobick, Claudio S. Pinhanez
Venue: CVPR
Year: 1997

Most view-based vision algorithms are based on strong assumptions about the disposition of the objects in the image. To safely apply those algorithms in real world image sequences, we propose that a vision system should be divided into two components. The first component contains an approximate world model of the scene-a low accuracy, coarse description of the objects and actions in the world. Approximate world models are constructed and updated by simple vision routines and by the use of action information provided by an external source. The second component employs view-based algorithms to perform required perceptual tasks; the selection and control of the view-based methods are determined by the information provided by the approximate world model. We demonstrate the approximate world model approach in a project to control cameras in a TV studio where the external context is provided by a script.
Experimental Performance Evaluation of Feature Grouping Modules
Authors: Sudhir Borra, Sudeep Sarkar
Venue: CVPR
Year: 1997

We present five performance measures to evaluate grouping modules in the context of constrained search and indexing based object recognition. Using these measures, we demonstrate a sound experimental framework based on statistical ANOVA tests to compare and contrast three edge based organization modules, namely those of A. Etemadi et al. (1991), D.W. Jacobs (1996), and S. Sarkar and K.L. Boyer (1993) in the domain of aerial objects using 50 images. With adapted parameters, the Jacobs module is overall the best choice for constraint based recognition. For fixed parameters, the Sarkar-Boyer module is the best in terms of recognition accuracy and indexing speedup. Etemadi et al.'s module performs equally well with fixed and adapted parameters while the Jacobs module is most sensitive to fixed and adapted parameter choices. The overall performance ranking of the modules is Jacobs, Sakar-Boyer, and Etemadi et al.
Model-based multi-objective analysis of ultrasound image sequences in prenatal diagnosis
Authors: H. Isil Bozma, Erdem Kemik
Venue: CVPR
Year: 1997

In this study, ultrasound image sequences of fetus head are examined for the bi-parietal diameter measurement. There are five parts of the fetus head that have to be seen clearly before such a measurement can be made. Our approach is based on model-based multi-objective analysis. For each part, an objective function encoding both data and anatomic constraints is formulated. The resulting multi-objective problem is then transformed into a problem of coupled differential equations and is then solved using numerical integration. Throughout the process, continuity principle is used between the frames so that the results of analysis on one frame can be used on the next frame-whenever possible.
Coupled hidden Markov models for complex action recognition
Authors: Matthew Brand, Nuria Oliver, Alex Pentland
Venue: CVPR
Year: 1997

We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm and a clear Bayesian semantics. However the Markovian framework makes strong restrictive assumptions about the system generating the signal-that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.
Deterioration detection for digital film restoration
Authors: Olivier Buisson, Bernard Besserer, Samia Boukir, F. Helt
Venue: CVPR
Year: 1997

This paper presents a robust technique to detect local deteriorations of old cinematographic films. This method relies on spatio-temporal information and combines two different detectors: a morphological detector which uses spatial properties of deteriorations, and a dynamic detector based on motion estimation techniques. Our deterioration detector has been validated on several film sequences and turned out to be a powerful tool for digital film restoration.
Stereo Coupled Active Contours
Authors: Tat-Jen Cham, Roberto Cipolla
Venue: CVPR
Year: 1997

We consider how tracking in stereo may be enhanced by coupling pairs of active contours in different views via affine epipolar geometry and various subsets of planar affine transformations, as well as by implementing temporal constraints imposed by curve rigidity. 3D curve tracking is achieved using a submanifold model, where it is shown how the coupling mechanisms can be decomposed to cater for fired and variable epipolar geometries. In the case of tracking planar curves, the canonical frame model is developed such that the various geometrical constraints needed in different situations may be efficiently selected. The results show that coupled active contours add consistency and robustness to tracking in stereo.
Local Blur Estimation and Super-Resolution
Authors: Ming-Chao Chiang, Terrance E. Boult
Venue: CVPR
Year: 1997

Until now, all super-resolution algorithms have presumed that the images were taken under the same illumination conditions. This paper introduces a new approach to super-resolution, based on edge models and a local blur estimate, which circumvents these difficulties. The paper presents the theory and the experimental results using the new approach.
A Divide-and-conquer Strategy in Shape from Shading Problem
Authors: Seong Ik Cho, Hideo Saito 0001, Shinji Ozawa
Venue: CVPR
Year: 1997

A divide-and-conquer strategy in shape from shading problem under fully perspective conditions is proposed for the information recovery of book surfaces. The whole recovery process is composed of three sequential steps: preprocessing, apparent shape recovery, and ortho-image generation. Pure shade images are extracted in the preprocessing step by introducing a phenomenological model of interreflection and by removing pigment parts from observed images. A recurrence relation is derived from the definition of mean slope in the discrete image. Theoretically, it becomes possible to recover unique shape without iteration using the derived equations in the case of a Lambertian cylinder. However, a feedback shape recovery process is implemented as a practical algorithm in order to overcome self-shadows. Results of simulations and real experiments show the properness and acceptability of the proposed strategy and implemented algorithms.
Multi-Image Focus of Attention for Rapid Site Model Construction
Authors: Robert T. Collins
Venue: CVPR
Year: 1997

A multi-image focus of attention mechanism has been developed that can quickly distinguish raised objects like buildings from structured background clutter typical to many aerial image scenarios. The underlying approach is the space-sweep stereo method, in which features from multiple images are backprojected onto a virtual, horizontal plane that is methodically swept through the scene. Back-projected gradient orientations from multiple images are highly correlated when they come from scene locations containing structural edges that are roughly horizontal, like building roofs and terrain; otherwise, they tend to be uniformly distributed. These observations are used to define a structural salience measure that can determine whether a given volume of space contains a statistically significant number of structural edges, without first performing precise reconstruction of those edges. The utility of structural salience for computing focus of attention regions is illustrated on sample data from Ft. Hood, Texas.
Face Detection With Information-Based Maximum Discrimination
Authors: Antonio Colmenarez, Thomas S. Huang
Venue: CVPR
Year: 1997

In this paper we present a visual learning technique that maximizes the discrimination between positive and negative examples in a training set. We demonstrate our technique in the context of face detection with complex background without color or motion information, which has proven to be a challenging problem. We use a family of discrete Markov processes to model the face and background patterns and estimate the probability models using the data statistics. Then, we convert the learning process into an optimization, selecting the Markov process that optimizes the information-based discrimination between the two classes. The detection process is carried out by computing the likelihood ratio using the probability model obtained from the learning procedure. We show that because of the discrete nature of these models, the detection process is at least two orders of magnitude less computationally expensive than neural network approaches. However, no improvement in terms of correct-answer/false-alarm tradeoff is achieved.
Depth from Scattering
Authors: Fábio Gagliardi Cozman, Eric Krotkov
Venue: CVPR
Year: 1997

Light power is affected when it crosses the atmosphere; there is a simple, albeit non-linear, relationship between the radiance of an image at any given wavelength and the distance between object and viewer. This phenomenon is called atmospheric scattering and has been extensively studied by physicists and meteorologists. We present the first analysis of this phenomenon from an image understanding perspective: we investigate a group of techniques for extraction of depth cues solely from the analysis of atmospheric scattering effects in images. Depth from scattering techniques are discussed for indoor and outdoor environments, and experimental tests with real images are presented. We have found that depth cues in outdoor scenes can be recovered with surprising accuracy and can be used as an additional information source for autonomous vehicles.