OpenRaiser/Intern-Atlas huggingface dataset:
Image Alignment for Precise Camera Fixation and Aim
Authors: Lambert E. Wixson, Jayakrishnan Eledath, Michael W. Hansen, Robert Mandelbaum, Deepam Mishra
Venue: CVPR
Year: 1998

Two important problems in camera control are how to keep a moving camera fixated on a target point, and how to precisely aim a camera, whose approximate pose is known, towards a given 3D position. This paper describes how electronic image alignment techniques can be used to solve these problems, as well as provide other benefits such as stabilized video. Hence, stabilized, fixated imagery is obtained despite large latencies in the control loop, even for simple control strategies. These techniques have been tested using an airborne camera and real-time affine image alignment.
Image Understanding from Thermal Emission Polarization
Authors: Lawrence B. Wolff, Andrew J. Lundberg, Renjie Tang
Venue: CVPR
Year: 1998

Existing polarization-based image understanding techniques use information only from reflected light. Apart from incandescent bodies thermally emitted light radiation from elements of a scene in the visible spectrum is insignificant. However, at longer wavelengths such as in the infrared thermal emission is typically quite prevalent from a number of scene elements of interest. FLIR imagery of both indoor and outdoor scenes reveals that many objects thermally emit a significant amount of radiation. Polarization from thermally emitting objects has been observed as long as 170 years ago from incandescent objects but since then there have only been a limited number of empirical investigations into this phenomenon. This paper presents a comprehensive model for explaining polarization of thermal emission from both rough and smooth surfaces, in agreement with empirical data, that can significantly enhance the image understanding of FLIR imagery. In particular it is possible to discern metal from dielectric materials under certain conditions, and from an accurate model for thermally emitted polarization it is possible to predictively model polarization signatures from CAD models of importance to automatic target recognition.
Incremental Tracking of Human Actions from Multiple Views
Authors: Masanobu Yamamoto, Akitsugu Sato, Satoshi Kawada, Takuya Kondo, Yoshihiko Osaki
Venue: CVPR
Year: 1998

This paper proposes a new method for model-based tracking of a human body in 3D motion from multiple views. The tracking is performed by estimating the pose increment of the body parts from multiple image sequences after establishing of fitting an articulated model to the human body at the initial frame. The pose increment can be obtained from solving a system of linear equations. This calculation does not depend on the number of view points. Experiments verify that the proposed method can avoid occlusion and visual degenerate from a particular view point, and track a human body in complicated motion.
Extraction and Classification of Visual Motion Patterns for Hand Gesture Recognition
Authors: Ming-Hsuan Yang 0001, Narendra Ahuja
Venue: CVPR
Year: 1998

We present a new method for extracting and classifying motion patterns to recognize hand gestures. First, motion segmentation of the image sequence is generated based on a multiscale transform and attributed graph matching of regions across frames. This produces region correspondences and their affine transformations. Second, color information of motion regions is used to determine skin regions. Third, human head and palm regions are identified based on the shape and size of skin areas in motion. Finally, affine transformations defining a region's motion between successive frames are concatenated to construct the region's motion trajectory. Gestural motion trajectories are then classified by a time-delay neural network trained with backpropagation learning algorithm. Our experimental results show that hand gestures can be recognized well using motion patterns.
3D Object Depth Recovery from Highlights Using Active Sensor and Illumination Control
Authors: Xilin Yi, Octavia I. Camps
Venue: CVPR
Year: 1998

Two approaches for 3D curved object reconstruction using active sensor and illumination control are proposed and compared to each other. In both cases, the highlight information is fully utilized rather than discarded, and knowledge of the object surface is not required. The first approach requires camera control only and recovers shape (depth) from highlights and occluding contours. The second approach requires both camera and illumination control and recovers 3D depth from highlights only.
Rotated Wedge Averaging Method for Junction Classification
Authors: Weichuan Yu, Konstantinos Daniilidis, Gerald Sommer
Venue: CVPR
Year: 1998

The computational cost of conventional filter methods for junction characterization is very high. This burden can be attenuated by using steerable filters. However, in order to achieve a high orientational selectivity to characterize complex junctions a large number of basis filters is necessary. From this results a yet too high computational effort for steerable filters. In this paper we present a new method for characterizing junctions which keeps the high orientational resolution and is computationally efficient. It is based on applying rotated copies of a wedge averaging filter and estimating the derivative with respect to the polar angle. The new method is compared with the steerable wedge filter method in experiments with real images. We show the superiority of our method as well as its adaptability to scale changes and robustness against noise.
Empirical Performance Analysis of Linear Discriminant Classifiers
Authors: Wen-Yi Zhao, Rama Chellappa, Nagaraj Nandhakumar
Venue: CVPR
Year: 1998

In face recognition literature, holistic template matching systems and geometrical local feature based systems have been pursued. In the holistic approach, PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are popular ones. More recently, the combination of PCA and LDA has been proposed as a superior alternative over pure PCA and LDA. In this paper, we illustrate the rationales behind these methods and the pros and cons of applying them to pattern classification task. A theoretical performance analysis of LDA suggests applying LDA over the principal components from the original signal space or the subspace. The improved performance of this combined approach is demonstrated through experiments conducted on both simulated data and real data.
Qualitative and Quantitative Car Tracking from a Range Image Sequence
Authors: Liang Zhao, Chuck Thorpe
Venue: CVPR
Year: 1998

In this paper, we present a car tracking system which provides quantitative and qualitative motion estimates of the tracked car simultaneously from a moving observer. First, we construct three motion models (constant velocity, constant acceleration, and turning) to describe the qualitative motion of a moving car. Then the models are incorporated into the Extended Kalman Filters to perform quantitative tracking. Finally, we develop an Extended Interacting Multiple Model (EIMM) algorithm to manage the switching between models and to output both qualitative and quantitative motion estimates of the tracked car. Accurate motion modeling and efficient model management result in a high performance tracking system. The experimental results on simulated and real data demonstrate that our tracking system is reliable and robust, and runs in real-time. The multiple motion representations make the system useful in various autonomous driving tasks. 1 Introduction Vehicle tracking is an important a...
Model-Based Target Recognition in Pulsed Ladar Imagery
Authors: Qinfen Zheng, Sandor Z. Der, Rama Chellappa
Venue: CVPR
Year: 1998

A pulsed laser radar (ladar) based object recognition system with applications to automatic target recognition is reported. The approach used is to fit the sensed range images to the range templates extracted using laser physics based simulation of Computer Aided Design target models. A projection based pre-screener filters out more than 80 percent of candidate templates. An M of N pixel matching scheme for internal shape matching combined with a silhouette matching scheme is used for recognition. The system has been blind tested on a data set containing 276 real ladar images of military vehicles at various orientations and different ranges. The system achieves above 90 percent accuracy in recognition of 0.4 meters resolution ladar images.
Stochastic Computation of Medial Axis in Markov Random Fields
Authors: Song Chun Zhu
Venue: CVPR
Year: 1998

In this paper the computation of medial axis is posed as a statistical inference problem not as a mathematical transform. This method provides answers to two essential problems in computing the medial axis representation. I) Prior knowledge are adopted for axes and junctions so that the axes around junctions become well defined. II) A stochastic jump-diffusion process is proposed for estimating medial axis in a Markov random field. We argue that the stochastic algorithm for computing medial axis is compatible with existing algorithms for image segmentation, such as snake and region competition. Thus it provides a new direction for computing medial axis from real textured images. Experiments ale demonstrated on both synthetic and real shapes.
Linear Self-Calibration of a Rotating and Zooming Camera
Authors: Lourdes de Agapito, Eric Hayman, Richard I. Hartley
Venue: CVPR
Year: 1999

A linear self-calibration method is given for computing the calibration of a stationary but rotating camera. The internal parameters of the camera are allowed to vary from image to image, allowing for zooming (change of focal length) and possible variation of the principal point of the camera. In order for calibration to be possible some constraints must be placed on the calibration of each image. The method works under the minimal assumption of zero-skew (rectangular pixels), or the more restrictive but reasonable conditions of square pixels, known pixel aspect ratio, and known principal point. Being linear the algorithm is extremely rapid, and avoids the convergence problems characteristic of iterative algorithms.
Factorization as a Rank 1 Problem
Authors: Pedro M. Q. Aguiar, José M. F. Moura
Venue: CVPR
Year: 1999

Tomasi and Kanade (1992) introduced the factorization method for recovering 3D structure from 2D video. In their formulation, the 3D shape and 3D motion are computed by using an SVD to approximate a matrix that is rank 3 in a noiseless situation. In this paper we reformulate the problem using the fact that the x and y coordinates of each feature are known from their projection onto the image plane in frame 1. We show how to compute the 3D shape, i.e., the relative depths z, and the 3D motion by a simple factorization of a matrix that is rank 1 in a noiseless situation. This allows the use of very fast algorithms even when using a large number of features and large number of frames. We also show how to accommodate confidence weights for the feature trajectories. This is done without additional computational cost by rewriting the problem as the factorization of a modified matrix.
Bias Field Estimation and Adaptive Segmentation of MRI Data Using a Modified Fuzzy C-Means Algorithm
Authors: Mohamed N. Ahmed, Sameh M. Yamany, Aly A. Farag, Thomas Moriarty
Venue: CVPR
Year: 1999

In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
Space-Variant Dynamic Neural Fields for Visual Attention
Authors: Ingo Ahrns, Heiko Neumann
Venue: CVPR
Year: 1999

In this paper we propose a new method for the fast application of dynamic neural fields (DNF) by utilizing the data reduction properties of space-variant active vision (SVAV). We apply this method to the control of visual attention. Dynamic neural fields have several advantages which are useful for many robot vision tasks, e.g. navigation or gaze-control. The dynamics of lateral interaction between neural units generates well-localized areas of high neural activation, which can be easily detected and used for behavior selection. The major focus of this paper is to drastically reduce the computational expense for the application of two-dimensional DNF. For that purpose, the dynamics of DNF is transformed into a space-variant field representation, defining a new type of DNF, namely space-variant dynamic neural fields (SVDNF). The effectiveness of the proposed method is demonstrated for our integrated monocular space-variant vision system. This system uses SVAV for real-time fixation control, depth-from motion estimation and SVDNF for the control of visual attention.
A New Multi-Level Framework for Deformable Contour Optimization
Authors: Yusuf Sinan Akgul, Chandra Kambhamettu
Venue: CVPR
Year: 1999

Application of dynamic programming to the deformable contours has many advantages, such as guaranteed optimality and numerical stability. However, long execution times of these methods almost always force researchers to use dynamic programming in combination with multiresolution methods. Multiresolution methods shorten the execution time by subsampling the original images after an application of a smoothing filter. However, this speedup comes at the expense of contour optimality due to the loss of details in the decreased resolution. In this paper, we present a new multi-level framework for deformable contour optimization, which can achieve faster optimization times and performs better than current multiresolution methods. To form the new levels, this method uses a very efficient algorithm to segment the original images with respect to the deformable contour external energy instead of subsampling. An exhaustive search on these segments is carried out by dynamic programming. A novel gradient descent algorithm is employed to find optimal internal energy for large image segments, where the external energy remains constant due to segmentation. We also introduce a new algorithm to pass the contour information more precisely between the levels. We present an analysis of time and performance comparisons with the current multiresolution methods by the experiments done on variety of medical images, which confirmed efficiency and accuracy of our framework.
Advances in Daylight Statistical Colour Modelling
Authors: Daniel C. Alexander
Venue: CVPR
Year: 1999

In this paper, parametric statistical modelling of distributions of colour camera data is discussed. A review is provided with some analysis of the properties of some common models, which are generally based on an assumption of independence of the chromaticity and intensity components of colour data. Results of an empirical comparison of the performance of various models are also reviewed. These results indicate that such models are not appropriate for situations other than highly controlled environments. In particular, they perform poorly for daylight imagery. Here, a modification to existing statistical colour models is proposed and the resultant new models are assessed using the same methodology as for the previous results. This simple modification, which is based on the inclusion of an ambient term in the underlying physical model, is shown to have a major impact on the performance of the models in less constrained daylight environments.
Elastic Matching of Diffusion Tensor MRIs
Authors: Daniel C. Alexander, James C. Gee, Ruzena Bajcsy
Venue: CVPR
Year: 1999

In this paper we discuss work on the use of diffusion tensor MRIs for inter-subject brain matching. A multiresolution elastic matching algorithm for spatial normalisation of 3D image data, has been adapted for use with diffusion tensor data. The hope is that by exploitation of the added information contained in the diffusion tensor image, improved anatomical matches can be found, particularly in white matter regions of the brain. Results show that by matching on the diffusion tensor alone, anisotropic regions of the brain (white matter) are aligned better than if the match is computed on standard structural data. However, there is a cost of some accuracy in the alignment of prominent features in more conventional, structural MRI data, such as PD-, TI- and T2-weighted imagery. If both types of data to drive the matching process, prominent features in both images can be aligned simultaneously. The motivation for this work lies in the characterisation of the distribution of brain images taken from population groups.
Combining Central and Peripheral Vision for Reactive Robot Navigation
Authors: Antonis A. Argyros, Fredrik Bergholm
Venue: CVPR
Year: 1999

In this paper we present a new method for vision-based, reactive robot navigation that enables a robot to move in the middle of the free space by exploiting both central and peripheral vision. The robot employs a forward-looking camera for central vision and two side-looking cameras for sensing the periphery of its visual field. The developed method combines the information acquired by this trinocular vision system and produces low-level motor commands that keep the robot in the middle of the free space. The approach follows the purposive vision paradigm in the sense that vision is not studied in isolation but in the context of the behaviors that the system is engaged as well as the environment and the robot's motor capabilities. It is demonstrated that by taking into account these issues, vision processing can be drastically simplified still giving rise to quite complex behaviors. The proposed method does not make strict assumptions about the environment, requires very low level information to be extracted from the images, produces a robust robot behavior and is computationally efficient. Results obtained by bath simulations and from a prototype on-line implementation demonstrate the effectiveness of the method.
Trajectory Triangulation of Lines: Reconstruction of a 3D point Moving along a Line from a Monocular Image Sequence
Authors: Shai Avidan, Amnon Shashua
Venue: CVPR
Year: 1999

We consider the problem of reconstructing the location of a moving 3D point seen from a monocular moving camera, i.e., to reconstruct moving objects from line-of-sight measurements only. Since the point is moving while the camera is moving, then even if the camera motion is known, it is impossible to reconstruct the 3D location of the point under general circumstances. However we show that if the point is moving along a straight line, then the parameters of the line (and hence the 3D position of the point at each time instance) can be uniquely recovered, and by linear methods, from at least 5 views. Consequently, we propose a new approach for dealing with dynamic scenes (rich with moving objects) in which once the camera motion is recovered, the 3D trajectory (straight line) of the moving target can be recovered-even when the moving target consists of a single point.
Global Measures of Coherence for Edge Detector Evaluation
Authors: Simon Baker, Shree K. Nayar
Venue: CVPR
Year: 1999

We propose a class of benchmarks for edge detector evaluation that require no ground truth. Each benchmark consists of a large number of images of a carefully designed scene for which we enforce a constraint on the edges, for example, that they are co-linear. We sample the space of edge appearances as densely as possible by capturing the images under widely varying imaging conditions. Not only do we change the viewing geometry and the illumination direction, but we also vary the camera parameters and the physical properties of the objects in the scene. We show that the degrees to which the constraints hold in the output edge-maps can be used as highly discriminating measures of edge detector performance. The code, images, and results which form our benchmarks are all available from the website http://www.cs.columbia.edu/CAVE/. The code and images enable a user to compare any new detector against several previous ones with minimal effort.
Edge Preserving Orientation Adaptive Filtering
Authors: Peter Bakker, Lucas J. van Vliet, Piet W. Verbeek
Venue: CVPR
Year: 1999

In this paper we describe a new stragegy for combining orientation adaptive filtering and edge preserving filtering. The filter adapts to the local orientation and avoids filtering across borders. The local orientation for steering the filter will be estimated in a fixed sized window which never contains two orientation fields. This can be achieved using generalized Kuwahara filtering. This filter selects from a set of fixed sized windows that contain the current pixel, the orientation of the window with the highest anisotropy. We compare out filter strategy with a multi-scale approach. We found that our filter strategy has a lower complexity and yields a constant improvement of the SNR.
Performance Prediction and Validation for Object Recognition
Authors: Michael Boshra, Bir Bhanu
Venue: CVPR
Year: 1999

This paper addresses the problem of predicting fundamental performance of vote-based object recognition using 2-D point features. It presents a method for predicting a tight lower bound on performance. Unlike previous approaches, the proposed method considers data-distortion factors, namely uncertainty, occlusion, and clutter, in addition to model similarity, simultaneously. The similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. This information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using synthetic aperture radar (SAR) data obtained under different depression angles and target configurations.
Recognition of Strings Using Nonstationary Markovian Models: An Application in ZIP Code Recognition
Authors: Djamel Bouchaffra, Venu Govindaraju, Sargur N. Srihari
Venue: CVPR
Year: 1999

This paper presents nonstationary Markovian models and their application to recognition of strings of tokens, such as ZIP codes in the US mailstream. Unlike traditional approaches where digits are simply recognized in isolation, the novelty of our approach lies in the manner in which recognitions scores along with domain specific knowledge about the frequency distribution of various combination of digits are all integrated into one unified model. The domain knowledge is derived from postal directory files. This data feeds into the models as n-grams statistics that are seamlessly integrated with recognition scores of digit images. We present the recognition accuracy (90%) achieved on a set of 20,000 ZIP codes.