OpenRaiser/Intern-Atlas huggingface dataset:
Component-based Face Detection
Authors: Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso A. Poggio
Venue: CVPR
Year: 2001

We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components Of a face. On the second level, a single classifier checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face. We propose a method for automatically learning components by using 3-D head models, This approach has the advantage that no manual interaction is required for choosing and extracting components. Experiments show that the component-based system is significantly more robust against rotations in depth than a comparable system trained on whole face patterns.
Adaptive Quasiconformal Kernel Metric for Image Retrieval
Authors: Douglas R. Heisterkamp, Jing Peng, H. K. Dai 0001
Venue: CVPR
Year: 2001

The paper presents a novel approach to ranking relevant images for retrieval. Distance in the feature space associated with a kernel is used to rank relevant images. An adaptive quasiconformal mapping based on relevance feedback is used to generate successive new kernels. The effect of the quasiconformal mapping is a change in the spatial resolution of the feature space. The spatial resolution around irrelevant samples is dilated, whereas the spatial resolution around relevant samples is contracted. This new space created by the quasiconformal kernel is used to measure the distance between the query and the images in the database. An interesting interpretation of the metric is found by looking at the Taylor series approximation to the original kernel. Then the squared distance in the feature space can be seen as a combination of a parzen window estimate of the squared Chi-squared distance and a weighted squared Euclidean distance. Experimental results using real-world data validate the efficacy of our method.
Contextual Classification by Entropy-Based Polygonization
Authors: Lothar Hermes, Joachim M. Buhmann
Venue: CVPR
Year: 2001

To improve the performance of pixel-wise classification results for remotely sensed imagery, several contextual classification schemes have been proposed that aim at avoiding classification noise by local averaging. These algorithms, however, bear the serious disadvantage of smoothing the segment boundaries and producing rounded segments that hardly match the true shapes. The authors present a novel contextual classification algorithm that overcomes these shortcomings. Using a hierarchical approach for generating a triangular mesh, it decomposes the image into a set of polygons that, in our application, represent individual land-cover types. Compared to classical contextual classification approaches, this method has the advantage of generating output that matches the intuitively expected type of segmentation. Besides, it achieves excellent classification results.
Dense Image Matching with Global and Local Statistical Criteria: A Variational Approach
Authors: Gerardo Hermosillo, Olivier D. Faugeras
Venue: CVPR
Year: 2001

We present two novel algorithms for multimodal, dense matching of two images using a variational approach. These algorithms complete and generalise our previous work by treating the case of semi-local energy functionals (G. Hermosillo et al., 2001). In brief, they are derived from the maximization of two statistical criteria (mutual information and correlation ratio) estimated from corresponding regions around each pixel (or voxel in the 3D case). As a second contribution, we present a result of existence and uniqueness of the solution of the abstract evolution problems associated to these algorithms, as well as those of the corresponding global algorithms. This is important since it shows the well-posedness of the problems to solve. We finish by showing some applications of our methods to one synthetic and four real examples.
3D Object Recognition from Range Images using Local Feature Histograms
Authors: Günter Hetzel, Bastian Leibe, Paul Levi, Bernt Schiele
Venue: CVPR
Year: 2001

The paper explores a view-based approach to recognize free-form objects in range images. We are using a set of local features that are easy to calculate and robust to partial occlusions. By combining those features in a multidimensional histogram, we can obtain highly discriminant classifiers without the need for segmentation. Recognition is performed using either histogram matching or a probabilistic recognition algorithm. We compare the performance of both methods in the presence of occlusions and test the system on a database of almost 2000 full-sphere views of 30 free-form objects. The system achieves a recognition accuracy above 93% on ideal images, and of 89% with 20% occlusion.
Matching, Reconstructing and Grouping 3D Lines From Multiple Views Using Uncertain Projective Geometry
Authors: Stephan Heuel, Wolfgang Förstner
Venue: CVPR
Year: 2001

We present a geometric method for (i) matching 2D line segments from multiple oriented images, (ii) optimally reconstructing 3D line segments and (iii) grouping 3D line segments to corners. The proposed algorithm uses two developments in combining projective geometry and statistics, which are described in this article: (i) the geometric entities points, lines and planes in 2D and 3D and their uncertainty are represented in homogeneous coordinates and new entities may be constructed including their propagated uncertainty. The construction can be performed directly or as an estimation. (ii) relations such as incidence, equality, parallelism and orthogonality between points, lines and planes can be tested statistically based on a given significance level. Using these tools, the resulting algorithm is straightforward and gives reasonable results. It is only based on geometric information and does not use any image intensities, though it can be extended to use other information. The matching of 3D lines does not need any thresholds other than a significance value for the hypotheses tests.
Geometric Distributions for Catadioptric Sensor Design
Authors: R. Andrew Hicks, Ronald K. Perline
Venue: CVPR
Year: 2001

Catadioptric sensors are visual sensors that employ lenses (dioptrics) and mirrors (catoptrics). We present a general method of catadioptric sensor design for realizing prescribed projections. Our method makes use of geometric distributions in. 3-dimensional space, which are generalizations of vector fields. The main idea is this: if one desires a reflective surface that will image the world in a certain way, then this condition determines the orientation of the tangent planes to the surface. Analytically, this means that the surface will then be determined by a pair of partial differential equations, which may or may not have a common solution. We show how to check if a common solution exists. If no common solution exists, we describe a method for obtaining optimal approximate solutions in a least-squares sense. As an example application, we construct a mirror that will give a panoramic view of a scene without any digital unwarping.
Alpha Channel Estimation in High Resolution Images and Image Sequences
Authors: Peter Hillman 0001, John Hannah 0001, David S. Renshaw
Venue: CVPR
Year: 2001

For motion picture special effects, it is often necessary to take a source image of an actor, segment the actor from the unwanted background, and then composite over a new background. The standard approach requires the unwanted background to be a blue screen. While this technique is capable of handling areas where the foreground blends into the background, the physical requirements present many practical problems. This paper presents an algorithm that requires minimal human interaction to segment motion picture resolution images and image sequences. We show that it can be used not only to segment badly lit or noisy blue screen images, but also to segment actors where the background is more varied.
Video to Reference Image Alignment in the Presence of Sparse Features and Appearance Change
Authors: David J. Hirvonen, Bogdan Matei, Richard P. Wildes, Steven C. Hsu
Venue: CVPR
Year: 2001

A robust, multi-frame, progressive refinement framework for registering narrow field of view video to reference imagery is presented. A major strength of the approach is its effectiveness in the presence of dissimilar video and reference image appearance. Normalized oriented energy image pyramids are employed to enable alignment of images with global visual dissimilarities, yet local feature commonality. Local matching is then applied coarse-to-fine, along four dimensions: spatial frequency, local support, search range, and model order (a robust parametric model fit is used to reject outliers at each iteration). Globally optimal multi-frame alignment is obtained with respect to several constraints: frame-to-reference local matches, recovered frame-to-frame motion, and optional a priori estimates of sensor pose. The framework is described in detail and applied to two examples: aerial video to geographic reference image alignment (georegistration) and retinal slit lamp video to fundus image alignment.
A Hierarchical Scheme for Representing Curves without Self-Intersections
Authors: Pong-Sik Ho, Min-Hwan Kim
Venue: CVPR
Year: 2001

A hierarchical representation scheme for planar curves is proposed, which provides natural approximation and efficient localization. The scheme uses the iterative end-point fit algorithm (or Douglas-Peucker algorithm (D.H. Douglas and T.K. Peucker, 1973)), but approximation errors are adjusted by force to eliminate unnatural approximations. The error adjustment just makes the approximation error of a node in a hierarchical tree less than or equal to those of its ancestor. A self-intersection resolving algorithm is also developed to remove self-intersections in all the possible approximations for a curve, which uses the cross-link technique to reduce computation time remarkably. In point of localization, the bounding area of a curve is represented as a minimum bounding octangle (MBO), which can enclose the curve compactly. The MBO satisfies the hierarchical inclusion property which is useful for hierarchical geometrical operations, such as the polygon intersection test and the point-inclusion test. Through several experiments, we found that the proposed scheme always generated curve approximations without self-intersections and provided more natural representations than other hierarchical representation schemes such as the strip tree, the arc tree, and the HAL tree.
Direct Appearance Models
Authors: Xinwen Hou, Stan Z. Li, HongJiang Zhang, QianSheng Cheng
Venue: CVPR
Year: 2001

Active appearance model (AAM), which makes ingenious use of both shape and texture constraints, is a powerful tool for face modeling, alignment and facial feature extraction under shape deformations and texture variations. However, as we show through our analysis and experiments, there exist admissible appearances that are not modeled by AAM and hence cannot be reached by AAM search; also the mapping from the texture subspace to the shape subspace is many-to-one and therefore a shape should be determined entirely by the texture in it. We propose a new appearance model, called direct appearance model (DAM), without combining from shape and texture as in AAM. The DAM model uses texture information directly in the prediction of the shape and in the estimation of position and appearance (hence the name DAM). In addition, DAM predicts the new face position and appearance based on principal components of texture difference vectors, instead of the raw vectors themselves as in AAM. These lead to the following advantages over AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) convergence and accuracy are improved, and (3) memory requirement is cut down to a large extent. The advantages are substantiated by comparative experimental results.
Geocoded Terrestrial Mosaics Using Pose Sensors and Video Registration
Authors: Stephen Hsu
Venue: CVPR
Year: 2001

The paper presents a complete algorithm for building geocoded terrestrial mosaics from aerial video accompanied by GPS/INS readings, without relying on ground survey points or reference imagery to provide geographic control. The 2D mosaic-to-video frame mappings are jointly estimated by bundle adjustment of constraints from pose sensor data and interframe registrations. Multiple-swath video collections are handled by automatically registering spatially adjacent frames across swaths. The proposed approach optimally combines the pose and interframe constraints for geocoding, unlike existing 2D mosaic techniques, while avoiding the complexity of 3D reconstruction. The method was validated on two highly dissimilar operating scenarios, with quantitative evaluation against ground truth supplied by known geocoded reference imagery. One test over hilly terrain found median mosaic continuity and geocoding errors of 1.6 m and 3.1 m, respectively, which is acceptable for many tasks. Characterization of such errors is essential for acceptance of this video mosaic process in critical geospatial applications.
A High-Resolution Panoramic Camer
Authors: Hong Hua, Narendra Ahuja
Venue: CVPR
Year: 2001

Wide field of view (FOV) and high resolution are two desirable properties in many vision-based applications such as tele-conferencing, surveillance, and robot navigation. In some applications, such as 3D reconstruction and rendering, it is also desired that all viewing directions share a single viewpoint, the entire FOV be imaged simultaneously, in real-time, and the depth of field be large. In this paper, we review such a panoramic camera proposed by Nalwa in 1996 that uses reflections off planar mirrors to achieve the first four of the aforementioned capabilities He uses a single mirror pyramid (SMP) and a number of cameras that point to the individual pyramid faces. Together the cameras yield a visual field having a width of 360 degrees and a height same as that of the individual cameras. We propose a double mirror-pyramid (DALP) design that still achieves a 360-degree FOV horizontally but doubles the vertical FOV. It retains the other three capabilities namely high resolution, a single apparent viewpoint across the entire FOV, and real-time panoramic capture. We specify the visual field mapping from the scene to the sensor realized by, the proposed camera. Finally, an implementation of the proposed DMP design is described and examples of preliminary panoramic images obtained are included.
Finding Folds: On the Appearance and Identification of Occlusion
Authors: Patrick S. Huggins, Hansen F. Chen, Peter N. Belhumeur, Steven W. Zucker
Venue: CVPR
Year: 2001

A natural sequel to edge detection is the interpretation of edges. This interpretation can provide useful information to various computer vision processes, including recognition, reconstruction, and tracking. In this paper we consider the problem of identifying occlusion edges in a single image. We examine the appearance of occlusion edges under variable illumination, both analytically and empirically, and find that the pattern of shading in the neighborhood of occlusion edges is a stable feature. Finally, we derive a filter for detecting occlusion and present the results of its application.
Extended Performance Graphs for Cluster Retrieval
Authors: Dionysius P. Huijsmans, Nicu Sebe
Venue: CVPR
Year: 2001

Performance evaluations in probabilistic information retrieval are often presented as precision-recall or precision-scope graphs avoiding the otherwise dominating effect of the embedding irrelevant fraction. However, precision and recall values as such offer an incomplete overview of the information retrieval system under study: information about system parameters like generality (the embedding of the relevant fraction), random performance, and the effect of varying the scope is missed In this paper two cluster performance graphs are presented In those cases where complete ground truth is available (both cluster size and database size) the cluster precision-recall (Cluster PR) graph and the generality-precision=recall graph are proposed.
Outlier Detection in Video Sequences under Affine Projection
Authors: Du Q. Huynh, Anders Heyden
Venue: CVPR
Year: 2001

A novel robust method for outlier detection in structure and motion recovery for affine cameras is presented. It is an extension of the well-known Tomasi-Kanade factorization technique (C. Tomasi T. Kanade, 1992) designed to handle outliers. It can also be seen as an importation of the LMedS technique or RANSAC into the factorization framework. Based on the computation of distances between subspaces, it relates closely with the subspace-based factorization methods for the perspective case presented by G. Sparr (1996) and others and the subspace-based-factorization for affine cameras with missing data by D. Jacobs (1997). Key features of the method presented are its ability to compare different subspaces and the complete automation of the detection and elimination of outliers. Its performance and effectiveness are demonstrated by experiments involving simulated and real video sequences.
Mixtures of Trees for Object Recognition
Authors: Sergey Ioffe, David A. Forsyth
Venue: CVPR
Year: 2001

Efficient detection of objects in images is complicated by variations of object appearance due to intra-class object differences, articulation, lighting, occlusions, and aspect variations. To reduce the search required for detection, we employ the bottom-up approach where we find candidate image features and associate some of them with parts of the object model. We represent objects as collections of local features, and would like to allow any of them to be absent, with only a small subset sufficient for detection;furthermore, our model should allow efficient correspondence search. We propose a model, Mixture of Trees, that achieves these goals. With a mixture of trees, we can model the individual appearances of the features, relationships among them, and the aspect, and handle occlusions. Independences captured in the model make efficient inference possible. In our earlier work, we have shown that mixtures of trees can be used to model objects with a natural tree structure, in the context of human tracking. Now we show that a natural tree structure is not required, and use a mixture of trees for both frontal and view-invariant face detection. We also show that by modeling faces as collections of features we can establish an intrinsic coordinate frame for a face, and estimate the out-of-plane rotation of a face.
Robust Online Appearance Models for Visual Tracking
Authors: Allan D. Jepson, David J. Fleet, Thomas F. El-Maraghi
Venue: CVPR
Year: 2001

Abstract—We propose a framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects. The model adapts to slowly changing appearance, and it maintains a natural measure of the stability of the observed image structure during tracking. By identifying stable properties of appearance, we can weight them more heavily for motion estimation, while less stable properties can be proportionately downweighted. The appearance model involves a mixture of stable image structure, learned over long time courses, along with two-frame motion information and an outlier process. An online EM-algorithm is used to adapt the appearance model parameters over time. An implementation of this approach is developed for an appearance model based on the filter responses from a steerable pyramid. This model is used in a motion-based tracking algorithm to provide robustness in the face of image outliers, such as those caused by occlusions, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.
Recovering the Geometry of Single Axis Motions by Conic Fitting
Authors: Guang Jiang, Hung-Tat Tsui, Long Quan, Shang-Qian Liu
Venue: CVPR
Year: 2001

In this paper, we propose a new approach for recovering 3D geometry from uncalibrated and unknown rotation angles of single axis motions. Unlike previous approaches, the computation of the trifocal tensor is not required. The new approach is based on fitting a conic to the corresponding points in different images of the same space point. It is then shown that the essential geometry of single axis motion is encoded by a conic. Since we are determining only 5 parameters from N views instead of 18 parameters from a subsequence of 3 views, our approach is much more simple and robust. The experiments on a real image sequence demonstrate the accuracy and robustness of the new algorithm.
A New Analysis Framework for Relevance Feedback-Driven Similarity Measure Refinement in Content-Based Image Retrieval
Authors: Brett C. Jones, D. Mitchell Wilkes
Venue: CVPR
Year: 2001

Many recent content-based image retrieval techniques utilize relevance feedback (RF) from the user to adjust the system response to better meet user expectations. One school of RF-based methods uses a weighted Minkowski distance metric to assess similarity, and adjusts the weights to refine query response. A new method of estimating these weight vectors is presented which outperforms existing methods, particularly for the important case of limited training data. A new objective function is presented for an iterative optimization routine which more closely aligns optimization goals with true system goals. A new analysis framework is presented in the derivation of this technique which is useful for understanding the limitations of many RF methods.
Camera Trajectory Estimation Using Inertial Sensor Measurements and Structure from Motion Results
Authors: Sang-Hack Jung, Camillo J. Taylor
Venue: CVPR
Year: 2001

This paper describes an approach to estimating the trajectory of a moving camera based on the measurements acquired with an inertial sensor and estimates obtained by applying a structure from motion algorithm to a small set of keyframes in the video sequence. The problem is formulated as an offline trajectory fitting task rather than an online integration problem. This approach avoids many of the issues usually associated with inertial estimation schemes. One of the main advantages of the proposed technique is that it can be applied in situations where approaches based on feature tracking would have significant difficulties. Results obtained by applying the procedure to extended sequences acquired with both conventional and omnidirectional cameras are presented.
An Open Problem in Matching Sets of 3D Lines
Authors: Behzad Kamgar-Parsi, Behrooz Kamgar-Parsi
Venue: CVPR
Year: 2001

Methods for matching sets of 3D lines depend on whether line lengths are finite or infinite. In terms of line lengths, three basic cases arise in matching sets of lines: (1) finite to finite, (2) finite to infinite, and (3) infinite to infinite. For cases 1 and 2, which have not been treated in the literature, we present convergent iterative algorithms that (almost) always find the best match. For case 3, O.D. Faugeras and M. Hebert (FH) (1986) have proposed a popular iterative method that cannot be guaranteed to converge. We present an alternative approach that does converge. However, we also show that neither the FH solution, nor our solution is invariant with respect to coordinate transforms, which renders any best match meaningless. Thus, a satisfactory solution to case 3 does not yet exist. We discuss the underlying problem, which is the representation of infinite lines, and suggest alternatives that may lead to an invariant solution.