OpenRaiser/Intern-Atlas huggingface dataset:
Deformable Template and Distribution Mixture-Based Data Modeling for the Endocardial Contour Tracking in an Echographic Sequence
Authors: Max Mignotte, Jean Meunier
Venue: CVPR
Year: 1999

We present a new method to shape-based segmentation of deformable anatomical structures in medical images and validate this approach by detecting and tracking the endocardial border in an echographic image sequence. To this end, a global prior knowledge of the endocardial contour is captured by a prototype template with a set of admissible deformations to take into account its inherent natural variability over time. In this approach, the data likelihood model rely on an accurate statistical modeling of the grey level distribution of each class present in the image. The parameters of this distribution mixture are given by a preliminary estimation step which takes into account the distribution shape of each class. Then the tracking problem is stated in a Bayesian framework where it ends up as an optimization problem. This one is then efficiently solved by a genetic algorithm combined with a steepest ascent procedure. This technique has been successfully applied on synthetic images and on a real echocardiographic image sequence.
Recognizing Color Patterns Irrespective of Viewpoint and Illumination
Authors: Florica Mindru, Theo Moons, Luc Van Gool
Venue: CVPR
Year: 1999

New invariant features are presented that can be used for the recognition of planar color patterns such as labels, logos, pictograms, etc., irrespective of the viewpoint or the illumination conditions, and without the need for error prone contour extraction. The new features are based on moments of powers of the intensities in the individual color bands and combinations thereof. These moments implicitly characterize the shape, the intensity and the color distribution of the pattern in a uniform manner. The paper gives a classification of all functions of such moments which are invariant under both affine deformations of the pattern (thus achieving viewpoint invariance) as well as linear changes of the intensity values of the color bands (hence, coping with changes in the irradiance pattern due to different lighting conditions and/or viewpoints). The discriminant power and classification performance of the new invariants for color pattern recognition is tested on a data set of images of outdoors advertising panels. A comparison to moment invariants presented in literature is included as well.
Radiometric Self Calibration
Authors: Tomoo Mitsunaga, Shree K. Nayar
Venue: CVPR
Year: 1999

A simple algorithm is described that computes the radiometric response function of an imaging system, from images of an arbitrary scene taken using different exposures. The exposure is varied by changing either the aperture setting or the shutter speed. The algorithm does not require precise estimates of the exposures used. Rough estimates of the ratios of the exposures (e.g. F-number settings on an inexpensive lens) are sufficient for accurate recovery of the response function as well as the actual exposure ratios. The computed response function is used to fuse the multiple images into a single high dynamic range radiance image. Robustness is tested using a variety of scenes and cameras as well as noisy synthetic images generated using 100 randomly selected response curves. Automatic rejection of image areas that have large vignetting effects or temporal scene variations make the algorithm applicable to not just photographic but also video cameras.
Toboggan-Based Intelligent Scissors with a Four-Parameter Edge Model
Authors: Eric N. Mortensen, William A. Barrett
Venue: CVPR
Year: 1999

Intelligent Scissors is an interactive image segmentation tool that allows a user to select piece-wise globally optimal contour segments that correspond to a desired object boundary. We present a new and faster method of computing the optimal path by over-segmenting the image using tobogganing and then imposing a weighted planar graph on top of the resulting region boundaries. The resulting region-based graph is many times smaller than the previous pixel-based graph, thus providing faster graph searches and immediate user interaction. Further tobogganing provides an new systematic and predictable framework for computing edge model parameters, allowing subpixel localization as well as a measure of edge blur.
Pose Clustering with Density Estimation and Structural Constraints
Authors: Simon Moss, Edwin R. Hancock
Venue: CVPR
Year: 1999

This paper describes a statistical framework for object alignment by pose clustering. The idea underlying pose clustering is to transform the alignment process from the image domain to that of the appropriate transformation parameters. It commence by taking k-tuples from the primitive-sets for the model and the data. The size of the k-tuples is such that there are sufficient measurements available to estimate the full-set of transformation parameters. By pairing each k-tuple in the model and each k-tuple in the data, a set of transformation parameter estimates or alignment votes is accumulated. The work reported here draws on three ideas. Firstly, we estimate maximum likelihood alignment parameters by using the the EM algorithm to fit a mixture model to the set of transformation parameter votes. Secondly, we control the order of the underlying structure model using a minimum description length criterion. Finally, we limit problems of combinatorial background by imposing structural constraints on the k-tuples.
Integrating Shape from Shading and Range Data Using Neural Networks
Authors: Mostafa Gadal-Haqq M. Mostafa, Sameh M. Yamany, Aly A. Farag
Venue: CVPR
Year: 1999

This paper presents a framework for integrating multiple sensory data, sparse range data and dense depth maps from shape from shading in order to improve the 3D reconstruction of visible surfaces of 3D objects. The integration process is based on propagating the error difference between the two data sets by fitting a surface to that difference and using it to correct the visible surface obtained from shape from shading. A feedforward neural network is used to fit a surface to the sparse data. We also study the use of the extended Kalman filter for supervised learning and compare it with the backpropagation algorithm. A performance analysis is done to obtain the best neural network architecture and learning algorithm. It is found that the integration of sparse depth measurements has greatly enhanced the 3D visible surface obtained from shape from shading in terms of metric measurements.
3D Deformable Image Matching Using Multiscale Minimization of Global Energy Functions
Authors: Olivier Musse, Fabrice Heitz, Jean-Paul Armspach
Venue: CVPR
Year: 1999

This paper presents a hierarchical framework to perform deformable matching of three dimensional (3D) images. 3D shape deformations are parameterized at different scales, using a decomposition of the continuous deformation vector field over a sequence of nested subspaces, generated from a single scaling function. The parameterization of the field enables to enforce smoothness and differentiability constraints without performing explicit regularization. A global energy function, depending on the reference image and the transformed one, is minimized via a coarse-to-fine algorithm over this multiscale decomposition. Contrary to standard multigrid approaches, no reduction of image data is applied. The continuous field of deformation is always sampled at the same resolution, ensuring that the same energy function is handled at each scale and that the energy decreases at each step of the minimization.
Shape from Recognition and Learning: Recovery of 3-D Face Shapes
Authors: Dibyendu Nandy, Jezekiel Ben-Arie
Venue: CVPR
Year: 1999

In this paper a novel framework for the recovery of 3D surfaces of faces from single images is developed. The underlying principle is shape from recognition, i.e. the idea that pre-recognizing face parts can constrain the space of possible solutions to the image irradiance equation, thus allowing robust recovery of the 3D structure of a specific part. Shape recovery of the recognized part is based on specialized backpropagation based neural networks, each of which is employed in the recovery of a particular face part. Representation using principal components allows to efficiently encode classes of objects such as nose, lips, etc. The specialized networks are designed and trained to map the principal component coefficients of the shading images to another set of principal component coefficients that represent the corresponding 3D surface shapes. A method for integrating recovered 3D surface regions by minimizing the sum squared error in overlapping areas is also derived. Quantitative analysis of the reconstruction of the surface parts show relatively small errors indicating that the method is robust and accurate. The recovery of a complete face is performed by minimal squared error merging efface parts.
A New Structure--From--Motion Ambiguity
Authors: John Oliensis
Venue: CVPR
Year: 1999

This paper demonstrates the existence of a generic approximate ambiguity in Euclidean structure from motion (SFM) which applies to scenes with large depth variation. In projective SFM the ambiguity is absent, but the maximum-likelihood reconstruction is more likely to have occasional very large errors. The analysis gives a semi-quantitative characterization of the least-squares error surface over a domain complementary to that analyzed by Jepson/Heeger/Maybank.
Toward a Scale-Space Aspect Graph: Solids of Revolution
Authors: Sung-Il Pae, Jean Ponce
Venue: CVPR
Year: 1999

This paper addresses the problem of constructing the scale-space aspect graph of a solid of revolution whose surface is the zero set of a polynomial volumetric density undergoing a Gaussian diffusion process. Equations for the associated visual event surfaces are derived, and polynomial curve tracing techniques are used to delineate these surfaces. An implementation and examples are presented, and limitations as well as extensions of the proposed approach are discussed.
Exploiting the Dependencies in Information Fusion
Authors: Hao Pan 0002, Zhi-Pei Liang, Thomas S. Huang
Venue: CVPR
Year: 1999

This paper presents a novel approach for multisensory information fusion in the Bayesian inference framework. Specifically, under the maximum entropy principle, a formula is derived for estimating the joint probabilities of multisensory signals. The formula uses appropriate mapping functions to reflect the dependencies among multisensory signals. Selection of the mappings is guided by the maximum mutual information criterion. In addition, an algorithm is proposed for linear mappings of Gaussian random variables. Experiments on simulated Gaussian data and video/audio signals have been carried out. Preliminary results demonstrate that the proposed method can significantly improve the recognition accuracy for this type of tasks.
Unifying Boundary and Region-Based Information for Geodesic Active Tracking
Authors: Nikos Paragios, Rachid Deriche
Venue: CVPR
Year: 1999

This paper addresses the problem of tracking several non-rigid objects over a sequence of frames acquired from a static observer using boundary and region-based information under a coupled geodesic active contour framework. Given the current frame, a statistical analysis is performed on the observed difference frame which provides a measurement that distinguishes between the static and mobile regions in terms of conditional probabilities. An objective function is defined that integrates boundary-based and region-based module by seeking curves that attract the object boundaries and maximize the a posteriori segmentation probability on the interior curve regions with respect to intensity and motion properties. This function is minimized using a gradient descent method. The associated Euler-Lagrange PDE is implemented using a Level-Set approach, where a very fast front propagation algorithm evolves the initial curve towards the final tracking result. Very promising experimental results are provided using real video sequences.
Geodesic Active Contours for Supervised Texture Segmentation
Authors: Nikos Paragios, Rachid Deriche
Venue: CVPR
Year: 1999

This paper presents a variational method for supervised texture segmentation which is based on ideas coming from the curve propagation theory. We assume that a preferable texture pattern is known (e.g., the pattern that we want to distinguish from the rest of the image). The textured feature space is generated by filtering the input and the preferable pattern image using Gabor filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by minimizing a Geodesic Active Contour Model objective function where the boundary-based information is expressed via discontinuities on the statistical space associated with the multi-modal textured feature space. This function is minimized using a gradient descent method where the obtained PDE is implemented using a level set approach, that handles naturally the topological changes. Finally a fast method is used for the level set implementation. The performance of our method is demonstrated on a variety of synthetic and real textured images.
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
Authors: Vladimir Pavlovic 0001, Brendan J. Frey, Thomas S. Huang
Venue: CVPR
Year: 1999

We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying time-series data such as object trajectories. A hidden Markov model (HMM) of discrete actions is coupled with a linear dynamical system (LDS) model of continuous trajectory motion. This combination allows us to model both the discrete and continuous causes of trajectories such as human gestures. The model is derived using a rich theoretical corpus from the Bayesian network literature. This allows us to use an approximate structured variational inference technique to solve the otherwise intractable inference of action and system states. Using the same DBN framework we show how to learn the mixed-state model parameters from data. Experiments show that with high statistical confidence the mixed-state DBNs perform favorably when compared to decoupled HMM/LDS models on the task of recognizing human gestures made with a computer mouse.
Stereo Panorama with a Single Camera
Authors: Shmuel Peleg, Moshe Ben-Ezra
Venue: CVPR
Year: 1999

Full panoramic images, covering 360 degrees, can be created either by using panoramic cameras or by mosaicing together many regular images. Creating panoramic views in stereo, where one panorama is generated for the left eye, and another panorama is generated for the right eye is more problematic. Earlier attempts to mosaic images from a rotating pair of stereo cameras faced severe problems of parallax and of scale changes. A new family of multiple viewpoint image projections, the Circular Projections, is developed. Two panoramic images taken using such projections can serve as a panoramic stereo pair. A system is described to generates a stereo panoramic image using circular projections from images or video taken by a single rotating camera. The system works in real-time on a PC. It should be noted that the stereo images are created without computation of 3D structure, and the depth effects are created only in the viewer's brain.
Toward Learning Visual Discrimination Strategies
Authors: Justus H. Piater, Roderic A. Grupen
Venue: CVPR
Year: 1999

Humans learn strategies for visual discrimination through interaction with their environment. Discrimination skills are refined as demanded by the task at hand, and are not a priori determined by any particular feature set. Tasks are typically incompletely specified and evolve continually. This work presents a general framework for learning visual discrimination that addresses some of these characteristics. It is based on an infinite combinatorial feature space consisting of primitive features such as oriented edgels and texture signatures, and compositions thereof. Features are progressively sampled from this space in a simple-to-complex manner. A simple recognition procedure queries learned features one by one and rules out candidate object classes that do not sufficiently exhibit the queried feature. Training images are presented sequentially to the learning system, which incrementally discovers features for recognition. Experimental results on two databases of geometric objects illustrate the applicability of the framework.
Independent Motion: The Importance of History
Authors: Robert Pless, Tomás Brodský, Yiannis Aloimonos
Venue: CVPR
Year: 1999

We consider a problem central in aerial visual surveillance applications-detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames and the definition of a constraint violation function which acts as an indication of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filler. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence.
Histogram Clustering for Unsupervised Image Segmentation
Authors: Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann 0001
Venue: CVPR
Year: 1999

This paper introduces a novel statistical mixture model for probabilistic grouping of distributional (histogram) data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for the unsupervised segmentation of textured images based on local distributions of Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms.
A Computational Approach to Semantic Event Detection
Authors: Richard J. Qian, Niels C. Haering, M. Ibrahim Sezan
Venue: CVPR
Year: 1999

We propose a three-level video event detection algorithm and apply it to animal hunt detection in wildlife documentaries. The first level extracts texture, color and motion features, and detects motion blobs. The mid-level employs a neural network to verify whether the motion blobs belong to objects of interest. This level also generates shot summaries in terms of intermediate-level descriptors which combine low-level features from the first level and contain results of mid-level, domain specific inferences made on the basis of shot features. The shot summaries are then used by a domain-specific inference process at the third level to detect the video segments that contain events of interest, e.g., hunts. Event based video indexing, summarization and browsing are among the applications of the proposed approach.
Simultaneous Depth Recovery and Image Restoration from Defocused Images
Authors: A. N. Rajagopalan 0001, Subhasis Chaudhuri
Venue: CVPR
Year: 1999

We propose a method for simultaneous recovery of depth and restoration of scene intensity, given two defocused images of a scene. The space-variant blur parameter and the focused image of the scene are modeled as Markov random fields (MRFs). Line fields are included to preserve discontinuities. The joint posterior distribution of the blur parameter and the intensity process is examined for locality property and we derive an important result that the posterior is again Markov. The result enables us to obtain the maximum a posterior (MAP) estimates of the blur parameter and the focused image, within reasonable computational limits. The estimates of depth and the quality of the restored image are found to be quite good, even in the presence of discontinuities.
A Framework for Learning Query Concepts in Image Classification
Authors: Aparna Lakshmi Ratan, Oded Maron, W. Eric L. Grimson, Tomás Lozano-Pérez
Venue: CVPR
Year: 1999

In this paper, we adapt the Multiple Instance Learning paradigm using the Diverse Density algorithm as a way of modeling the ambiguity in images in order to learn "visual concepts" that can be used to classify new images. In this framework, a user labels an image as positive if the image contains the concept. Each example image is a bag of instances (sub-images) where only the bag is labeled-not the individual instances (sub-images). From a small collection of positive and negative examples, the system learns the concept and uses it to retrieve images that contain the concept from a large database. The learned "concepts" are simple templates that capture the color, texture and spatial properties of the class of images. We introduced this method earlier in the domain of natural scene classification using simple, low resolution sub-images as instances. In this paper, we extend the bag generator (the mechanism which takes an image and generates a set of instances) to generate more complex instances using multiple cues on segmented high resolution images. We show that this method can be used to learn certain object class concepts (e.g. cars) in addition, to natural scenes.
The Quotient Image: Class Based Recognition and Synthesis under Varying Illumination Conditions
Authors: Tammy Riklin-Raviv, Amnon Shashua
Venue: CVPR
Year: 1999

The paper addresses the problem of "class-based" recognition and image-synthesis with varying illumination. The class-based synthesis and recognition tasks are defined as follows: given a single input image of an object, and a sample of images with varying illumination conditions of other objects of the same general class, capture the equivalence relationship (by generation of new images or by invariants) among all images of the object corresponding to new illumination conditions. The key result in our approach is based on a definition of an illumination invariant signature image, we call the "quotient" image, which enables an analytic generation of the image space with varying illumination from a single input image and a very small sample of other objects of the class-in our experiments as few as two objects. In many cases the recognition results outperform by far conventional methods and the image-synthesis is of remarkable quality considering the size of the database of example images and the mild pre-process required for making the algorithm work.