OpenRaiser/Intern-Atlas huggingface dataset:
Shape and Appearance Context Modeling
Authors: Xiaogang Wang, Gianfranco Doretto, Thomas Sebastian, Jens Rittscher, Peter H. Tu
Venue: ICCV
Year: 2007

In this work we develop appearance models for computing the similarity between image regions containing deformable objects of a given class in realtime. We introduce the concept of shape and appearance context. The main idea is to model the spatial distribution of the appearance relative to each of the object parts. Estimating the model entails computing occurrence matrices. We introduce a generalization of the integral image and integral histogram frameworks, and prove that it can be used to dramatically speed up occurrence computation. We demonstrate the ability of this framework to recognize an individual walking across a network of cameras. Finally, we show that the proposed approach outperforms several other methods.
Multi-Camera Calibration with One-Dimensional Object under General Motions
Authors: Liang Wang 0021, Fuchao Wu, Zhanyi Hu
Venue: ICCV
Year: 2007

It is well known that in order to calibrate a single camera with a one-dimensional (1D) calibration object, the object must undertake some constrained motions, in other words, it is impossible to calibrate a single camera if the object motion is of general one. For a multi-camera setup, i.e., when the number of camera is more than one, can the cameras be calibrated by a 1D object under general motions? In this work, we prove that all cameras can indeed be calibrated and a calibration algorithm is also proposed and experimentally tested. In contrast to other multi-camera calibration method, no one calibrated "base" camera is needed. In addition, we show that for such multi-camera cases, the minimum condition of calibration and critical motions are similar to those of calibrating a single camera with 1D calibration object.
Semiautomatic registration between ground-level panoramas and an orthorectified aerial image for building modeling
Authors: Lu Wang 0013, Suya You, Ulrich Neumann
Venue: ICCV
Year: 2007

Aerial imagery and ground-level imagery are two complementary data sources for architectural modeling. How to integrate them is a critical issue in creating complete, photo-realistic and large-scale urban models. We describe a semiautomatic approach of detecting feature correspondences between ground-level images and the building footprint in an orthorectified aerial image. The ground-level images are stitched into panoramas in order to obtain a wide camera field of view. Line segments are extracted from ground-level images. Their corresponding segments on the building footprints are automatically detected through a voting process. Meanwhile the camera pose of the ground-level images is also obtained. Wrong correspondences are corrected through user interaction. Later, the height values of the building roof corners are computed and a piece-wise planar 3D model with photo-realistic facade and roof texture is then created.
Diffusion Maps Segmentation of Magnetic Resonance Q-Ball Imaging
Authors: Demian Wassermann, Maxime Descoteaux, Rachid Deriche
Venue: ICCV
Year: 2007

We present a Diffusion Maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well-known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent High Angular Resolution Diffusion Imaging (HARDI) such has Q-Ball Imaging (QBI) have been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maximum(a) agreeing with the underlying fiber population. In this paper, we use the ODF representation in a small set of spherical harmonic coefficients as input to the Diffusion Maps clustering method. We first show the advantage of using Diffusion Maps clustering over classical methods such as N-Cuts and Laplacian Eigenmaps. In particular, our ODF Diffusion Maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptative scale-space parameter. We also show that our ODF Diffusion Maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our method succeeds to separate fiber bundles and crossing regions whereas the DT- based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real brain dataset where we successfully segment the fiber bundles.
Interactive Offline Tracking for Color Objects
Authors: Yichen Wei, Jian Sun 0001, Xiaoou Tang, Heung-Yeung Shum
Venue: ICCV
Year: 2007

In this paper, we present an interactive offline tracking system for generic color objects. The system achieves 60- 100 fps on a 320 times 240 video. The user can therefore easily refine the tracking result in an interactive way. To fully exploit user input and reduce user interaction, the tracking problem is addressed in a global optimization framework. The optimization is efficiently performed through three steps. First, from user's input we train a fast object detector that locates candidate objects in the video based on proposed features called boosted color bin. Second, we exploit the temporal coherence to generate multiple object trajectories based on a global best-first strategy. Last, an optimal object path is found by dynamic programming.
Using High-Level Visual Information for Color Constancy
Authors: Joost van de Weijer 0001, Cordelia Schmid, Jakob Verbeek
Venue: ICCV
Year: 2007

We propose to use high-level visual information to improve illuminant estimation. Several illuminant estimation approaches are applied to compute a set of possible illuminants. For each of them an illuminant color corrected image is evaluated on the likelihood of its semantic content: is the grass green, the road grey, and the sky blue, in correspondence with our prior knowledge of the world. The illuminant resulting in the most likely semantic composition of the image is selected as the illuminant color. To evaluate the likelihood of the semantic content, we apply probabilistic latent semantic analysis. The image is modelled as a mixture of semantic classes, such as sky, grass, road, and building. The class description is based on texture, position and color information. Experiments show that the use of high-level information improves illuminant estimation over a purely bottom-up approach. Furthermore, the proposed method is shown to significantly improve semantic class recognition performance.
Enabling Users to Guide the Design of Robust Model Fitting Algorithms
Authors: Matthias Wimmer, Freek Stulp, Bernd Radig
Venue: ICCV
Year: 2007

Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is to determine the model parameters that best match a given image, which corresponds to finding the global optimum of the objective function. When it comes to the robustness and accuracy of fitting models to specific images, humans still outperform state- of-the-art model fitting systems. Therefore, we propose a method in which non-experts can guide the process of designing model fitting algorithms. In particular, this paper demonstrates how to obtain robust objective functions for face model fitting applications, by learning their calculation rules from example images annotated by humans. We evaluate the obtained function using a publicly available image database and compare it to a recent state-of-the-art approach in terms of accuracy.
Extracting Spatiotemporal Interest Points using Global Information
Authors: Shu-Fai Wong, Roberto Cipolla
Venue: ICCV
Year: 2007

Local spatiotemporal features or interest points provide compact but descriptive representations for efficient video analysis and motion recognition. Current local feature extraction approaches involve either local filtering or entropy computation which ignore global information (e.g. large blobs of moving pixels) in video inputs. This paper presents a novel extraction method which utilises global information from each video input so that moving parts such as a moving hand can be identified and are used to select relevant interest points for a condensed representation. The proposed method involves obtaining a small set of subspace images, which can synthesise frames in the video input from their corresponding coefficient vectors, and then detecting interest points from the subspaces and the coefficient vectors. Experimental results indicate that the proposed method can yield a sparser set of interest points for motion recognition than existing methods.
Robust Visual Tracking Using the Time-Reversibility Constraint
Authors: Hao Wu 0014, Rama Chellappa, Aswin C. Sankaranarayanan, Shaohua Kevin Zhou
Venue: ICCV
Year: 2007

Visual tracking is a very important front-end to many vision applications. We present a new framework for robust visual tracking in this paper. Instead of just looking forward in the time domain, we incorporate both forward and backward processing of video frames using a novel time-reversibility constraint. This leads to a new minimization criterion that combines the forward and backward similarity functions and the distances of the state vectors between the forward and backward states of the tracker. The new framework reduces the possibility of the tracker getting stuck in local minima and significantly improves the tracking robustness and accuracy. Our approach is general enough to be incorporated into most of the current tracking algorithms. We illustrate the improvements due to the proposed approach for the popular KLT tracker and a search based tracker. The experimental results show that the improved KLT tracker significantly outperforms the original KLT tracker. The time-reversibility constraint used for tracking can be incorporated to improve the performance of optical flow, mean shift tracking and other algorithms.
Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection
Authors: Bo Wu 0001, Ramakant Nevatia
Venue: ICCV
Year: 2007

Detection of object of a known class is a fundamental problem of computer vision. The appearance of objects can change greatly due to illumination, view point, and articulation. For object classes with large intra-class variation, some divide-and-conquer strategy is necessary. Tree structured classifier models have been used for multi-view multi- pose object detection in previous work. This paper proposes a boosting based learning method, called Cluster Boosted Tree (CBT), to automatically construct tree structured object detectors. Instead of using predefined intra-class sub- categorization based on domain knowledge, we divide the sample space by unsupervised clustering based on discriminative image features selected by boosting algorithm. The sub-categorization information of the leaf nodes is sent back to refine their ancestors' classification functions. We compare our approach with previous related methods on several public data sets. The results show that our approach outperforms the state-of-the-art methods.
Consistent Correspondence between Arbitrary Manifold Surfaces
Authors: Huai-Yu Wu, Chunhong Pan, Qing Yang 0002, Songde Ma
Venue: ICCV
Year: 2007

We propose a novel framework for consistent correspondence between arbitrary manifold meshes. Different from most existing methods, our approach directly maps the connectivity of the source mesh onto the target mesh without needing to segment input meshes, thus effectively avoids dealing with unstable extreme conditions (e.g. complex boundaries or high genus). In this paper, firstly, a novel mean-value Laplacian fitting scheme is proposed, which aims at computing a shape-preserving (conformal) correspondence directly in 3D-to-3D space, efficiently avoiding local optimum caused by the nearest-point search, and achieving good results even with only a few marker points. Secondly, we introduce a vertex relocation and projection approach, which refines the initial fitting result in the way of local conformity. Each vertex of the initial result is gradually projected onto the target model's surface to ensure a complete surface match. Furthermore, we provide a fast and effective approach to automatically detect critic points in the context of consistent correspondence. By fitting these critic points that capture the important features of the target mesh, the output compatible mesh matches the target mesh's profiles quite well. Compared with previous approaches, our scheme is robust, fast, and convenient, thus suitable for common applications.
Joint Affinity Propagation for Multiple View Segmentation
Authors: Jianxiong Xiao, Jingdong Wang 0001, Ping Tan, Long Quan
Venue: ICCV
Year: 2007

A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse affinity propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised affinity propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches.
Dynamic Cascades for Face Detection
Authors: Rong Xiao 0003, Huaiyi Zhu, He Sun 0003, Xiaoou Tang
Venue: ICCV
Year: 2007

In this paper, we propose a novel method, called "dynamic cascade", for training an efficient face detector on massive data sets. There are three key contributions. The first is a new cascade algorithm called "dynamic cascade ", which can train cascade classifiers on massive data sets and only requires a small number of training parameters. The second is the introduction of a new kind of weak classifier, called "Bayesian stump", for training boost classifiers. It produces more stable boost classifiers with fewer features. Moreover, we propose a strategy for using our dynamic cascade algorithm with multiple sets of features to further improve the detection performance without significant increase in the detector's computational cost. Experimental results show that all the new techniques effectively improve the detection performance. Finally, we provide the first large standard data set for face detection, so that future researches on the topic can be compared on the same training and testing set.
Diffusion Tensor Image Smoothing Using Efficient and Effective Anisotropic Filtering
Authors: Qing Xu 0003, Adam W. Anderson, John C. Gore, Zhaohua Ding
Venue: ICCV
Year: 2007

To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging, an anisotropic smoothing algorithm is presented for reducing noise in diffusion tensor images efficiently and effectively. The presented algorithm is based on previous anisotropic diffusion filtering, which is implemented with a straightforward but inefficient explicit numerical scheme. The main contribution of this paper is to improve the performance of the previous method considerably by using unconditionally stable and second order time accurate semi-implicit scheme. Our new method needs only few or even one iteration to achieve better smoothed images than what is generated by tens of iterations of the previous method, which makes it more attractive to practical use. Experiments with simulated and in vivo data have demonstrated the advantage of our new algorithm for denoising diffusion tensor images in terms of efficiency and effectiveness.
Learning Motion Correlation for Tracking Articulated Human Body with a Rao-Blackwellised Particle Filter
Authors: Xinyu Xu, Baoxin Li
Venue: ICCV
Year: 2007

Inference in 3D articulated human body tracking is challenging due to the high dimensionality and nonlinearity of the parameter-space. We propose a particle filter with Rao-Blackwellisation which marginalizes part of the state variables by exploiting the correlation between the right-side and the left-side joint Euler angles. The correlation is naturally induced by the symmetric and repetitive patterns in specific human activities. A novel algorithm is proposed to learn the correlation from the training data using partial least square regression. The learned correlation is then used as motion prior in designing the Rao-Blackwellised particle filter, which estimates only one group of state variables using the Monte Carlo method, leaving the other group being exactly computed through an analytical filter that utilizes the learned motion correlation. We evaluate the effectiveness of the motion correlation for 3D articulated human body tracking. The accuracy of the proposed 3D tracker is quantitatively assessed based on the distance between the true and the estimated marker positions. Extensive experiments with multi-camera walking sequences from the HumanEva-I/II data set show that (i) the proposed tracker achieves significantly lower estimation error than both the annealed particle filter and the standard particle filter; and (ii) the learned motion correlation generalizes well to motion performed by subjects other than the training subject.
Segmentation using Meta-texture Saliency
Authors: Yaser Yacoob, Larry S. Davis
Venue: ICCV
Year: 2007

We address segmentation of an image into patches that have an underlying salient surface-roughness. Three intrinsic images are derived: reflectance, shading and meta- texture images. A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.
Coplanar Shadowgrams for Acquiring Visual Hulls of Intricate Objects
Authors: Shuntaro Yamazaki, Srinivasa G. Narasimhan, Simon Baker, Takeo Kanade
Venue: ICCV
Year: 2007

Acquiring 3D models of intricate objects (like tree branches, bicycles and insects) is a hard problem due to severe self-occlusions, repeated thin structures and surface discontinuities. In theory, a shape-from-silhouettes (SFS) approach can overcome these difficulties and use many views to reconstruct visual hulls that are close to the actual shapes. In practice, however, SFS is highly sensitive to errors in silhouette contours and the calibration of the imaging system, and therefore not suitable for obtaining reliable shapes with a large number of views. We present a practical approach to SFS using a novel technique called coplanar shadowgram imaging, that allows us to use dozens to even hundreds of views for visual hull reconstruction. Here, a point light source is moved around an object and the shadows (silhouettes) cast onto a single background plane are observed. We characterize this imaging system in terms of image projection, reconstruction ambiguity, epipolar geometry, and shape and source recovery. The coplanarity of the shadowgrams yields novel geometric properties that are not possible in traditional multi-view camera- based imaging systems. These properties allow us to derive a robust and automatic algorithm to recover the visual hull of an object and the 3D positions of light source simultaneously, regardless of the complexity of the object. We demonstrate the acquisition of several intricate shapes with severe occlusions and thin structures, using 50 to 120 views.
3D Model based Object Class Detection in An Arbitrary View
Authors: Pingkun Yan, Saad M. Khan, Mubarak Shah
Venue: ICCV
Year: 2007

In this paper, a novel object class detection method based on 3D object modeling is presented. Instead of using a complicated mechanism for relating multiple 2D training views, the proposed method establishes spatial connections between these views by mapping them directly to the surface of 3D model. The 3D shape of an object is reconstructed by using a homographic framework from a set of model views around the object and is represented by a volume consisting of binary slices. Features are computed in each 2D model view and mapped to the 3D shape model using the same homographic framework. To generalize the model for object class detection, features from supplemental views are also considered. A codebook is constructed from all of these features and then a 3D feature model is built. Given a 2D test image, correspondences between the 3D feature model and the testing view are identified by matching the detected features. Based on the 3D locations of the corresponding features, several hypotheses of viewing planes can be made. The one with the highest confidence is then used to detect the object using feature location matching. Performance of the proposed method has been evaluated by using the PASCAL VOC challenge dataset and promising results are demonstrated.
Optimization Algorithms for Labeling Brain Sulci Based on Graph Matching
Authors: Faguo Yang, Frithjof Kruggel
Venue: ICCV
Year: 2007

Graph matching techniques are widely used in pattern recognition problems such as scene description, finger print identification, or face recognition. In this paper, we put forward two optimization methods for graph matching and compare them in the context of brain sulcus identification. The first approach is based on a constraint search in a neighborhood; the second uses a genetic algorithm for optimization. Experiments demonstrate that both methods yield satisfactory identification rates, however, the second method is more general and easier to adapt to similar problems.
A General Discriminant Model for Color Face Recognition
Authors: Jian Yang 0003, Chengjun Liu
Venue: ICCV
Year: 2007

This paper presents a general discriminant model (GDM) for color face recognition. The GDM model involves two sets of variables: a set of color component combination coefficients for color image representation and a set of projection basis vectors for image discrimination. An iterative whitening-maximization (IWM) algorithm is designed to find the optimal solution of the model. The proposed algorithm is further extended to generate three color components (like the three color components of RGB color images) for further improving the face recognition performance. Experiments using the face recognition grand challenge (FRGC) database and the biometric experimentation environment (BEE) system show the effectiveness of the proposed model and algorithm. In particular, for the most challenging FRGC version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, the proposed method achieves the face verification rate (ROC III) of 74.91% at the false accept rate of 0.1%.