OpenRaiser/Intern-Atlas huggingface dataset:
Pursuing Informative Projection on Grassmann Manifold
Authors: Dahua Lin, Shuicheng Yan, Xiaoou Tang
Venue: CVPR
Year: 2006

Inspired by the underlying relationship between classification capability and the mutual information, in this paper, we first establish a quantitative model to describe the information transmission process from feature extraction to final classification and identify the critical channel in this propagation path, and then propose a Maximum Effective Information Criteria for pursuing the optimal subspace in the sense of preserving maximum information that can be conveyed to final decision. Considering the orthogonality and rotation invariance properties of the solution space, we present a Conjugate Gradient method constrained on a Grassmann manifold to exploit the geometric traits of the solution space for enhancing the efficiency of optimization. Comprehensive experiments demonstrate that the framework integrating the Maximum Effective Information Criteria and Grassmann manifold-based optimization method significantly improves the classification performance.
Neighborhood Aided Implicit Active Contours
Authors: Huafeng Liu 0003, Yunmei Chen, Wufan Chen, Pengcheng Shi
Venue: CVPR
Year: 2006

We have developed a geometric deformable model that employs neighborhood influence to achieve robust segmentation for noisy and broken edges. The fundamental power of this strategy rests with the explicitly combination of regional inter-point constraints, image forces, and a priori boundary information for each geometric contour point within its adaptively determined local influence domain. This formulation thus naturally unifies the essences of the geometric and parametric snakes through automatic local scale selection, and exhibits their respective fundamental strengths of allowing stable boundary detection when the edge information is weak and possibly discontinuous, while maintaining the abilities to handle topological changes during front evolution. In particular, this paper presents an implementation of the method through local integration of the level set function and the image/prior-driven evolution forces, where the resulting partial differential equation is solved numerically using standard finite difference method. Experimental results on synthetic and real images demonstrate its superior performance.
Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval
Authors: Yi Liu, Hongbin Zha, Hong Qin 0001
Venue: CVPR
Year: 2006

This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bag-of-words model. The main contributions of this paper are threefold as follows: 1) a partial shape dissimilarity measure is proposed to rank shapes according to their distances to the input query, without using any timeconsuming alignment procedure; 2) by applying the probabilistic text analysis technique, a highly compact representation "Shape Topics" and accompanying algorithms are developed for efficient 3D partial shape retrieval, the mapping from "Shape Topics" to "object categories" is established using multi-class SVMs; and 3) a method for evaluating the performance of partial shape retrieval is proposed and tested. To our best knowledge, very few existing methods are able to perform well online partial shape retrieval for large 3D shape repositories. Our experimental results are expected to validate the efficacy and effectiveness of our novel approach.
Deformation Modeling for Robust 3D Face Matching
Authors: Xiaoguang Lu, Anil K. Jain 0001
Venue: CVPR
Year: 2006

Human face recognition based on 3D surface matching is promising for overcoming the limitations of current 2D image-based face recognition systems. The 3D shape is invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates for each subject in a large database (associated with various expressions) is not practical. We present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously. Experimental results demonstrate that the proposed matching scheme based on deformation modeling improves the matching accuracy.
Dynamics Based Robust Motion Segmentation
Authors: Roberto Lublinerman, Mario Sznaier, Octavia I. Camps
Venue: CVPR
Year: 2006

In this paper we consider the problem of segmenting multiple rigid motions using multi-frame point correspondence data. The main idea of the method is to group points according to the complexity of the model required to explain their relative motion. Intuitively, this formalizes the idea that points on the same rigid share more modes of motion (for instance a common translation or rotation) than points on different objects, leading to less complex models. By exploiting results from systems theory, the problem of estimating the complexity of the underlying model is reduced to simply computing the rank of a matrix constructed from the correspondence data. This leads to a simple segmentation algorithm, computationally no more expensive than a sequence of SVDs. Since the proposed method exploits both spatial and temporal constraints, is less sensitive to the effect of noise or outliers than approaches that rely solely on factorizations of the measurements matrix. In addition, the method can also naturally handle "degenerate cases", e.g. cases where the objects partially share motion modes. These results are illustrated using several examples involving both degenerate and non-degenerate cases.
Learning Patch Dependencies for Improved Pose Mismatched Face Verification
Authors: Simon Lucey, Tsuhan Chen
Venue: CVPR
Year: 2006

Most pose robust face verification algorithms, which employ 2D appearance, rely heavily on statistics gathered from offline databases containing ample facial appearance variation across many views. Due to the high dimensionality of the face images being employed, the validity of the assumptions employed in obtaining these statistics are essential for good performance. In this paper we assess three common approaches in 2D appearance pose mismatched face recognition literature. In our experiments we demonstrate where these approaches work and fail. As a result of this analysis, we additionally propose a new algorithm that attempts to learn the statistical dependency between gallery patches (i.e. local regions of pixels) and the whole appearance of the probe image. We demonstrate improved performance over a number of leading 2D appearance face recognition algorithms.
Automatic Landmark Tracking and its Application to the Optimization of Brain Conformal Mapping
Authors: Lok Ming Lui, Yalin Wang 0001, Tony F. Chan, Paul M. Thompson
Venue: CVPR
Year: 2006

Anatomical features on cortical surfaces are usually represented by landmark curves, called sulci/gyri curves. These landmark curves are important information for neuroscientists to study brain diseases and to match different cortical surfaces. Manual labelling of these landmark curves is time-consuming, especially when there is a large set of data. In this paper, we proposed to trace the landmark curves on cortical surfaces automatically based on the principal directions. Suppose we are given the global conformal parametrization of a cortical surface, By fixing two endpoints, the anchor points, we propose to trace the landmark curves iteratively on the spherical/rectangular parameter domain along the principal direction. Consequently, the landmark curves can be mapped onto the cortical surface. To speed up the iterative scheme, a good initial guess of the landmark curve is necessary. We proposed a method to get a good initialization by extracting the high curvature region on the cortical surface using the Chan-Vese segmentation. This involves solving a PDE on the manifold using our global conformal parametrization technique. Experimental results show that the landmark curves detected by our algorithm closely resemble to those manually labelled curves. As an application, we used these automatically labelled landmark curves to build average cortical surfaces with an optimized brain conformal mapping method. Experimental results show our method can help automatically matching brain cortical surfaces.
Structure and View Estimation for Tomographic Reconstruction: A Bayesian Approach
Authors: Satya P. Mallick, Sameer Agarwal 0001, David J. Kriegman, Serge J. Belongie, Bridget Carragher, Clinton S. Potter
Venue: CVPR
Year: 2006

This paper addresses the problem of reconstructing the density of a scene from multiple projection images produced by modalities such as x-ray, electron microscopy, etc. where an image value is related to the integral of the scene density along a 3D line segment between a radiation source and a point on the image plane. While computed tomography (CT) addresses this problem when the absolute orientation of the image plane and radiation source directions are known, this paper addresses the problem when the orientations are unknown - it is akin to the structure-from-motion (SFM) problem when the extrinsic camera parameters are unknown. We study the problem within the context of reconstructing the density of protein macro-molecules in Cryogenic Electron Microscopy (cryo-EM), where images are very noisy and existing techniques use several thousands of images. In a non-degenerate configuration, the viewing planes corresponding to two projections, intersect in a line in 3D. Using the geometry of the imaging setup, it is possible to determine the projections of this 3D line on the two image planes. In turn, the problem can be formulated as a type of orthographic structure from motion from line correspondences where the line correspondences between two views are unreliable due to image noise. We formulate the task as the problem of denoising a correspondence matrix and present a Bayesian solution to it. Subsequently, the absolute orientation of each projection is determined followed by density reconstruction. We show results on cryo-EM images of proteins and compare our results to that of Electron Micrograph Analysis (EMAN) - a widely used reconstruction tool in cryo-EM.
A Geodesic Active Contour Framework for Finding Glass
Authors: Kenton McHenry, Jean Ponce
Venue: CVPR
Year: 2006

This paper addresses the problem of finding objects made of glass (or other transparent materials) in images. Since the appearance of glass objects depends for the most part on what lies behind them, we propose to use binary criteria ("are these two regions made of the same material?") rather than unary ones ("is this glass?") to guide the segmentation process. Concretely, we combine two complementary measures of affinity between regions made of the same material and discrepancy between regions made of different ones into a single objective function, and use the geodesic active contour framework to minimize this function over pixel labels. The proposed approach has been implemented, and qualitative and quantitative experimental results are presented.
Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis
Authors: Chris McIntosh, Ghassan Hamarneh
Venue: CVPR
Year: 2006

We present a novel approach to the segmentation and analysis of vasculature from volumetric medical image data. Our method is an adoption and significant extension of deformable organisms, an artificial life framework for medical image analysis that complements classical deformable models with high-level, anatomically-driven control mechanisms. We extend deformable organisms to 3D, model their bodies as tubular spring-mass systems, and equip them with a new repertoire of sensory modules, behavioral routines, and decision making strategies. The result is a new breed of robust deformable organisms, vessel crawlers, that crawl along vasculature in 3D images, accurately segmenting vessel boundaries, detecting and exploring bifurcations, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation and analysis of vascular structures in both noisy synthetic and real medical image data.
Real-time Hand Pose Recognition Using Low-Resolution Depth Images
Authors: Zhenyao Mo, Ulrich Neumann
Venue: CVPR
Year: 2006

Gesture recognition methods based on intensity or color images often suffer from low efficiency and lack of robustness. In this paper, we employ a new laser-based camera that produces reliable low-resolution depth images at video rates. By decomposing and recognizing hand poses as finger states (finger poses and finger inter-relations), we achieve robust hand pose recognition in real-time (30 frames/second).
Impact of Dynamics on Subspace Embedding and Tracking of Sequences
Authors: Kooksang Moon, Vladimir Pavlovic 0001
Venue: CVPR
Year: 2006

In this paper we study the role of dynamics in dimensionality reduction problems applied to sequences. We propose a new family of marginal auto-regressive (MAR) models that describe the space of all stable auto-regressive sequences, regardless of their specific dynamics. We apply the MAR class of models as sequence priors in probabilistic sequence subspace embedding problems. In particular, we consider a Gaussian process latent variable approach to dimensionality reduction and show that the use of MAR priors may lead to better estimates of sequence subspaces than the ones obtained by traditional non-sequential priors. We then propose a learning method for estimating nonlinear dynamic system (NDS) models that utilizes the new MAR priors. The utility of the proposed methods is demonstrated on several synthetic datasets as well as on the task of tracking 3D articulated figures in monocular image sequences.
Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics
Authors: Vlad I. Morariu, Octavia I. Camps
Venue: CVPR
Year: 2006

Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D trajectories and fully take advantage of the additional information available from the multiple sensors. Previous approaches to the "correspondence across views" problem include matching features, using camera calibration information, and computing homographies between views under the assumption that the world is planar. However, it can be difficult to match features across significantly different views. Furthermore, calibration information is not always available and planar world hypothesis can be too restrictive. In this paper, a new approach is presented for matching correspondences based on the use of nonlinear manifold learning and system dynamics identification. The proposed approach does not require similar views, calibration nor geometric assumptions of the 3D environment, and is robust to noise and occlusion. Experimental results demonstrate the use of this approach to generate and predict views in cases where identity labels become ambiguous.
Real-Time Semi-Automatic Segmentation Using a Bayesian Network
Authors: Eric N. Mortensen, Jin Jia
Venue: CVPR
Year: 2006

This paper presents a semi-automatic segmentation technique called Bayesian cut that formulates object boundary detection as the most probable explanation (MPE) of a Bayesian network’s joint probability distribution. A two-layer Bayesian network structure is formulated from a planar graph representing a watershed segmentation of an image. The network’s prior probabilities encode the confidence that an edge in the planar graph belongs to an object boundary while the conditional probability tables (CPTs) enforce global contour properties of closure and simplicity (i.e., no self-intersections). Evidence, in the form of one or more connected boundary points, allows the network to compute the MPE with minimal user guidance. The constraints imposed by CPTs also permit a linear-time algorithm to compute the MPE, which in turn allows for interactive segmentation where every mouse movement recomputes the MPE based on the current cursor position and displays the corresponding segmentation.
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
Authors: Vidhya Navalpakkam, Laurent Itti
Venue: CVPR
Year: 2006

Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.
Improved watershed segmentation using water diffusion and local shape priors
Authors: Hieu Tat Nguyen, Qiang Ji
Venue: CVPR
Year: 2006

The watershed algorithm has many nice properties in terms of robustness to image noise, topology, and effective handling non-rigid deformations. It also has drawbacks including oversegmentation and lack of regularization. We present new methods to overcome these drawbacks. We propose a novel region merging algorithm based on the water diffusion principle. Starting with a large number of markers, lakes formed around the markers are merged in the order they meet during the immersion. The merging is not performed immediately but delayed until the amount of water diffusion between two lakes is significant enough to overwhelm the small lake. The delay makes the merging result robust to "leaks" in object boundaries, when weak edges could trigger the merging of object into the background as in traditional methods. Regularization is achieved by imposing priors of local shape configurations. Local shape features are extracted from Gaussian derivatives of the object indicator function. The ensemble of shape features at multiple scales increases representation power. These features are used to incorporate smoothness and domain knowledge into the evolution of region boundaries in the watershed algorithm. The method has been successfully applied to segmentation of worms.
Robust multi-target tracking using spatio-temporal context
Authors: Hieu Tat Nguyen, Qiang Ji, Arnold W. M. Smeulders
Venue: CVPR
Year: 2006

In multi-target tracking, the maintaining of the correct identity of targets is challenging. In the presented tracking method, accurate target identification is achieved by incorporating the appearance information of the spatial and temporal context of each target. The spatial context of a target involves local background and nearby targets. The first contribution of the paper is to provide a new discriminative model for multi-target tracking with the embedded classification of each target against its context. As a result, the tracker not only searches for the image region similar to the target but also avoids latching on nearby targets or on a background region. The temporal context of a target includes its appearances seen during tracking in the past. The past appearances are used to train a probabilistic PCA that is used as the measurement model of the target at the present. As the second contribution, we develop a new incremental scheme for probabilistic PCA. It can update accurately the full set of parameters including a noise parameter still ignored in related literature. The experiments show robust tracking performance under the condition of severe clutter, occlusions and pose changes.
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
Authors: Peter Nillius, Josephine Sullivan, Stefan Carlsson
Venue: CVPR
Year: 2006

Multi-target tracking requires locating the targets and labeling their identities. The latter is a challenge when many targets, with indistinct appearances, frequently occlude one another, as in football and surveillance tracking. We present an approach to solving this labeling problem. When isolated, a target can be tracked and its identity maintained. While, if targets interact this is not always the case. This paper assumes a track graph exists, denoting when targets are isolated and describing how they interact. Measures of similarity between isolated tracks are defined. The goal is to associate the identities of the isolated tracks, by exploiting the graph constraints and similarity measures. We formulate this as a Bayesian network inference problem, allowing us to use standard message propagation to find the most probable set of paths in an efficient way. The high complexity inevitable in large problems is gracefully reduced by removing dependency links between tracks. We apply the method to a 10 min sequence of an international football game and compare results to ground truth.
Incremental learning of object detectors using a visual shape alphabet
Authors: Andreas Opelt, Axel Pinz, Andrew Zisserman
Venue: CVPR
Year: 2006

We address the problem of multiclass object detection. Our aims are to enable models for new categories to benefit from the detectors built previously for other categories, and for the complexity of the multiclass system to grow sublinearly with the number of categories. To this end we introduce a visual alphabet representation which can be learnt incrementally, and explicitly shares boundary fragments (contours) and spatial configurations (relation to centroid) across object categories. We develop a learning algorithm with the following novel contributions: (i) AdaBoost is adapted to learn jointly, based on shape features; (ii) a new learning schedule enables incremental additions of new categories; and (iii) the algorithm learns to detect objects (instead of categorizing images). Furthermore, we show that category similarities can be predicted from the alphabet. We obtain excellent experimental results on a variety of complex categories over several visual aspects. We show that the sharing of shape features not only reduces the number of features required per category, but also often improves recognition performance, as compared to individual detectors which are trained on a per-class basis.