OpenRaiser/Intern-Atlas huggingface dataset:
Robust null space representation and sampling for view-invariant motion trajectory analysis
Authors: Xu Chen 0019, Dan Schonfeld, Ashfaq A. Khokhar
Venue: CVPR
Year: 2008

In this paper, we propose a novel robust retrieval and classification system for video and motion events based on null space representation. In order to analyze the robustness of the system, the perturbed null operators have been derived with the first order perturbation theory. Subsequently, the sensitivity of the null operators is discussed in terms of the error ratio and the SNR respectively. Meanwhile, the normwise bounds and componentwise bounds based on classical matrix perturbation theory are presented and discussed. Given the perturbation, uniform sampling are proposed for the convergence of the SNR and Poisson sampling are proposed for the convergence of the error ratio in the mean sense by choosing the rate parameter the same order as the number of samples. The simulation results are provided to demonstrate the effectiveness and robustness of our system in motion event indexing, retrieval and classification that is invariant to affine transformation due to camera motions.
A robust descriptor based on Weber's Law
Authors: Jie Chen 0001, Shiguang Shan, Guoying Zhao 0001, Xilin Chen 0001, Wen Gao 0001, Matti Pietikäinen
Venue: CVPR
Year: 2008

Inspired by Weber’s Law, this paper proposes a simple, yet very powerful and robust local descriptor, Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends on not only the change of a stimulus (such as sound, lighting, et al.) but also the original intensity of the stimulus. Specifically, WLD consists of two components: its differential excitation and orientation. A differential excitation is a function of the ratio between two terms: One is the relative intensity differences of its neighbors against a current pixel; the other is the intensity of the current pixel. An orientation is the gradient orientation of the current pixel. For a given image, we use the differential excitation and the orientation components to construct a concatenated WLD histogram feature. Experimental results on Brodatz textures show that WLD impressively outperforms the other classical descriptors (e.g., Gabor). Especially, experimental results on face detection show a promising performance. Although we train only one classifier based on WLD features, the classifier obtains a comparable performance to state-of-the-art methods on MIT+CMU frontal face test set, AR face dataset and CMU profile test set.
Calibration of an Articulated Camera System
Authors: Junzhou Chen 0001, Kin Hong Wong
Venue: CVPR
Year: 2008

Multiple Camera Systems (MCS) have been widely used in many vision applications and attracted much attention recently. There are two principle types of MCS, one is the Rigid Multiple Camera System (RMCS); the other is the Articulated Camera System (ACS). In a RMCS, the relative poses (relative 3-D position and orientation) between the cameras are invariant. While, in an ACS, the cameras are articulated through movable joints, the relative pose between them may change. Therefore, through calibration of an ACS we want to find not only the relative poses between the cameras but also the positions of the joints in the ACS. Although calibration methods for RMCS have been extensively developed during the past decades, the studies of ACS calibration are still rare. In this paper, two ACS calibration methods are proposed. The first one uses the feature correspondences between the cameras in the ACS. The second one requires only the ego-motion information of the cameras and can be used for the calibration of the non-overlapping view ACS. In both methods, the ACS is assumed to have performed general transformations in a static environment. The efficiency and robustness of the proposed methods are tested by simulation and real experiments. In the real experiment, the intrinsic and extrinsic parameters of the ACS are calibrated using the same image sequences, no extra data capturing step is required. The corresponding trajectory is recovered and illustrated using the calibration results of the ACS. To our knowledge, we are the first to study the calibration of ACS.
Robust dual motion deblurring
Authors: Jia Chen, Lu Yuan, Chi-Keung Tang, Long Quan
Venue: CVPR
Year: 2008

This paper presents a robust algorithm to deblur two consecutively captured blurred photos from camera shaking. Previous dual motion deblurring algorithms succeeded in small and simple motion blur and are very sensitive to noise. We develop a robust feedback algorithm to perform iteratively kernel estimation and image deblurring. In kernel estimation, the stability and capability of the algorithm is greatly improved by incorporating a robust cost function and a set of kernel priors. The robust cost function serves to reject outliers and noise, while kernel priors, including sparseness and continuity, remove ambiguity and maintain kernel shape. In deblurring, we propose a novel and robust approach which takes two blurred images as input to infer the clear image. The deblurred image is then used as feedback to refine kernel estimation. Our method can successfully estimate large and complex motion blurs which cannot be handled by previous dual or single image motion deblurring algorithms. The results are shown to be significantly better than those of previous approaches.
Adaptive and compact shape descriptor by progressive feature combination and selection with boosting
Authors: Cheng Chen 0023, Yueting Zhuang, Jun Xiao 0001, Fei Wu 0001
Venue: CVPR
Year: 2008

Many types of shape descriptors have been proposed for 2D shape analysis, but most of them consist of component features that are not adapted to specific problems. This has two drawbacks. First, computation is wasted on the irrelevant components; second, the accuracy is impaired. This paper proposes an effective method that generates compact descriptors adapted to specific problems in hand, where each component of the new descriptor is a linear combination of the components in some classic descriptors. A progressive strategy is used to construct and select the most suitable linear combinations in successive rounds, where a variant of Adaboost is employed to ensure the optimum of the selected combinations in each round. Experiments show that our method effectively generates adaptive and compact descriptors for typical applications such as shape classification and retrieval.
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
Authors: Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang Zhang
Venue: CVPR
Year: 2008

We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this as a structure learning task and our strategy is to learn and combine basic POM's that make use of complementary image cues. Each POM has algorithms for inference and parameter learning, but: (i) the structure of each POM is unknown, and (ii) the inference and parameter learning algorithm for a POM may be impractical without additional information. We address these problems by a novel structure induction procedure which uses knowledge propagation to enable POM's to provide information to other POM's and "teach them" (which greatly reduced the amount of supervision required for training). In particular, we learn a POM-IP defined on interest points using weak supervision [1, 2] and use this to train a POM- mask, defined on regional features, which yields a combined POM which performs segmentation/localization. This combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large datasets which show that the full POM is invariant to scale and rotation of the object (for learning and inference) and performs inference rapidly. In addition, we show that we can apply POM's to learn objects classes (i.e. when there are several objects and the identity of the object in each image is unknown). We emphasize that these models can match between different objects from the same category and hence enable object recognition.
Local tensor descriptor from micro-deformation analysis
Authors: Bangsheng Cheng
Venue: CVPR
Year: 2008

This paper proposes a novel method called micro-deformation analysis to analyze and describe local image structures. This method is a general analytic tool and can be applied to any high-dimensional scalar or vector functions. We derive the tensor matrix from this method as the descriptor to represent the information within local image patches. Our experimental results suggest that we can design low-dimensional local tensor descriptors with performance comparable to the popular SIFT descriptor, which is the state-of-the-art feature descriptor used for object recognition and categorization.
A deformable local image descriptor
Authors: Hong Cheng 0002, Zicheng Liu 0001, Nanning Zheng 0001, Jie Yang 0001
Venue: CVPR
Year: 2008

This paper presents a novel local image descriptor that is robust to general image deformations. A limitation with traditional image descriptors is that they use a single support region for each interest point. For general image deformations, the amount of deformation for each location varies and is unpredictable such that it is difficult to choose the best scale of the support region. To overcome this difficulty, we propose to use multiple support regions of different sizes surrounding an interest point. A feature vector is computed for each support region, and the concatenation of these feature vectors forms the descriptor for this interest point. Furthermore, we propose a new similarity measure model, Local-to-Global Similarity (LGS) model, for point matching that takes advantage of the multi-size support regions. Each support region acts as a ‘weak’ classifier and the weights of these classifiers are learned in an unsupervised manner. The proposed approach is evaluated on a number of images with real and synthetic deformations. The experiment results show that our method outperforms existing techniques under different deformations.
The patch transform and its applications to image editing
Authors: Taeg Sang Cho, Moshe Butman, Shai Avidan, William T. Freeman
Venue: CVPR
Year: 2008

We introduce the patch transform, where an image is broken into non-overlapping patches, and modifications or constraints are applied in the “patch domain”. A modified image is then reconstructed from the patches, subject to those constraints. When no constraints are given, the reconstruction problem reduces to solving a jigsaw puzzle. Constraints the user may specify include the spatial locations of patches, the size of the output image, or the pool of patches from which an image is reconstructed. We define terms in a Markov network to specify a good image reconstruction from patches: neighboring patches must fit to form a plausible image, and each patch should be used only once. We find an approximate solution to the Markov network using loopy belief propagation, introducing an approximation to handle the combinatorially difficult patch exclusion constraint. The resulting image reconstructions show the original image, modified to respect the user’s changes. We apply the patch transform to various image editing tasks and show that the algorithm performs well on real world images.
Unsupervised feature selection via distributed coding for multi-view object recognition
Authors: C. Mario Christoudias, Raquel Urtasun, Trevor Darrell
Venue: CVPR
Year: 2008

Object recognition accuracy can be improved when information from multiple views is integrated, but information in each view can often be highly redundant. We consider the problem of distributed object recognition or indexing from multiple cameras, where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive. In this scenario, it is desirable to avoid sending redundant visual features from multiple views. Traditional supervised feature selection approaches are inapplicable as the class label is unknown at each camera. In this paper we propose an unsupervised multi-view feature selection algorithm based on a distributed coding approach. With our method, a Gaussian process model of the joint view statistics is used at the receiver to obtain a joint encoding of the views without directly sharing information across encoders. We demonstrate our approach on recognition and indexing tasks with multi-view image databases and show that our method compares favorably to an independent encoding of the features from each camera.
Automatic symmetry plane estimation of bilateral objects in point clouds
Authors: Benoît Combès, Robin Hennessy, John Waddington, Neil Roberts, Sylvain Prima
Venue: CVPR
Year: 2008

In this paper, the problem of estimating automatically the symmetry plane of bilateral objects (having perfect or imperfect mirror symmetry) in point clouds is reexamined. Classical methods, mostly based on the ICP algorithm, are shown to be limited and complicated by an inappropriate parameterization of the problem. First, we show how an adequate parameterization, used in an ICP-like scheme, can lead to a simpler, more accurate and faster algorithm. Then, using this parameterization, we reinterpret the problem in a probabilistic framework, and use the maximum likelihood principle to define the optimal symmetry plane. This problem can be solved efficiently using an EM algorithm. The resulting iterative scheme can be seen as an ICP-like algorithm with multiple matches between the two sides of the object. This new algorithm, implemented using a multiscale, multiresolution approach, is evaluated in terms of accuracy, robustness and speed on ground truth data, and some results on real data are presented.
Discriminative modeling by Boosting on Multilevel Aggregates
Authors: Jason J. Corso
Venue: CVPR
Year: 2008

This paper presents a new approach to discriminative modeling for classification and labeling. Our method, called boosting on multilevel aggregates (BMA), adds a new class of hierarchical, adaptive features into boosting-based discriminative models. Each pixel is linked with a set of aggregate regions in a multilevel coarsening of the image. The coarsening is adaptive, rapid and stable. The multilevel aggregates present additional information rich features on which to boost, such as shape properties, neighborhood context, hierarchical characteristics, and photometric statistics. We implement and test our approach on three two-class problems: classifying documents in office scenes, buildings and horses in natural images. In all three cases, the majority, about 75%, of features selected during boosting are our proposed BMA features rather than patch-based features. This large percentage demonstrates the discriminative power of the multilevel aggregate features over conventional patch-based features. Our quantitative performance measures show the proposed approach gives superior results to the state-of-the-art in all three applications.
Shape priors in variational image segmentation: Convexity, Lipschitz continuity and globally optimal solutions
Authors: Daniel Cremers, Frank R. Schmidt, Frank Barthel
Venue: CVPR
Year: 2008

In this work, we introduce a novel implicit representation of shape which is based on assigning to each pixel a probability that this pixel is inside the shape. This probabilistic representation of shape resolves two important drawbacks of alternative implicit shape representations such as the level set method: Firstly, the space of shapes is convex in the sense that arbitrary convex combinations of a set of shapes again correspond to a valid shape. Secondly, we prove that the introduction of shape priors into variational image segmentation leads to functionals which are convex with respect to shape deformations. For a large class of commonly considered (spatially continuous) functionals, we prove that - under mild regularity assumptions - segmentation and tracking with statistical shape priors can be performed in a globally optimal manner. In experiments on tracking a walking person through a cluttered scene we demonstrate the advantage of global versus local optimality.
Geo-located image analysis using latent representations
Authors: Marco Cristani, Alessandro Perina, Umberto Castellani, Vittorio Murino
Venue: CVPR
Year: 2008

Image categorization is undoubtedly one of the most challenging open problems faced in computer vision, far from being solved by employing pure visual cues. Recently, additional textual ldquotagsrdquo can be associated to images, enriching their semantic interpretation beyond the pure visual aspect, and helping to bridge the so-called semantic gap. One of the latest class of tags consists in geo-location data, containing information about the geographical site where an image has been captured. Such data motivate, if not require, novel strategies to categorize images, and pose new problems to focus on. In this paper, we present a statistical method for geo-located image categorization, in which categories are formed by clustering geographically proximal images with similar visual appearance. The proposed strategy permits also to deal with the geo-recognition problem, i.e., to infer the geographical area depicted by images with no available location information. The method lies in the wide literature on statistical latent representations, in particular, the probabilistic latent semantic analysis (pLSA) paradigm has been extended, introducing a latent aspect which characterizes peculiar visual features of different geographical zones. Experiments on categorization and georecognition have been carried out employing a well-known geographical image repository: results are actually very promising, opening new interesting challenges and applications in this research field.
Texture classification with a dictionary of basic image features
Authors: Michael Crosier, Lewis D. Griffin
Venue: CVPR
Year: 2008

Many successful recent approaches to texture classification model texture images as distributions over a set of discrete features, or textons, which correspond to a partitioning of the space of responses to local descriptors such as filter banks or image patches. This partitioning is learned by unsupervised clustering of descriptor responses taken from the dataset to be analysed. Here, we explore a quantization of filter responses into a dictionary of discrete features which is based on geometrical, rather than statistical, considerations, resulting in a simple texture description based on a dictionary of dasiavisual wordspsila which is independent of the images to be described. A multi-scale classification scheme built on this dictionary is evaluated. The results presented are, to the best of our knowledge, state-of-the-art for the UIUCTex and KTH-TIPS datasets, and close to the state-of-the-art for CUReT, despite using a less sophisticated classifier.
Motion from blur
Authors: Shengyang Dai, Ying Wu 0001
Venue: CVPR
Year: 2008

Motion blur retains some information about motion, based on which motion may be recovered from blurred images. This is a difficult problem, as the situations of motion blur can be quite complicated, such as they may be space-variant, nonlinear, and local. This paper addresses a very challenging problem: can we recover motion blindly from a single motion-blurred image? A major contribution of this paper is a new finding of an elegant motion blur constraint. Exhibiting a very similar mathematical form as the optical flow constraint, this linear constraint applies locally to pixels in the image. Therefore, a number of challenging problems can be addressed, including estimating global affine motion blur, estimating global rotational motion blur, estimating and segmenting multiple motion blur, and estimating nonparametric motion blur field. Extensive experiments on blur estimation and image deblurring on both synthesized and real data demonstrate the accuracy and general applicability of the proposed approach.
Real-time pose estimation of articulated objects using low-level motion
Authors: Ben Daubney, David P. Gibson, Neill W. Campbell
Venue: CVPR
Year: 2008

We present a method that is capable of tracking and estimating pose of articulated objects in real-time. This is achieved by using a bottom-up approach to detect instances of the object in each frame, these detections are then linked together using a high-level a priori motion model. Unlike other approaches that rely on appearance, our method is entirely dependent on motion; initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as pictorial structures using motion. A sparse cloud of points extracted using a standard feature tracker are used as observational data, this data contains noise that is not Gaussian in nature but systematic due to tracking errors. Using a probabilistic framework we are able to overcome both corrupt and missing data whilst still inferring new poses from a generative model. Our approach requires no manual initialisation and we show results for a number of complex scenes and different classes of articulated object, this demonstrates both the robustness and versatility of the presented technique.
Pan, zoom, scan - Time-coherent, trained automatic video cropping
Authors: Thomas Deselaers, Philippe Dreuw, Hermann Ney
Venue: CVPR
Year: 2008

We present a method to fully automatically fit videos in 16:9 format on 4:3 screens and vice versa. It can be applied to arbitrary aspect ratios and can be used to make videos suitable for mobile viewing devices with small and possibly uncommonly sized displays. The cropping sequence is optimised over time to create smooth transitions and thus leads to an excellent viewing experience. Current televisions have simple and often disturbing methods which either show the centre region of the image, distort the image, or pad it with black borders. The technique presented here can fully automatically find the ldquorightrdquo viewing area for each image in a video sequence. It works in real-time with only very little time-shift. We employ different low-level features and a log-linear model to learn how to find the right area. The method is able to automatically decide whether padding with black borders is necessary or whether all relevant image areas fit on screen by cropping the image. Evaluation is done on ten videos from five different types of content and the baseline methods are clearly outperformed.
A robust identification approach to gait recognition
Authors: Tao Ding
Venue: CVPR
Year: 2008

In this paper we address the problem of human gait recognition from a robust identification and model (in)validation prospective. The main idea is to apply dimensionality reduction technique to extract the spatio-temporal information by mapping the gait silhouette sequence to a low dimensional time sequence, which is considered as the output of a linear time invariant (LTI) system. A class of gaits is associated to a nominal discrete LTI system which has a periodic impulse response and is identified by robust identification approach. Correspondingly, gait recognition can be formulated as measuring the difference between the models representing different gait sequences. Our approach provides an efficient way to extract, to model shape-motion information of gait sequence, and to measure the difference between gait sequence models which is robust to gait cycle localization, gross appearance variation, and time scaling. These results are illustrated with practical examples on popular gait databases.