OpenRaiser/Intern-Atlas huggingface dataset:
Generalized group sparse classifiers with application in fMRI brain decoding
Authors: Bernard Ng, Rafeef Abugharbieh
Venue: CVPR
Year: 2011

The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic resonance imaging (fMRI) classification. In this paper, we present a novel formulation for constructing “Generalized Group Sparse Classifiers” (GSSC) to alleviate these problems. In particular, we propose an extension of group LASSO that permits associations between features within (predefined) groups to be modeled. Integrating this new penalty into classifier learning enables incorporation of additional prior information beyond group structure. In the context of fMRI, GGSC provides a flexible means for modeling how the brain is functionally organized into specialized modules (i.e. groups of voxels) with spatially proximal voxels often displaying similar level of brain activity (i.e. feature associations). Applying GSSC to real fMRI data improved predictive performance over standard classifiers, while providing more neurologically interpretable classifier weight patterns. Our results thus demonstrate the importance of incorporating prior knowledge into classification problems.
Spatial-DiscLDA for visual recognition
Authors: Zhenxing Niu, Gang Hua 0001, Xinbo Gao 0001, Qi Tian 0001
Venue: CVPR
Year: 2011

Topic models such as pLSA, LDA and their variants have been widely adopted for visual recognition. However, most of the adopted models, if not all, are unsupervised, which neglected the valuable supervised labels during model training. In this paper, we exploit recent advancement in supervised topic modeling, more particularly, the DiscLDA model for object recognition. We extend it to a part based visual representation to automatically identify and model different object parts. We call the proposed model as Spatial-DiscLDA (S-DiscLDA). It models the appearances and locations of the object parts simultaneously, which also takes the supervised labels into consideration. It can be directly used as a classifier to recognize the object. This is performed by an approximate inference algorithm based on Gibbs sampling and bridge sampling methods. We examine the performance of our model by comparing its performance with another supervised topic model on two scene category datasets, i.e., LabelMe and UIUC-sport dataset. We also compare our approach with other approaches which model spatial structures of visual features on the popular Caltech-4 dataset. The experimental results illustrate that it provides competitive performance.
Which parts of the face give out your identity?
Authors: Omar Ocegueda, Shishir K. Shah, Ioannis A. Kakadiaris
Venue: CVPR
Year: 2011

We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex to be “discriminative” or “non-discriminative”. As an application of the proposed framework, we present a method for the selection of compact and robust features for 3D face recognition. The resulting signature consists of 360 coefficients, based on which we are able to build a classifier yielding better recognition rates than currently reported in the literature. The main contribution of this work lies in the development of a novel framework for feature selection in scenarios in which the most discriminative information is known to be concentrated along piece-wise smooth regions of a lattice.
Unsupervised local color correction for coarsely registered images
Authors: Miguel Oliveira 0001, Angel Domingo Sappa, Vítor M. F. Santos
Venue: CVPR
Year: 2011

The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time.
A two-stage reconstruction approach for seeing through water
Authors: Omar Oreifej, Guang Shu, Teresa Pace, Mubarak Shah
Venue: CVPR
Year: 2011

Several attempts have been lately proposed to tackle the problem of recovering the original image of an underwater scene using a sequence distorted by water waves. The main drawback of the state of the art [18] is that it heavily depends on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In this paper, we revisit the problem by proposing a data-driven two-stage approach, each stage is targeted toward a certain type of noise. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative robust registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences even only after the first stage.
Learning object color models from multi-view constraints
Authors: Trevor Owens, Kate Saenko, Ayan Chakrabarti, Ying Xiong, Todd E. Zickler, Trevor Darrell
Venue: CVPR
Year: 2011

Color is known to be highly discriminative for many object recognition tasks, but is difficult to infer from uncontrolled images in which the illuminant is not known. Traditional methods for color constancy can improve surface reflectance estimates from such uncalibrated images, but their output depends significantly on the background scene. In many recognition and retrieval applications, we have access to image sets that contain multiple views of the same object in different environments; we show in this paper that correspondences between these images provide important constraints that can improve color constancy. We introduce the multi-view color constancy problem, and present a method to recover estimates of underlying surface reflectance based on joint estimation of these surface properties and the illuminants present in multiple images. The method can exploit image correspondences obtained by various alignment techniques, and we show examples based on matching local region features. Our results show that multi-view constraints can significantly improve estimates of both scene illuminants and object color (surface reflectance) when compared to a baseline single-view method.
The magic sigma
Authors: Dirk Ryan Padfield
Venue: CVPR
Year: 2011

With the explosion in the usage of mobile devices and other smart electronics, embedded devices are becoming ubiquitous. Most such embedded architectures utilize fixed-point rather than floating-point computation to meet power, heat, and speed requirements leading to the need for integer-based processing algorithms. Operations involving Gaussian kernels are common to such algorithms, but the standard methods of constructing such kernels result in approximations and lack a property that enables efficient bitwise shift operations. To overcome these limitations, we present how to precisely combine the power of integer arithmetic and bitwise shifts with intrinsically real valued Gaussian kernels. We prove mathematically that there exist a set of what we call “magic sigmas” for which the integer kernels exactly represent the Gaussian function whose values are all powers-of-two, and we discovered that the maximum sigma that leads to such properties is about 0.85. We also designed a simple and precise algorithm for designing kernels composed exclusively of integers given any arbitrary sigma and show how this can be exploited for Gaussian filter design. Considering the ubiquity of Gaussian filtering and the need for integer computation for increasing numbers of embedded devices, this is an important result for both theoretical and practical purposes.
Multifactor analysis based on factor-dependent geometry
Authors: Sung Won Park, Marios Savvides
Venue: CVPR
Year: 2011

This paper proposes a novel method that preserves the geometrical structure created by variation of multiple factors in analysis of multiple factor models, i.e., multifactor analysis. We use factor-dependent submanifolds as constituent elements of the factor-dependent geometry in a multiple factor framework. In this paper, a submanifold is defined as some subset of a manifold in the data space, and factor-dependent submanifolds are defined as the submani-folds created for each factor by varying only this factor. In this paper, we show that MPCA is formulated using factor-dependent submanifolds, as is our proposed method. We show, however, that MPCA loses the original shapes of these submanifolds because MPCA's parameterization is based on averaging the shapes of factor-dependent subman-ifolds for each factor. On the other hand, our proposed multifactor analysis preserves the shapes of individual factor-dependent submanifolds in low-dimensional spaces. Because the parameters obtained by our method do not lose their structures, our method, unlike MPCA, sufficiently covers original factor-dependent submanifolds. As a result of sufficient coverage, our method is appropriate for accurate classification of each sample.
A coarse-to-fine approach for fast deformable object detection
Authors: Marco Pedersoli, Andrea Vedaldi, Jordi Gonzàlez 0001
Venue: CVPR
Year: 2011

We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
Image analysis by counting on a grid
Authors: Alessandro Perina, Nebojsa Jojic
Venue: CVPR
Year: 2011

In recent object/scene recognition research images or large image regions are often represented as disorganized ”bags” of image features. This representation allows direct application of models of word counts in text. However, the image feature counts are likely to be constrained in different ways than word counts in text. As a camera pans upwards from a building entrance over its first few floors and then above the penthouse to the backdrop formed by the mountains, and then further up into the sky, some feature counts in the image drop while others rise-only to drop again giving way to features found more often at higher elevations (Fig. 1). The space of all possible feature count combinations is constrained by the properties of the larger scene as well as the size and the location of the window into it. Accordingly, our model is based on a grid of feature counts, considerably larger than any of the modeled images, and considerably smaller than the real estate needed to tile the images next to each other tightly. Each modeled image is assumed to have a representative window in the grid in which the sum of feature counts mimics the distribution in the image. We provide learning procedures that jointly map all images in the training set to the counting grid and estimate the appropriate local counts in it. Experimentally, we demonstrate that the resulting representation captures the space of feature count combinations more accurately than the traditional models, such as latent Dirichlet allocation, even when modeling images of different scenes from the same category.
Sampling bedrooms
Authors: Luca Del Pero, Jinyan Guan, Ernesto Brau, Joseph Schlecht, Kobus Barnard
Venue: CVPR
Year: 2011

We propose a top down approach for understanding indoor scenes such as bedrooms and living rooms. These environments typically have the Manhattan world property that many surfaces are parallel to three principle ones. Further, the 3D geometry of the room and objects within it can largely be approximated by non overlapping simple structures such as single blocks (e.g. the room boundary), thin blocks (e.g. picture frames), and objects that are well modeled by single blocks (e.g. simple beds). We separately model the 3D geometry, the imaging process (camera parameters), and edge likelihood, to provide a generative statistical model for image data. We fit this model using data driven MCMC sampling. We combine reversible jump Metropolis Hastings samples for discrete changes in the model such as the number of blocks, and stochastic dynamics to estimate continuous parameter values in a particular parameter space that includes block positions, block sizes, and camera parameters. We tested our approach on two datasets using room box pixel orientation. Despite using only bounding box geometry and, in particular, not training on appearance, our method achieves results approaching those of others. We also introduce a new evaluation method for this domain based on ground truth camera parameters, which we found to be more sensitive to the task of understanding scene geometry.
Foreground-background segmentation using iterated distribution matching
Authors: Viet Quoc Pham, Keita Takahashi 0001, Takeshi Naemura
Venue: CVPR
Year: 2011

This paper addresses the problem of image segmentation with a reference distribution. Recent studies have shown that segmentation with global consistency measures outperforms conventional techniques based on pixel-wise measures. However, such global approaches require a precise distribution to obtain the correct extraction. To overcome this strict assumption, we propose a new approach in which the given reference distribution plays a guiding role in inferring the latent distribution and its consistent region. The inference is based on an assumption that the latent distribution resembles the distribution of the consistent region but is distinct from the distribution of the complement region. We state the problem as the minimization of an energy function consisting of global similarities based on the Bhattacharyya distance and then implement a novel iterated distribution matching process for jointly optimizing distribution and segmentation. We evaluate the proposed algorithm on the GrabCut dataset, and demonstrate the advantages of using our approach with various segmentation problems, including interactive segmentation, background subtraction, and co-segmentation.
A general method for the point of regard estimation in 3D space
Authors: Fiora Pirri, Matia Pizzoli, Alessandro Rudi
Venue: CVPR
Year: 2011

A novel approach to 3D gaze estimation for wearable multi-camera devices is proposed and its effectiveness is demonstrated both theoretically and empirically. The proposed approach, firmly grounded on the geometry of the multiple views, introduces a calibration procedure that is efficient, accurate, highly innovative but also practical and easy. Thus, it can run online with little intervention from the user. The overall gaze estimation model is general, as no particular complex model of the human eye is assumed in this work. This is made possible by a novel approach, that can be sketched as follows: each eye is imaged by a camera; two conics are fitted to the imaged pupils and a calibration sequence, consisting in the subject gazing a known 3D point, while moving his/her head, provides information to 1) estimate the optical axis in 3D world; 2) compute the geometry of the multi-camera system; 3) estimate the Point of Regard in 3D world. The resultant model is being used effectively to study visual attention by means of gaze estimation experiments, involving people performing natural tasks in wide-field, unstructured scenarios.
Global optimization for optimal generalized procrustes analysis
Authors: Daniel Pizarro 0001, Adrien Bartoli
Venue: CVPR
Year: 2011

This paper deals with generalized procrustes analysis. This is the problem of registering a set of shape data by estimating a reference shape and a set of rigid transformations given point correspondences. The transformed shape data must align with the reference shape as best possible. This is a difficult problem. The classical approach computes alternatively the reference shape, usually as the average of the transformed shapes, and each transformation in turn. We propose a global approach to generalized procrustes analysis for two- and three-dimensional shapes. It uses modern convex optimization based on the theory of Sum Of Squares functions. We show how to convert the whole procrustes problem, including missing data, into a semidefinite program. Our approach is statistically grounded: it finds the maximum likelihood estimate. We provide results on synthetic and real datasets. Compared to classical alternation our algorithm obtains lower errors. The discrepancy is very high when similarities are estimated or when the shape data have significant deformations.
Nonlinear shape manifolds as shape priors in level set segmentation and tracking
Authors: Victor Adrian Prisacariu, Ian D. Reid 0001
Venue: CVPR
Year: 2011

We propose a novel nonlinear, probabilistic and variational method for adding shape information to level set-based segmentation and tracking. Unlike previous work, we represent shapes with elliptic Fourier descriptors and learn their lower dimensional latent space using Gaussian Process Latent Variable Models. Segmentation is done by a nonlinear minimisation of an image-driven energy function in the learned latent space. We combine it with a 2D pose recovery stage, yielding a single, one shot, optimisation of both shape and pose. We demonstrate the performance of our method, both qualitatively and quantitatively, with multiple images, video sequences and latent spaces, capturing both shape kinematics and object class variance.
Scalable multi-class object detection
Authors: Nima Razavi, Juergen Gall, Luc Van Gool
Venue: CVPR
Year: 2011

Scalability of object detectors with respect to the number of classes is a very important issue for applications where many object classes need to be detected. While combining single-class detectors yields a linear complexity for testing, multi-class detectors that localize all objects at once come often at the cost of a reduced detection accuracy. In this work, we present a scalable multi-class detection algorithm which scales sublinearly with the number of classes without compromising accuracy. To this end, a shared discriminative codebook of feature appearances is jointly trained for all classes and detection is also performed for all classes jointly. Based on the learned sharing distributions of features among classes, we build a taxonomy of object classes. The taxonomy is then exploited to further reduce the cost of multi-class object detection. Our method has linear training and sublinear detection complexity in the number of classes. We have evaluated our method on the challenging PASCAL VOC'06 and PASCAL VOC'07 datasets and show that scaling the system does not lead to a loss in accuracy.
Fast cost-volume filtering for visual correspondence and beyond
Authors: Christoph Rhemann, Asmaa Hosni, Michael Bleyer, Carsten Rother, Margrit Gelautz
Venue: CVPR
Year: 2011

Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge preserving filter. In this paper we propose a generic and simple framework comprising three steps: (i) constructing a cost volume (ii) fast cost volume filtering and (iii) winner-take-all label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve (i) disparity maps in real-time, whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and (ii) optical flow fields with very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.
Efficient multi-camera detection, tracking, and identification using a shared set of haar-features
Authors: Reyes Rios-Cabrera, Tinne Tuytelaars, Luc Van Gool
Venue: CVPR
Year: 2011

This paper presents an integrated solution for the problem of detecting, tracking and identifying vehicles in a tunnel surveillance application, taking into account practical constraints including realtime operation, poor imaging conditions, and a decentralized architecture. Vehicles are followed through the tunnel by a network of non-overlapping cameras. They are detected and tracked in each camera and then identified, i.e. matched to any of the vehicles detected in the previous camera(s). To limit the computational load, we propose to reuse the same set of Haar-features for each of these steps. For the detection, we use an Adaboost cascade. Here we introduce a composite confidence score, integrating information from all stage of the cascades. A subset of the features used for detection is then selected, optimizing for the identification problem. This results in a compact binary `vehicle fingerprint', requiring very limited bandwidth. Finally, we show that the same set of features can also be used for tracking. This haar features based `tracking-by-identification' yields surprisingly good results on standard datasets, without the need to update the model online.
The importance of intermediate representations for the modeling of 2D shape detection: Endstopping and curvature tuned computations
Authors: Antonio Jose Rodríguez-Sánchez, John K. Tsotsos
Venue: CVPR
Year: 2011

Computational models of visual processes with biological inspiration - and even biological realism - are currently of great interest in the computer vision community. This paper provides a biologically plausible model of 2D shape which incorporates intermediate layers of visual representation that have not previously been fully explored. We propose that endstopping and curvature cells are of great importance for shape selectivity and show how their combination can lead to shape selective neurons. This shape representation model provides a highly accurate fit with neural data from and provides comparable results with real-world images to current computer vision systems. The conclusion is that such intermediate representations may no longer require a learning approach as a bridge between early representations based on Gabor or Difference of Gaussian filters (that are not learned since they are well-understood) and later representations closer to object representations that still can benefit from a learning methodology.